A wheat variety selection method based on multi-index preference solution distance analysis

Abstract

The application discloses a kind of wheat variety selection methods based on multi-index merit solution distance analysis, it is related to the field of wheat cultivation, comprising: constructing green low-carbon wheat variety selection index system;Build test field, and cultivation comparison test is carried out to each wheat variety;Sample collection and index determination are carried out to each wheat variety in test field;Data preprocessing is carried out to index determination result;Based on the data after each index preprocessing, principal component analysis is carried out and the common factor weight of each index is calculated;According to each wheat variety and index value and corresponding weight, merit solution distance analysis is carried out and wheat variety is selected according to analysis result.The application method is novel and unique, scientific and reasonable, easy to operate, can be effectively used for green low-carbon wheat variety selection, has very strong practical value, can provide effective technical support for green low-carbon wheat variety selection, can realize the green low-carbon cultivation goal of wheat, has significant economic benefit, ecological benefit, social benefit.

Description

Technical Field

[0001] This invention relates to the field of wheat cultivation technology, and more specifically to a method for wheat variety selection based on multi-index superiority-inferiority distance analysis. Background Technology

[0002] The selection of green and low-carbon wheat varieties is a key technological measure to ensure food security, improve wheat quality, and address climate change, and is of great significance. Global agricultural carbon emissions account for 12%-14% of total anthropogenic emissions, and dryland carbon emissions account for approximately 19% of global food system carbon emissions. Nitrous oxide is a major source of carbon dioxide in dryland crops, and its warming potential is 298 times that of carbon dioxide (usually converted to carbon emissions; the carbon emissions mentioned in this patent include nitrous oxide emissions converted to carbon dioxide). Excessive application of nitrogen fertilizer is one of the main reasons for the persistently high nitrous oxide emissions from dryland crops, significantly impacting global climate change. Furthermore, in the selection of wheat varieties, low heavy metal accumulation and high yield are not contradictory. Targeted selection of wheat varieties that combine green and high yield can achieve synergy between the two. This synergy between low heavy metal accumulation and high yield is a core pathway to reduce the harm of heavy metal pollution and improve wheat quality. Different wheat varieties exhibit significant differences in their ability to absorb, translocate, and accumulate heavy metals. Selecting varieties with low heavy metal accumulation can reduce grain heavy metal content while increasing wheat yield, thus lowering food safety risks.

[0003] However, current wheat variety selection still faces some problems. On the one hand, some wheat growers lack scientific knowledge of variety selection, relying solely on experience and habit to choose wheat varieties, leading to variety homogenization and increasing the risk of pests and diseases. On the other hand, the extensive use of chemical fertilizers and pesticides during wheat cultivation increases heavy metal content and carbon emissions. Single-indicator wheat variety selection, including rust-resistant, drought-resistant, and high-phosphorus-efficiency varieties, does not address green and low-carbon indicators; marker-based wheat variety selection is mainly used for breeding, and the actual expression of genotype-superior varieties is influenced by the environment.

[0004] Therefore, how to select green and low-carbon varieties is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] In view of this, the present invention provides a wheat variety selection method based on multi-index superiority distance analysis. Taking medium-gluten wheat, which has the largest planting area and the widest application, as the object, the green and low-carbon indicators of different medium-gluten wheat varieties are measured, and the green and low-carbon indicators of different varieties are normalized. The weight of each selection indicator is calculated by principal component analysis, and superiority distance analysis is performed using SPSSAU online software to select medium-gluten wheat varieties. The selected wheat varieties have high green and low-carbon economic benefits, ecological benefits, and social benefits.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: This invention discloses a wheat variety selection method based on multi-index superiority / inferiority distance analysis, comprising: Step 1: Construct an index system for selecting green and low-carbon wheat varieties; Step 2: Establish experimental fields and conduct comparative cultivation trials on various wheat varieties; Step 3: Collect samples and measure indicators for each wheat variety in the experimental field; Step 4: Perform data preprocessing on the indicator measurement results; Step 5: Based on the preprocessed data of each indicator, perform principal component analysis and calculate the common factor weights of each indicator; Step 6: Based on each wheat variety, index value, and corresponding weight, perform a distance analysis between superior and inferior solutions and select wheat varieties based on the analysis results.

[0007] Furthermore, the wheat variety selection index system is constructed based on wheat yield, grain heavy metal content, low carbon index, and disease resistance index, including: average yield, yield variation coefficient, grain cadmium content, grain mercury content, grain arsenic content, grain lead content, grain chromium content, nitrogen fertilizer use efficiency, water use efficiency, harvest index, carbon emissions per unit yield, stripe rust disease index, leaf spot disease index, and powdery mildew disease index.

[0008] Furthermore, step 2 specifically includes: in the crop experimental field, selecting farmland with similar soil physical and chemical properties, and selecting wheat varieties for cultivation comparison experiments; setting up 3 plots for each wheat variety, and randomly sampling the plots, with a plot size of 10×10 meters and a row spacing of 20 centimeters, setting up a protection zone around the experimental area, and setting the sowing density at 180,000 seedlings / mu.

[0009] Furthermore, the data preprocessing specifically includes: Using the ascending distribution function, the membership function values ​​of the average yield, nitrogen fertilizer use efficiency, water use efficiency, and harvest index are calculated using the following formula: ; In the formula, Represents the first of any type of indicator i Normalized values ​​of indicators for each wheat variety x i Represents the first of any type of indicator i Index values ​​for individual wheat varieties ，x min , x max These are the minimum and maximum values ​​of this type of indicator, respectively. i=1······ n ; Using the reduced distribution function, the membership function values ​​of the yield variation coefficient, grain cadmium content, grain mercury content, grain arsenic content, grain lead content, grain chromium content, carbon emissions per unit yield, stripe rust disease index, leaf spot disease index, and powdery mildew disease index are calculated using the following formula: .

[0010] Furthermore, the calculation of the common factor weights of each indicator specifically includes: Based on the principal component analysis results, the factor weights of the first to fourth principal components are calculated using the following formula: i =1, 2, 3, … , n ; In the formula, W i Indicates the first i The weights of each common factor among all common factors i For each wheat variety i One common factor, P i Indicates the number of each wheat variety i Contribution rate of each common factor; W i Normalization is performed: W ig =(W i -W min ) / (W max -W min ) ; In the formula, W ig Indicates the first i The normalized values ​​of the weights of all common factors. W min express W i The minimum value in, W max express W i The maximum value in.

[0011] Furthermore, the step of performing the superior-inferior solution distance analysis and selecting wheat varieties based on the analysis results specifically includes: calculating the positive ideal solution distance value, negative ideal solution distance value, and relative proximity value of each wheat variety based on the index value and corresponding common factor weight; then ranking the wheat varieties according to the positive ideal solution distance value, negative ideal solution distance value, and relative proximity value, and selecting the wheat varieties with the best overall performance.

[0012] As can be seen from the above technical solution, compared with the prior art, the present invention provides a wheat variety selection method based on multi-index superiority-inferiority solution distance analysis, which has the following beneficial effects: This invention measured 14 green and low-carbon indicators for different wheat varieties, calculated the relative similarity values ​​of each wheat variety, and quantitatively selected wheat varieties. Based on monitoring and testing experimental data, this invention uses scientific techniques and computer software to select suitable wheat varieties. The method is easy to operate, conforms to the actual situation of wheat variety selection, and has obtained consistent and similar results through repeated experiments. These results have been verified with local field conditions and are highly consistent. This invention's method is novel, unique, scientifically sound, and easy to operate. It can be effectively used for the selection of green and low-carbon wheat varieties, has strong practical value, provides effective technical support for the selection of green and low-carbon wheat varieties, and can achieve the goal of green and low-carbon wheat cultivation, resulting in significant economic, ecological, and social benefits. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0014] Figure 1 This is a schematic diagram of the overall process of the present invention.

[0015] Figure 2 A schematic diagram of the wheat variety selection index system provided by the present invention.

[0016] Figure 3 This is a schematic diagram of the data normalization and weight calculation method provided by the present invention.

[0017] Figure 4 A schematic diagram illustrating the relative proximity value calculation method provided by this invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] This invention discloses a wheat variety selection method based on multi-index superiority / inferiority distance analysis, such as... Figure 1 As shown, it includes: Step 1: Construct an index system for selecting green and low-carbon wheat varieties; Step 2: Establish experimental fields in wheat production bases to conduct comparative cultivation trials on various wheat varieties; Step 3: Collect samples and measure indicators for each wheat variety in the experimental field; Step 4: Perform data preprocessing on the indicator measurement results; Step 5: Based on the preprocessed data of each indicator, perform principal component analysis and calculate the common factor weights of each indicator; Step 6: Based on each wheat variety, index value, and corresponding weight, perform a distance analysis between superior and inferior solutions and select wheat varieties based on the analysis results.

[0020] In one specific embodiment, the wheat variety selection index system is constructed based on wheat yield, grain heavy metal content (green index), low carbon index, and disease resistance index (disease-resistant varieties can reduce pesticide use and carbon input, which is an indirect low carbon index). It consists of 14 evaluation indicators in 4 index categories, including: average yield, yield variation coefficient, grain cadmium content, grain mercury content, grain arsenic content, grain lead content, grain chromium content, nitrogen fertilizer use efficiency, water use efficiency, harvest index, carbon emissions per unit yield, stripe rust disease index, leaf spot disease index, and powdery mildew disease index.

[0021] In a specific embodiment, step 2 specifically includes: in the crop experimental field, selecting farmland with similar soil physicochemical properties, and selecting medium-gluten wheat varieties with large local cultivation areas for cultivation comparison experiments; setting up 3 plots for each wheat variety, and randomly sampling the plots to eliminate errors caused by differences in soil physicochemical properties, with plot size of 10×10 meters and row spacing of 20 centimeters, and setting up a protection zone around the experimental area, with a sowing density of 180,000 seedlings / mu, as the site for determining wheat variety selection indicators.

[0022] The method for determining and calculating the selection indicators for wheat varieties is to determine and calculate the indicator values ​​for each wheat variety according to the technical specifications in Table 1.

[0023] Table 1. Index Categories and Measurement Calculation Methods

[0024] Table 2 shows the experimental, monitoring and testing data of 14 indicators for 6 commonly used wheat varieties in Henan Province. To avoid unnecessary contradictions, the wheat variety names are represented by numbers.

[0025] Table 2 Index values ​​for different wheat varieties

[0026] In one specific embodiment, data preprocessing specifically includes: Using the ascending distribution function, the membership function values ​​of average yield, nitrogen fertilizer use efficiency, water use efficiency, and harvest index are calculated using the following formula: ; In the formula, Represents the first of any type of indicator Normalized values ​​of indicators for each wheat variety x i Represents the first of any type of indicator i Index values ​​for individual wheat varieties ，x min , x max These are the minimum and maximum values ​​of this type of indicator, respectively. i =1······ n ; Using the reduced distribution function, the membership function values ​​for yield variation coefficient, grain cadmium content, grain mercury content, grain arsenic content, grain lead content, grain chromium content, carbon emissions per unit yield, stripe rust disease index, leaf spot disease index, and powdery mildew disease index are calculated. The formula is as follows: .

[0027] Specifically, such as Figure 3 As shown, to eliminate the differences in dimensions among the selected indicators, the observation and detection data of the selected indicators were normalized. Using Excel 2010 software, the membership function values ​​for wheat yield, nitrogen fertilizer absorption efficiency, water absorption efficiency, and harvest index were calculated using the ascending distribution function. The membership function values ​​for yield variation coefficient, grain cadmium content, grain mercury content, grain arsenic content, grain lead content, grain chromium content, carbon emissions per unit yield, stripe rust disease index, leaf spot disease index, and powdery mildew disease index were calculated using the descending distribution function. Based on the data in Table 2, the normalized values ​​of each factor were calculated using Excel 2010 software, as shown in Table 3.

[0028] Table 3. Normalized values ​​of selection indicators for different wheat varieties

[0029] In a specific embodiment, calculating the common factor weights of each indicator specifically includes: Based on the principal component analysis results, the factor weights of the first to fourth principal components are calculated using the following formula: i =1, 2, 3, … , n ; In the formula, W i Indicates the first i The weights of each common factor among all common factors i For each wheat variety i One common factor, P i Indicates the number of each wheat variety i Contribution rate of each common factor; W i Normalization is performed: W ig =(W i -W min ) / (W max -W min ) ; In the formula, W ig Indicates the first i The normalized values ​​of the weights of all common factors. W min express W i The minimum value in, W max express W i The maximum value in.

[0030] Specifically, the principal component score coefficients were calculated based on the analysis results of SPSS 26.0 software, and the principal component score coefficient matrix was obtained, as shown in Table 4.

[0031] Table 4 Principal Component Score Coefficient Matrix

[0032] Table 4 lists the principal component analysis results for different factors calculated using SPSS 26.0. W iThe calculation formula is used to calculate the factor weights of the first to fourth principal components, and then the common factor weights are calculated using Excel 2010 software, as shown in Table 5.

[0033] Table 5 Common Factor Weights

[0034] In a specific embodiment, performing the Top-to-Bottom Solution Distance (TOPSIS) analysis and selecting wheat varieties based on the analysis results specifically includes: calculating the positive ideal solution distance value, negative ideal solution distance value, and relative proximity value for each wheat variety based on its index value and corresponding common factor weight; then ranking the wheat varieties according to these values ​​and selecting the wheat varieties with the best overall performance. The specific calculation of the Top-to-Bottom Solution Distance (TOPSIS) involves logging into the SPSSAU website and, according to the attached... Figure 4 The steps described above involve calculating the positive and negative rational distances and relative proximity of each wheat variety, as shown in Table 6.

[0035] Table 6 Evaluation Results of Distance Between Superior and Inferior Solutions

[0036] Table 6 shows that TOPSIS evaluation was conducted on 14 green and low-carbon indicators for 6 wheat varieties, and the distance value D between the positive and negative ideal solutions for each wheat variety was calculated. + and D - The ideal solution D + Larger wheat varieties generally have better overall performance, and the secondary ideal solution D is... - Larger wheat varieties generally have poorer overall performance. Using the relative similarity score (Ci) obtained from TOPSIS evaluation, wheat varieties were ranked, with varieties showing higher relative similarity scores exhibiting better overall performance. Green and low-carbon wheat variety selection: Table 6 shows the relative similarity scores of different wheat varieties as follows: Wheat No. 6 > Wheat No. 1 > Wheat No. 3 > Wheat No. 4 > Wheat No. 5 > Wheat No. 2. Based on the multi-indicator relative similarity scores reflecting the overall differences among different wheat varieties, relative similarity can be used as a basis for wheat variety selection. Before wheat cultivation, varieties with higher relative similarity scores, i.e., better overall performance, should be prioritized.

[0037] As can be seen from the above wheat variety selection process, this invention measured 14 green and low-carbon indicators for different wheat varieties, calculated the relative similarity values ​​of each wheat variety, and quantitatively selected wheat varieties. To achieve the goal of green and low-carbon wheat cultivation, suitable wheat varieties were selected using scientific techniques and computer software based on monitoring and testing experimental data. The method is easy to operate, conforms to the actual situation of wheat variety selection, and has obtained the same or similar results through repeated experiments. These results were verified with local field conditions and showed great consistency, indicating that the method is stable and reliable, has practical application value, can provide an effective technical means for the selection of green and low-carbon wheat varieties, can achieve the goal of green and low-carbon wheat cultivation, and has significant economic, ecological, and social benefits.

[0038] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0039] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for wheat variety selection based on multi-index superiority / inferiority distance analysis, characterized in that, include: Step 1: Construct an index system for selecting green and low-carbon wheat varieties; Step 2: Establish experimental fields and conduct comparative cultivation trials on various wheat varieties; Step 3: Collect samples and measure indicators for each wheat variety in the experimental field; Step 4: Perform data preprocessing on the indicator measurement results; Step 5: Based on the preprocessed data of each indicator, perform principal component analysis and calculate the common factor weights of each indicator; Step 6: Based on each wheat variety, index value, and corresponding weight, perform a distance analysis between superior and inferior solutions and select wheat varieties based on the analysis results.

2. The wheat variety selection method based on multi-index superiority / inferiority distance analysis according to claim 1, characterized in that, The wheat variety selection index system is constructed based on wheat yield, grain heavy metal content, low carbon index and disease resistance index, including: average yield, yield variation coefficient, grain cadmium content, grain mercury content, grain arsenic content, grain lead content, grain chromium content, nitrogen fertilizer use efficiency, water use efficiency, harvest index, carbon emissions per unit yield, stripe rust disease index, leaf spot disease index and powdery mildew disease index.

3. The wheat variety selection method based on multi-index superiority / inferiority distance analysis according to claim 1, characterized in that, Step 2 specifically includes: in the crop experimental field, select farmland with similar soil physical and chemical properties, and select wheat varieties for cultivation comparison experiment; set up 3 plots for each wheat variety, and set up the plots by random sampling. The plot size is 10×10 meters, the row spacing is 20 centimeters, and a protection zone is set up around the experimental area. The sowing density is set at 180,000 seedlings / mu.

4. The wheat variety selection method based on multi-index superiority / inferiority distance analysis according to claim 2, characterized in that, The data preprocessing specifically includes: Using the ascending distribution function, the membership function values ​​of the average yield, nitrogen fertilizer use efficiency, water use efficiency, and harvest index are calculated using the following formula: ； In the formula, Represents the first of any type of indicator Normalized values ​​of indicators for each wheat variety x i Represents the first of any type of indicator Index values ​​for individual wheat varieties ，x min , x max These are the minimum and maximum values ​​of this type of indicator, respectively. i =1······ n ; Using the reduced distribution function, the membership function values ​​of the yield variation coefficient, grain cadmium content, grain mercury content, grain arsenic content, grain lead content, grain chromium content, carbon emissions per unit yield, stripe rust disease index, leaf spot disease index, and powdery mildew disease index are calculated using the following formula: 。 5. The wheat variety selection method based on multi-index superiority / inferiority distance analysis according to claim 1, characterized in that, The calculation of the common factor weights of each indicator specifically includes: Based on the principal component analysis results, the factor weights of the first to fourth principal components are calculated using the following formula: i =1, 2, 3 , … , n ； In the formula, W i Indicates the first i The weights of each common factor among all common factors i For each wheat variety i One common factor, P i Indicates the number of each wheat variety i Contribution rate of each common factor; W i Normalization is performed: W ig = ( W i -W min ) / ( W max -W min )； In the formula, W ig Indicates the first i The normalized values ​​of the weights of all common factors. W min express W i The minimum value in, W max express W i The maximum value in.

6. The wheat variety selection method based on multi-index superiority / inferiority distance analysis according to claim 1, characterized in that, The process of performing the distance analysis of superior and inferior solutions and selecting wheat varieties based on the analysis results specifically includes: calculating the positive ideal solution distance value, negative ideal solution distance value, and relative proximity value of each wheat variety based on the index value and corresponding common factor weight; then ranking the wheat varieties according to the positive ideal solution distance value, negative ideal solution distance value, and relative proximity value, and selecting the wheat varieties with the best overall performance.

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