Castanea mollissima photosynthetic efficiency high-precision physiological marking and classifying method

Abstract

The invention relates to a Chinese chestnut photosynthetic efficiency high-precision physiological marking and classification method, which integrates photosynthetic parameters under different environment light intensity conditions, and considers the weight of each photosynthetic parameter and the correlation of each photosynthetic parameter at the same time. A second-order clustering method is adopted for the first time, the responsiveness of Chinese chestnut germplasm to light intensity changes is evaluated and classified, and finally high-light-efficiency light-delay Chinese chestnut germplasm is used as target germplasm for precise screening. Experiments prove that the high-photosynthetic-efficiency light-retardation Chinese chestnut germplasm not only has high photosynthetic efficiency, but also has the ability to resist light stress, and still can maintain good photosynthetic activity in a low-light-stress environment. The method comprehensively, scientifically and effectively evaluates the photosynthetic characteristics and the photosynthetic sensitivity of the chestnut germplasm, further realizes high-precision physiological marking and classification of the chestnut photosynthetic efficiency, and has a remarkable effect on chestnut breeding.

Description

Technical Field

[0001] The present invention belongs to the field of chestnut breeding, and relates to high-precision evaluation of photosynthetic efficiency of chestnut germplasm resources, screening of high-light-efficiency germplasm, high-light-efficiency inheritance and its application technology, especially a high-precision physiological marker and classification method for chestnut photosynthetic efficiency. Background Art

[0002] Photosynthesis is the basis of chestnut yield. Using chestnut varieties with high photosynthesis efficiency to improve light energy conversion efficiency and thus increase yield is an effective method to break the yield ceiling in chestnut breeding. The internal factors that affect chestnut photosynthesis are variety characteristics, including variety photosynthetic characteristics determined by genotype, stomatal opening and closing speed, leaf characteristics, chlorophyll content, etc.; external factors are environmental factors, including light intensity, water, temperature, CO2 concentration, cultivation conditions, etc. That is, chestnut photosynthetic efficiency is the result of the interaction between genotype, environment and management. This means that in the measurement process, even a small change, whether this change comes from the chestnut inside, the surrounding environment or the measuring instrument, may cause a large error in the measured photosynthetic parameters. Therefore, in the actual measurement process, even the most advanced photosynthetic instrument in the world, the LI-6800 photosynthetic instrument, will have a large error. Under such error conditions, it is unscientific to reflect the photosynthesis of the variety only by the average value of a few sets of data. At the same time, the existing photosynthetic efficiency evaluation method only reflects the photosynthetic efficiency under normal light intensity or a certain light intensity, while the ambient light intensity is constantly changing. Therefore, the comprehensive score of photosynthetic parameters of chestnut germplasm resources under different light intensities can better reflect its photosynthetic capacity.

[0003] In addition, photosensitivity is the ability of chestnuts to adapt to changes in external light intensity. The chestnut varieties we pursue not only need to maintain high photosynthetic efficiency under appropriate light intensity, but more importantly, they can maintain high photosynthetic efficiency under both low-light and high-light stress, that is, chestnut varieties with high photosynthetic efficiency and insensitive to changes in ambient light intensity (we call them high-photosynthetic efficiency photo-retardant chestnut varieties). While maintaining high photosynthetic efficiency, such varieties also have the ability to resist light stress, that is, they can still maintain good photosynthetic activity under low-light and high-light stress, thereby ensuring high yields.

[0004] However, there is no evaluation method for chestnut germplasm resources' sensitivity to light intensity changes in the prior art, let alone an accurate evaluation method. Therefore, it is necessary to invent an accurate and scientific evaluation method for chestnut photosynthetic efficiency and photosensitivity, which can eliminate the influence of the environment and instruments on the measured values ​​of chestnut photosynthetic parameters, correct the errors caused by environmental factors, management factors, etc., and through the application of this method, accurately evaluate the photosynthetic efficiency and ability to respond to light intensity changes of existing chestnut germplasm resources, screen high-efficiency light-retardant chestnut germplasm resources, and provide a material basis for chestnut high-efficiency breeding and production. Summary of the invention

[0005] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a high-precision physiological marker and classification method for chestnut photosynthetic efficiency. For the first time, the second-order clustering method is used to evaluate and classify the chestnut's response to changes in light intensity. The method is applied to the evaluation of chestnut photosynthetic efficiency, which can effectively evaluate the light efficiency and light sensitivity of chestnut germplasm, thereby predicting the photosynthetic capacity of chestnut germplasm resources, which has outstanding substantial effects and significant progress in increasing chestnut yield.

[0006] The present invention solves the technical problem by adopting the following technical solutions:

[0007] A method for accurately marking and classifying chestnut photosynthetic efficiency with high accuracy, the method steps are as follows:

[0008] Step 1: Select chestnut germplasm resources with consistent cultivation and management conditions, and collect photosynthetic parameters, including: stomatal conductance Gsw, transpiration rate Tr, actual photochemical quantum efficiency PhiPS2 of PSII in the presence of action light, electron transfer rate Etr, and total stomatal conductance Gtw;

[0009] Step 2: Use the photosynthetic parameters collected in step 1 to construct regression equations for the germplasm stomatal conductance Gsw, transpiration rate Tr, actual photochemical quantum efficiency PhiPS2 of PSII in the presence of action light, electron transfer rate Etr, total stomatal conductance Gtw and ambient light intensity PAR. The equations between Gsw, Tr, PhiPS2, and Gtw and PAR are logarithmic equations, denoted as y=k+b1*ln(x)(0<x≤Max 实测值 ), where y is Gsw, Tr, PhiPS2 and Gtw, k is a constant with a value of -0.123, b1 is a coefficient with a value of 0.065, x is the PAR value, Max 实测值 is the maximum value of the field measured value, 0<x≤Max 实测值 is the range of field measured values; Etr is the linear regression equation, recorded as y = k + b1*x (0 ≤ x ≤ Max 实测值 ), where y is Etr, k is a constant, b1 is a coefficient, x is PAR, 0≤x≤Max 实测值 is the range of the field measured value. According to the above equation, 100, 500, 1000 and 1500 are substituted as x values, and the y values ​​calculated are 100, 500, 1000 and 1500 μmol·m -2 ·s -1 Gsw value, Tr value, PhiPS2 value, Etr value, and Gtw value under light intensity;

[0010] Step 3: Apply principal component analysis to calculate the concentrations of germplasm at 100, 500, 1000 and 1500 μmol·m -2 ·s -1 The comprehensive score of photosynthetic parameters under light intensity is expressed as follows based on the matrix coefficient and the standardized data function:

[0011] H1=0.828*GnGsw+0.047*GnTr+0.030*GnPhiPS2+0.777*GnEtr+0.778*GnGtw,

[0012] H2=-0.068*GnGsw+0.049*GnTr+0.093*GnPhiPS2+0.277*GnEtr-0.575*GnGtw,

[0013] H3=0.353*GnGsw+0.946*GnTr+0.055*GnPhiPS2-0.303*GnEtr+0.070*GnGtw.

[0014] Among them, GnGsw, GnTr, GnPhiPS2, GnEtr and GnGtw are the nth germplasm, i.e., G1, G2, G3, ... Gn, at 100 μmol·m -2 ·s -1 The Gsw value, Tr value, PhiPS2 value, Etr value and Gtw value under different light intensities were calculated by using the comprehensive evaluation function F = C1H1 + C2H2 + C3H3 according to the variance contribution rate of each principal component. -2 ·s -1 Comprehensive score of photosynthetic parameters under different light intensities, where C1, C2 and C3 are comprehensive evaluation function coefficients.

[0015] The same method was used to calculate 500, 1000, and 1500 μmol·m -2 ·s -1 Comprehensive score of photosynthetic parameters under different light intensities;

[0016] Step 4: Determine the germplasm single plant yield mean_yield, chlorophyll a Ca, chlorophyll b Cb, total chlorophyll content Ct, carotenoid Cx.c, leaf length LL, leaf width LW, leaf thickness LT, and leaf area LA;

[0017] Step 5: Use the systematic clustering method to analyze the -2 ·s -1 Comprehensive score of photosynthetic parameters under light intensity OS_100lx, 500μmol·m -2 ·s -1Comprehensive score of photosynthetic parameters under light intensity OS_500lx, 1000μmol·m -2 ·s -1 Comprehensive score of photosynthetic parameters under light intensity OS_1000lx and 1500μmol·m -2 ·s -1 The comprehensive score of photosynthetic parameters under light intensity OS_1500lx, mean_yield per plant, chlorophyll a Ca, chlorophyll b Cb, total chlorophyll content Ct, carotenoid Cx.c, leaf length LL, leaf width LW, leaf thickness LT, leaf area LA were clustered. The analysis showed that OS_100lx, OS_500lx, OS_1000lx, OS_1500lx, and mean_yield were clustered together, and these five variables were used to replace the 13 variables for the subsequent light efficiency classification.

[0018] Step 6: Use the five variables in step 5 above to conduct a second-order cluster analysis on chestnut germplasm and divide it into three types: high light efficiency, medium light efficiency, and low light efficiency;

[0019] Step 7: Using the second-order clustering method, we further classified germplasms with different light efficiency types in the range of 100-1500 μmol·m -2 ·s -1 The photosynthetic parameters change under different light intensities, and the photosynthetic sensitivity of the three types of chestnut germplasm classified in step 6, namely, high photosynthetic efficiency, medium photosynthetic efficiency, and low photosynthetic efficiency, were graded. The high photosynthetic efficiency chestnut germplasm was then divided into six photosynthetic types, namely, high photosynthetic efficiency photosynthetic inert, high photosynthetic efficiency photosynthetic sensitive, medium photosynthetic efficiency photosynthetic inert, medium photosynthetic efficiency photosynthetic sensitive, low photosynthetic efficiency photosynthetic inert, and low photosynthetic efficiency photosynthetic sensitive, to evaluate the photosynthetic efficiency of chestnut.

[0020] In steps 1 to 7 above, data was entered using Microsoft Excel 2020 and then analyzed using IBM SPSS Statistics 27.

[0021] The advantages and positive effects of the present invention are:

[0022] 1. The method of the present invention constructs a regression equation of the changes of various photosynthetic parameters (including Gsw, Tr, PhiPS2, Etr and Gtw) of the chestnut germplasm to be screened with light intensity, scientifically shows the relationship between the photosynthetic parameters of the chestnut germplasm and light intensity, and reflects the response characteristics of the germplasm to internal and external factors. At the same time, the regression equation between the photosynthetic parameters of the chestnut germplasm and light intensity eliminates the influence of the environment and instruments on the measured values ​​of the photosynthetic parameters, and can accurately calculate the photosynthetic parameters of the germplasm to be screened under any light intensity.

[0023] 2. The method of the present invention uses the principal component analysis method to calculate the concentration of the germplasm to be screened at 100, 500, 1000 and 1500 μmol·m -2 ·s -1 Comprehensive score of photosynthetic parameters under different light intensities. Compared with the existing photosynthetic efficiency evaluation method, which only reflects the photosynthetic efficiency under normal light intensity or a certain light intensity, this method also evaluates the constantly changing ambient light intensity. The comprehensive score of photosynthetic parameters of chestnut germplasm resources under different light intensities can more accurately reflect its photosynthetic capacity.

[0024] 3. The method of the present invention uses a system clustering method to reduce many indicators such as photosynthetic parameters: Gsw, Tr, PhiPS2, Etr, Gtw, leaf indicators: leaf length, width, thickness, area, chlorophyll content: chlorophyll a, chlorophyll b, total chlorophyll content, carotenoids and yield to 5 indicators, making the screening and evaluation results clearer and more concise, and also improving the efficiency of subsequent data analysis.

[0025] 4. The method of the present invention adopts the second-order clustering method for the first time to evaluate and classify the response ability of chestnut germplasm to changes in light intensity. According to the variation range of photosynthetic parameters of different light efficiency types of germplasm under a light intensity of 100-1500μmol·m-2·s-1, the photosensitivity of three types of chestnut germplasm, high photosynthetic efficiency, medium photosynthetic efficiency and low photosynthetic efficiency, is graded, and the chestnut germplasm is divided into six photosynthetic types: high photosynthetic efficiency and light inert, high photosynthetic efficiency and light sensitive, medium photosynthetic efficiency and light inert, medium photosynthetic efficiency and light sensitive, low photosynthetic efficiency and light inert, and low photosynthetic efficiency and light sensitive. Finally, the high photosynthetic efficiency and light inert chestnut germplasm was used as the target germplasm for precise screening, and experiments have shown that these high photosynthetic efficiency and light inert chestnut germplasm not only have high photosynthetic efficiency, but also have the ability to resist light stress, and can still maintain good photosynthetic activity under low light stress environment.

[0026] 5. The method of the present invention carefully considers the changes in photosynthetic parameters under different environmental light intensity conditions, integrates multiple related parameters, and considers the weights of each photosynthetic parameter and the correlation of each photosynthetic parameter. The evaluation results are comprehensive and accurate, and have high scientific reliability and application value. This method provides an effective classification and evaluation basis for the photosynthetic characteristics and photosensitivity of chestnut germplasm, successfully realizes the photosynthetic efficiency evaluation of a large number of chestnut germplasm, and screens out high-efficiency light-retarded chestnut germplasm. It can be foreseen that after evaluating and classifying the existing representative chestnut germplasm using the method of the present invention, when evaluating new germplasm, as long as the relevant measurement results are obtained, the photosynthetic efficiency and photosensitivity of chestnut can be evaluated based on the evaluation and classification data, thereby predicting the photosynthetic capacity of the resource and screening chestnut germplasm with high photosynthetic efficiency, which is of great significance for guiding chestnut breeding. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1This is a distribution diagram of the Gsw-PAR regression equation of the chestnut variety "Yanbao" in the embodiment of the present invention;

[0028] Figure 2 The results are as follows: 82 chestnut germplasms in the present invention were -2 ·s -1 PCA graph under light intensity;

[0029] Figure 3 This is a result diagram of cluster analysis of 13 variables using a system clustering method in an embodiment of the present invention;

[0030] Figure 4 This is a diagram showing the light efficiency classification results of 82 chestnut germplasm resources in the embodiment of the present invention;

[0031] Figure 5 This is a diagram showing the light sensitivity classification results of 39 high-light-efficiency chestnuts in the examples of the present invention. DETAILED DESCRIPTION

[0032] The present invention is further described in detail below through specific examples. The following examples are only illustrative and not restrictive, and the protection scope of the present invention cannot be limited thereto.

[0033] A method for accurately marking and classifying chestnut photosynthetic efficiency with high accuracy, the specific experimental method steps are as follows:

[0034] Step 1: 82 chestnut germplasm resources with consistent cultivation and management conditions were used as test materials. The photosynthetic parameters of each germplasm were measured multiple times using the LI-600 fluorescence stomatal meter (USA, LI-600, Beijing Ligaotai Technology Co., Ltd.), and more than 250 groups of photosynthetic parameters were collected.

[0035] Step 2: Use the photosynthetic parameters collected above to construct regression equations for each germplasm's stomatal conductance (Gsw), transpiration rate (Tr), actual photochemical quantum efficiency of PSII in the presence of action light (PhiPS2), electron transfer rate (Etr), total stomatal conductance (Gtw) and ambient light intensity (PAR).

[0036] Take the construction of the Gsw-PAR regression equation of the chestnut variety "Yanbao" as an example. First, use Microsoft Excel 2020 to prepare the data: the first column is PAR data, and the second column is Gsw data; open IBM SPSS Statistics 27, copy the PAR and Gsw data to the data view box, and modify the name of the first column in the variable view to PAR, with 0 decimal places, and the second column to Gsw, with 2 decimal places, and both columns are digital types; then execute: Analysis--Regression--Curve Estimation (the parameter settings are: select PAR as the variable and Gsw as the dependent variable, check "Include constants in the equation" and "Model drawing", select "Logarithm" for the model, and check "Show ANOVA table")--Confirm; the results are shown in Tables 1 and Figure 1 .

[0037] Table 1. Summary of the Gsw and PAR regression equation model and parameter estimates. Dependent variable: Gsw

[0038]

[0039] Independent variable: PAR

[0040] From the results in Table 1 above, the logarithmic equation is y=k+b1*ln(x)(0≤x≤1617), where x is the PAR value, y is Gsw, k is a constant of -0.123, b1 is a coefficient of 0.065, and 0<x≤1617 is the range of field measured values. According to this equation, 100, 500, 1000 and 1500 are substituted as x values, and the calculated y values ​​are (0.176, 0.281, 0.326, 0.352 μmol·m -2 ·s -1 ) is 100, 500, 1000 and 1500 μmol·m -2 ·s -1 Gsw value under light intensity.

[0041] Tr, PhiPS2 and Gtw are all logarithmic regression equations, and the equation construction method is the same as Gsw. Etr is a linear regression equation, and the model selection in the parameter setting is "Linear", and the linear equation is y=k+b1*x(0≤x≤1617). The equation is used to calculate the concentration of 100, 500, 1000 and 1500 μmol·m -2 ·s -1 Tr, PhiPS2, Etr, and Gtw values ​​under different light intensities.

[0042] The same method was used to construct the regression equations of Gsw, Tr, PhiPS2, Etr and Gtw with PAR for the remaining 81 accessions, and the PAR values ​​of each accession at 100, 500, 1000 and 1500 μmol·m -2·s -1 Gsw, Tr, PhiPS2, Etr, and Gtw values ​​under different light intensities.

[0043] Step 3: Use the principal component analysis method to calculate the comprehensive scores of photosynthetic parameters of various substances under different light intensities, including calculating the photosynthetic parameters of various substances at 100, 500, 1000 and 1500 μmol·m -2 ·s -1 The comprehensive score of photosynthetic parameters under different light intensities was calculated by using the 82 germplasms at 100 μmol·m -2 ·s -1 Taking the comprehensive scoring under light intensity as an example, the specific steps are as follows:

[0044] 82 accessions were found to have a concentration of 100 μmol·m -2 ·s -1 The Gsw, Tr, PhiPS2, Etr and Gtw values ​​under different light intensities are placed in the same excel sheet, as shown in Table 2.

[0045] Table 282 chestnut germplasms 100 μmol·m -2 ·s -1 Standard values ​​of Gsw, Tr, PhiPS2, Etr and Gtw under light intensity

[0046]

[0047]

[0048] Open IBMSPSSStatistics27, copy the data in Table 2 to the data view box, modify the name of the first column in the variable view to germplasm code, string type, the names of the 2nd to 6th columns are Gsw, Tr, PhiPS2, Etr and Gtw, the number of decimal places is 4, and all five columns are digital type; then execute: Analysis--Dimensionality Reduction--Factor Analysis, open the Factor Analysis dialog box, select Gsw, Tr, PhiPS2, Etr and Gtw into the variable column; set the parameters as follows: Description: Select "Coefficient" and "KMO and Bartlett's Sphericity Test"; Extraction: Select "Scree Plot"; Rotation: Select "Loading Plot"; Options: Select "Sort by Size"--OK; obtain the results as shown in Tables 3, 4, 5 and Figure 2 shown.

[0049] Table 3 82 chestnut germplasms at 100 μmol·m -2 ·s -1 Total variance explained by principal component analysis of Gsw, Tr, PhiPS2, Etr and Gtw under light intensity

[0050]

[0051] Extraction method: principal component analysis.

[0052] Table 4 Comprehensive evaluation function coefficients of PCA1, PCA2 and PCA3 as three principal components

[0053]

[0054] Table 5 Rotated component matrix a

[0055]

[0056] Extraction method: principal component analysis.

[0057] Rotation method: Kaiser normalization with varimax.

[0058] a. The rotation has converged after 5 iterations.

[0059] Figure 2 Where A is 100 μmol·m -2 ·s -1 PCA plot of photosynthetic parameters under different light intensities, B is 500 μmol·m -2 ·s -1 PCA plot of principal components of photosynthetic parameters under different light intensities, C is 1000 μmol·m -2 ·s -1 PCA plot of photosynthetic parameters under different light intensities, D is 1500 μmol·m -2 ·s -1 Principal component analysis (PCA) of photosynthetic parameters under different light intensities.

[0060] According to the matrix coefficients and the standardized data function expression:

[0061] H1=0.828*GnGsw+0.047*GnTr+0.030*GnPhiPS2+0.777*GnEtr+0.778*GnGtw,

[0062] H2=-0.068*GnGsw+0.049*GnTr+0.093*GnPhiPS2+0.277*GnEtr-0.575*GnGtw,

[0063] H3=0.353*GnGsw+0.946*GnTr+0.055*GnPhiPS2-0.303*GnEtr+0.070*GnGtw.

[0064] Among them, GnGsw, GnTr, GnPhiPS2, GnEtr and GnGtw are the nth germplasm (G1, G2, G3, ..., Gn) at 100 μmol·m-2 ·s -1 Gsw, Tr, PhiPS2, Etr, and Gtw values ​​under different light intensities.

[0065] Then, according to the variance contribution rate corresponding to each principal component, the comprehensive evaluation function F = C1H1 + C2H2 + C3H3 (the values ​​of C1, C2 and C3 are shown in Table 4) was used to calculate the chestnut germplasm content at 100 μmol·m -2 ·s -1 Comprehensive score of photosynthetic capacity under different light intensities.

[0066] 500, 1000 and 1500 μmol·m -2 ·s -1 The calculation method for the comprehensive score of photosynthetic capacity under different light intensities is the same as above (see Table 6 for the comprehensive score).

[0067] The existing photosynthetic efficiency evaluation method only reflects the photosynthetic efficiency under normal light intensity or a certain light intensity, while the ambient light intensity is constantly changing. Therefore, the comprehensive score of photosynthetic parameters of chestnut germplasm resources under different light intensities can better reflect its photosynthetic capacity.

[0068] Step 4: Use conventional measurement methods to measure leaf length, leaf width, leaf thickness, leaf area, chlorophyll a, chlorophyll b, total chlorophyll content, carotenoids and single plant yield.

[0069] Step 5: Using the systematic clustering method, we conducted a random analysis of the concentrations of 100, 500, 1000, and 1500 μmol·m -2 ·s -1 Cluster analysis was performed on 13 indicators, including the comprehensive score of photosynthetic parameters under light intensity, leaf length, leaf width, leaf thickness, leaf area, chlorophyll a, chlorophyll b, total chlorophyll content, carotenoids and single plant yield. The specific indicator data are shown in Table 6 below.

[0070] Table 6 Values ​​of 13 photosynthetic related indexes of 82 chestnut germplasms

[0071]

[0072] Note: OS_100lx, OS_500lx, OS_1000lx, OS_1500lx, mean_yield, Ca, Cb, Ct, Cx.c, LL, LW, LA, LT represent 100 μmol·m -2 ·s -1 Comprehensive score of photosynthetic parameters under 500 μmol·m -2 ·s -1 Comprehensive score of photosynthetic parameters, 1000 μmol·m -2 ·s -1Comprehensive score of photosynthetic parameters, 1500 μmol·m -2 ·s -1 Comprehensive score of photosynthetic parameters, yield per plant, chlorophyll a, chlorophyll b, total chlorophyll content, carotenoids, leaf length, leaf width, leaf area and leaf thickness.

[0073] The specific steps are as follows:

[0074] Open IBM SPSS Statistics 27, copy the data in Table 6 to the data view box, and in the variable view, change the name of the first column to germplasm code, string type, and the names of the 2nd to 14th columns to 100 μmol·m -2 ·s -1 Comprehensive score of photosynthetic parameters under 500 μmol·m -2 ·s -1 Comprehensive score of photosynthetic parameters, 1000 μmol·m -2 ·s -1 Comprehensive score of photosynthetic parameters, 1500 μmol·m -2 ·s -1 Comprehensive score of photosynthetic parameters, yield per plant, chlorophyll a, chlorophyll b, total chlorophyll content, carotenoids, leaf length, leaf width, leaf area and leaf thickness, all 13 columns are digital type; then execute: Analysis--Classification--System Clustering, open the System Cluster Analysis dialog box, select OS_100lx, OS_500lx, OS_1000lx, OS_1500lx, mean_yield, Ca, Cb, Ct, Cx.c, LL, LW, LA, LT into the "Variable" column, select the germplasm code into the "Case Labeling Basis", select "Variable" for clustering, and the rest are default; parameter settings are: Statistics: default; Figure: select "Pedigree Chart", the rest are default; Method: default-OK; get the results, see Figure 3 , Figure 3 OS_100lx, OS_500lx, OS_1000lx, OS_1500lx, mean_yield, Ca, Cb, Ct, Cx.c, LL, LW, LA, and LT represent 100 μmol·m -2 ·s -1 Comprehensive score of photosynthetic parameters under 500 μmol·m -2 ·s -1 Comprehensive score of photosynthetic parameters, 1000 μmol·m -2 ·s -1 Comprehensive score of photosynthetic parameters, 1500 μmol·m -2 ·s -1Comprehensive score of photosynthetic parameters, yield per plant, chlorophyll a, chlorophyll b, total chlorophyll content, carotenoids, leaf length, leaf width, leaf area and leaf thickness.

[0075] It can be seen that OS_100lx, OS_500lx, OS_1000lx, OS_1500lx, and mean_yield are clustered together, so these five variables are used instead of the 13 variables for the subsequent lighting effect classification.

[0076] Step: 6: Using the five indicators in Table 7 below, a second-order cluster analysis was performed on the 82 chestnut germplasms, and the 82 chestnut germplasms were successfully divided into three types: high light efficiency, medium light efficiency, and low light efficiency, and the clustering quality was good.

[0077] Table 7 Values ​​of five photosynthetic indexes and light efficiency classification of 82 chestnut germplasms

[0078]

[0079] Note: OS_100lx, OS_100lx, OS_500lx, OS_1000lx, OS_100lx and mean_yield represent 100 μmol·m -2 ·s -1 Comprehensive score of photosynthetic parameters under 500 μmol·m -2 ·s -1 Comprehensive score of photosynthetic parameters, 1000 μmol·m -2 ·s -1 Comprehensive score of photosynthetic parameters, 1500 μmol·m -2 ·s -1 Comprehensive score of photosynthetic parameters and yield per plant.

[0080] The specific steps are as follows:

[0081] Open IBM SPSS Statistics 27, copy the data in Table 7 to the data view box, and in the variable view, change the name of the first column to germplasm code, string type, and the names of the 2nd to 6th columns to 100 μmol·m -2 ·s -1 Comprehensive score of photosynthetic parameters under 500 μmol·m -2 ·s -1 Comprehensive score of photosynthetic parameters, 1000 μmol·m -2 ·s -1 Comprehensive score of photosynthetic parameters, 1500 μmol·m -2 ·s -1Comprehensive score of photosynthetic parameters, yield per plant, all 5 columns are digital type; then execute: Analysis--Classification--Second-order Clustering, open the Second-order Cluster Analysis dialog box, select the germplasm code into the "Classification Variable" column, select OS_100lx, OS_100lx, OS_500lx, OS_1000lx, OS_100lx, mean_yield into the "Continuous Variable" column, select "Specify Fixed Value" for the number of clusters, value: 3, and the rest are default; parameter settings are: Options: Default; Output: Select "Pivot Table", "Create Cluster Member Variables", the rest are default--OK; get the results, see Figure 4 , Figure 4 The color intensity in the legend in the upper right corner represents the importance of each predictor variable, and the first, second, and third rows of the cluster diagram represent the cluster number, label, type, proportion, and number of cases in each category. OS_100lx, OS_500lx, OS_1000lx, OS_1500lx, and mean_yield represent 100 μmol·m -2 ·s -1 Comprehensive score of photosynthetic parameters under 500 μmol·m -2 ·s -1 Comprehensive score of photosynthetic parameters, 1000 μmol·m -2 ·s -1 Comprehensive score of photosynthetic parameters, 1500 μmol·m -2 ·s -1 Comprehensive score of photosynthetic parameters and yield per plant.

[0082] Results The 82 chestnut germplasms were divided into three categories: high light efficiency, medium light efficiency and low light efficiency.

[0083] Step 7: Using the second-order clustering method, we further classified germplasms with different light efficiency types in the range of 100-1500 μmol·m -2 ·s -1 The photosynthetic parameter variation under light intensity was used to classify the photosynthetic sensitivity of three types of chestnut germplasms: high light efficiency, medium light efficiency, and low light efficiency. The greater the variation of photosynthetic parameters of the germplasm, the more sensitive it is to light. The clustering quality obtained by this analysis was good. Taking the light efficiency and light sensitivity classification of 39 germplasms as an example, it is shown in Table 8 below.

[0084] Table 839 high photosynthetic efficiency chestnuts in the range of 100 to 1500 μmol·m -2 ·s -1 Variation range of photosynthetic parameters under light intensity and classification of photosensitivity

[0085]

[0086]

[0087] The specific steps are as follows: Open IBM SPSS Statistics 27, copy the data in Table 8 to the data view box, modify the name of the first column in the variable view to germplasm code, string type, and the names of the 2nd to 6th columns to 100-1500 μmol·m -2 ·s -1 The change ranges of Gsw, Tr, PhiPS2, Etr and Gtw are all digital types. Then execute: Analysis--Classification--Second-order Clustering, open the Second-order Cluster Analysis dialog box, select the germplasm code into the "Classification Variable" column, select the change ranges of Gsw, Tr, PhiPS2, Etr and Gtw into the "Continuous Variable" column, select "Specify Fixed Value" for the number of clusters, value: 2, and the rest are default; parameter settings are: Options: Default; Output: Select "Pivot Table", "Create Cluster Member Variables", and the rest are default--OK; see the results for details. Figure 5 , Figure 5 The color intensity of the legend in the upper right corner represents the importance of each predictor variable, and the first, second, and third rows of each cluster plot represent the cluster number, label, type, proportion, and number of cases in each category. VM_Gsw, VM_Tr, VM_PhiPS2, VM_Etr, and VM_Gtw represent Gsw, Tr, PhiPS2, Etr, and Gtw from 100 to 1500 μmol·m -2 ·s -1 The magnitude of the change in light intensity.

[0088] Results The 39 high light-efficiency chestnut germplasms were divided into two categories: high light-efficiency light-retarded type and high light-efficiency light-sensitive type.

[0089] The classification method of photosynthetic sensitivity of medium and low light efficiency chestnut germplasms was the same as that of high light efficiency germplasms. Finally, the 82 chestnut germplasms were divided into six photosynthetic types: high light efficiency, light inert, high light efficiency, light sensitive, medium light efficiency, light inert, medium light efficiency, low light efficiency, and low light efficiency.

[0090] Although the embodiments of the present invention are disclosed for illustrative purposes, those skilled in the art will appreciate that various substitutions, changes and modifications are possible without departing from the spirit and scope of the present invention and the appended claims. Therefore, the scope of the present invention is not limited to the contents disclosed in the embodiments.

Claims

1. A high-precision physiological marker and classification method for chestnut photosynthetic efficiency, characterized by: The steps are as follows: Step 1: Select chestnut germplasm resources and collect photosynthetic parameters: stomatal conductance Gsw, transpiration rate Tr, actual photochemical quantum efficiency PhiPS2 of PSII in the presence of action light, electron transfer rate Etr, and total stomatal conductance Gtw; Step 2: Use the photosynthetic parameters collected in step 1 to construct regression equations for the germplasm stomatal conductance Gsw, transpiration rate Tr, actual photochemical quantum efficiency PhiPS2 of PSII in the presence of action light, electron transfer rate Etr, total stomatal conductance Gtw and ambient light intensity PAR, where the equations between Gsw, Tr, PhiPS2, and Gtw and PAR are logarithmic equations, denoted as y=k+b1*ln(x), 0<x≤Max 实测值 , where y values ​​are Gsw, Tr, PhiPS2 and Gtw, k is a constant with a value of -0.123, b1 is a coefficient with a value of 0.065, x values ​​are PAR values, Max 实测值 is the maximum value of the field measured value, 0<x≤Max 实测值 is the range of field measured values; Etr is the linear regression equation, recorded as y=k+b1*x, 0≤x≤Max 实测值 , where y is Etr, k is a constant, b1 is a coefficient, x is PAR, 0≤x≤Max 实测值 is the range of the measured values ​​in the field. According to the above equation, 100, 500, 1000 and 1500 were substituted as the x values, and the y values ​​calculated were the Gsw values, Tr values, PhiPS2 values, Etr values ​​and Gtw values ​​at light intensities of 100, 500, 1000 and 1500 μmol·m-2·s-1, respectively; Step 3: The principal component analysis method was used to calculate the comprehensive scores of photosynthetic parameters of germplasm at light intensities of 100, 500, 1000 and 1500 μmol·m-2·s-1. The function expression based on the matrix coefficients and the standardized data was: H1=0.828*GnGsw+0.047*GnTr+0.030*GnPhiPS2+0.777*GnEtr+0.778*GnGtw, H2=-0.068*GnGsw+0.049*GnTr+0.093*GnPhiPS2+0.277*GnEtr-0.575*GnGtw, H3=0.353*GnGsw+0.946*GnTr+0.055*GnPhiPS2-0.303*GnEtr+0.070*GnGtw. Among them, GnGsw, GnTr, GnPhiPS2, GnEtr and GnGtw are the Gsw value, Tr value, PhiPS2 value, Etr value and Gtw value of the nth germplasm, i.e. G1, G2, G3, ... Gn, under a light intensity of 100 μmol·m-2·s-1. Then, according to the variance contribution rate corresponding to each principal component as the weight, the comprehensive score of photosynthetic parameters of chestnut germplasm under a light intensity of 100 μmol·m-2·s-1 was calculated by the comprehensive evaluation function F = C1H1+C2H2+C3H3, among which C1, C2 and C3 are the coefficients of the comprehensive evaluation function. The same method was used to calculate the comprehensive scores of photosynthetic parameters at light intensities of 500, 1000, and 1500 μmol·m-2·s-1; Step 4: Determine the germplasm single plant yield mean_yield, chlorophyll a Ca, chlorophyll b Cb, total chlorophyll content Ct, carotenoid Cx.c, leaf length LL, leaf width LW, leaf thickness LT, and leaf area LA; Step 5: Using the system clustering method, the comprehensive scores of photosynthetic parameters under the light intensity of 100μmol·m-2·s-1 OS_100lx, 500μmol·m-2·s-1 OS_500lx, 1000μmol·m-2·s-1 OS_1000lx and 1500μmol·m-2·s-1 OS_1500lx, single plant yield mean_yield, chlorophyll a Ca, chlorophyll b Cb, total chlorophyll content Ct, carotenoids Cx.c, leaf length LL, leaf width LW, leaf thickness LT, leaf area LA were analyzed. The 13 indicators were clustered and analyzed, and it was found that OS_100lx, OS_500lx, OS_1000lx, OS_1500lx, and mean_yield were clustered together. These five variables were used to replace the 13 indicator variables for the subsequent light effect classification. Step 6: Use the five variables in step 5 above to conduct a second-order cluster analysis on chestnut germplasm and divide it into three types: high light efficiency, medium light efficiency, and low light efficiency; Step 7: Using the second-order clustering method, we further classified germplasms with different light efficiency types in the range of 100-1500 μmol·m -2 ·s -1 The photosynthetic parameters change under different light intensities, and the photosynthetic sensitivity of the three types of chestnut germplasm classified in step 6, namely, high photosynthetic efficiency, medium photosynthetic efficiency, and low photosynthetic efficiency, were graded. The high photosynthetic efficiency chestnut germplasm was then divided into six photosynthetic types, namely, high photosynthetic efficiency photosynthetic inert, high photosynthetic efficiency photosynthetic sensitive, medium photosynthetic efficiency photosynthetic inert, medium photosynthetic efficiency photosynthetic sensitive, low photosynthetic efficiency photosynthetic inert, and low photosynthetic efficiency photosynthetic sensitive, to evaluate the photosynthetic efficiency of chestnut.

2. A chestnut photosynthetic efficiency high-precision physiological marker and classification method according to claim 1, characterized in that: In steps 1 to 7, data is entered using Microsoft Excel 2020 and then analyzed using IBM SPSS Statistics 27.

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