A method for quantifying the implementation effect of a conservation tillage technique
The comprehensive evaluation index constructed by principal component analysis and entropy weight method solves the problems of fragmented indicator system and subjective weighting in the quantification of conservation tillage effectiveness in existing technologies. It realizes a comprehensive quantification and robust evaluation of the implementation effect of conservation tillage technology, and provides a scientific basis for decision-making and directions for improvement.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- NORTHEAST INST OF GEOGRAPHY & AGRIECOLOGY C A S
- Filing Date
- 2026-04-30
- Publication Date
- 2026-06-05
AI Technical Summary
Existing comprehensive farmland evaluation methods suffer from problems such as fragmented indicator systems, insufficient information integration capabilities, strong subjectivity in weight determination, and lack of unified standards in the construction of quantitative indices when quantifying the effectiveness of conservation tillage. These problems result in poor objectivity and low stability of quantitative results, making it difficult to meet the precision and objectivity requirements of modern agriculture.
Principal component analysis (PCA) was used to integrate soil nutrient index and crop growth index, and entropy weighting was used to construct a comprehensive evaluation index. Through the preprocessing and standardization of multi-source agricultural basic data, a three-dimensional quantitative framework based on soil nutrients, crop growth and topographic location was constructed to achieve effective information integration and objective weight allocation.
It enables a comprehensive characterization of the effects of conservation tillage technology, improves the robustness and interpretability of quantitative results, provides a scientific basis for technology promotion and application decisions, and can identify dominant factors and guide precision fertilization and field management.
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Figure CN122155537A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of agricultural ecological environment assessment technology, and also to the field of multi-source heterogeneous data processing technology. Background Technology
[0002] Conservation tillage, by improving soil structure, increasing organic matter content, and reducing soil erosion, is considered a crucial technological pathway for achieving green agricultural development and efficient resource utilization. Scientifically quantifying the effectiveness of conservation tillage is of great significance for guiding its promotion and application and ensuring food security. Research shows that the effects of conservation tillage are not only reflected in changes in crop yield but also in improvements in soil nutrient status, enhanced crop growth, and changes in resource utilization efficiency under different terrain conditions. Therefore, there is an urgent need to construct a quantitative system that can comprehensively reflect the synergistic changes in the soil-crop-environment system.
[0003] Current farmland comprehensive evaluation methods have significant limitations when used to quantify the effectiveness of conservation tillage: In terms of indicator system construction, existing methods either focus only on soil nutrients or only on crop growth, lacking a systematic indicator integration mechanism for the implementation effect of conservation tillage technology; in terms of weight determination mechanism, subjective weighting methods rely on expert experience and are difficult to avoid human bias, while machine learning models, although able to extract feature importance, are sensitive to sample size, complex, and lack interpretability; in terms of multi-source heterogeneous data fusion processing, existing technologies mostly use simple weighted summation, failing to fully consider the correlation and information redundancy between indicators, and the inconsistent scales of different data sources, the existence of outliers and missing values, make the quantitative results susceptible to the influence of extreme samples.
[0004] In summary, existing farmland comprehensive evaluation technologies, when used to quantify the effectiveness of conservation tillage, generally suffer from problems such as fragmented indicator systems, insufficient information integration capabilities, strong subjectivity in weight determination, and a lack of unified standards in the construction of quantitative indices. These issues result in poor objectivity and low stability of the quantitative results, making it difficult to meet the needs of modern agriculture for refined and objective quantification of the effects of conservation tillage. Summary of the Invention
[0005] This invention solves the problems of fragmented indicator systems, insufficient information integration capabilities, strong subjectivity in weight determination, and lack of unified standards in the construction of quantitative indices in the prior art.
[0006] A method for quantifying the effectiveness of conservation tillage techniques, the method comprising the following steps: Step 1: Obtain multi-source agricultural basic data within the cultivated land area. The multi-source agricultural basic data set includes soil nutrient data, crop vegetation index data, and topographic location index within the cultivated land area. Step 2: Preprocess the multi-source agricultural basic data; Step 3: Based on the soil nutrient data and crop vegetation index data, construct the soil nutrient index and crop growth index through principal component analysis; Step 4: The soil nutrient index, crop growth index, and topographic location index are used as sub-indices. The entropy weight method is used to assign weights to the sub-indices to construct a comprehensive evaluation index as a quantitative indicator for evaluating the effectiveness of conservation tillage technology implementation in the cultivated land area.
[0007] In a further optimized scheme, in step 1, the soil nutrient data includes soil organic matter content, total nitrogen content, total phosphorus content, and total potassium content.
[0008] In a further optimized approach, the soil nutrient data is derived from the test results of on-site sampling in the cultivated land area.
[0009] Further optimization of the scheme: In step 1, the crop vegetation index data includes normalized vegetation index value, enhanced vegetation index value, red-edge normalized vegetation index value, and green-light normalized vegetation index value.
[0010] In a further preferred embodiment, the crop vegetation index data is obtained based on remote sensing image data of the cultivated land area for the previous N consecutive years, where N is an integer greater than 2.
[0011] In a further optimized approach, in step 1, the terrain location index is calculated based on the digital elevation model data of the cultivated land area.
[0012] In a further optimized approach, step 2, the preprocessing includes outlier identification and processing, missing value imputation, and standardization transformation of the multi-source agricultural basic data.
[0013] Further optimizing the scheme, step 3, the construction of the soil nutrient index and crop growth index includes the following steps: Step 31: Construct a soil nutrient raw data matrix based on the soil nutrient data, and construct a crop growth raw data matrix based on the crop vegetation index data; Step 32: Perform Z-score standardization on the original soil nutrient data matrix and the original crop growth data matrix to obtain the corresponding standardized soil nutrient data matrix and standardized crop growth data matrix. Step 33: Construct a soil nutrient covariance matrix based on the soil nutrient standardized data matrix, and construct a crop growth covariance matrix based on the crop growth standardized data matrix; Step 34: Perform eigenvalue decomposition on the soil nutrient covariance matrix and the crop growth covariance matrix respectively, solve the characteristic equations, obtain the corresponding eigenvalues and eigenvectors, and calculate the variance contribution rate of each principal component based on the eigenvalues. Step 35: Select the first principal component based on the variance contribution rate of the soil nutrient covariance matrix, and use the eigenvector of the first principal component to perform a linear combination with the standardized soil nutrient data matrix to calculate the first principal component score, which is then used as the soil nutrient index; Select the first principal component based on the variance contribution rate of the crop growth variance matrix, and use the eigenvector of the first principal component to perform a linear combination with the standardized crop growth data matrix to calculate the first principal component score, which is then used as the crop growth index.
[0014] To further optimize the solution, step 4 involves constructing the comprehensive evaluation index, which includes the following steps: Step 41: Construct an original evaluation matrix based on the multi-source agricultural basic data and the sub-indices; Step 42: Standardize the original evaluation matrix to obtain a standardized matrix; Step 43: Convert the standardized matrix into a scaling matrix; Step 44: Calculate the sub-index information entropy based on the ratio matrix; Step 45: Calculate the difference index based on the information entropy; Step 46: Calculate the weights of the sub-indices based on the difference index; Step 47: Construct a comprehensive evaluation index based on the weights and weighted linear model.
[0015] The beneficial effects of this invention compared to the prior art are as follows: The method described in this application addresses the specific need for quantifying the effectiveness of conservation tillage technology by constructing a three-dimensional quantitative framework centered on soil nutrient index, crop growth index, and topographic location index. This framework achieves a comprehensive characterization of the effectiveness of conservation tillage technology from three dimensions: "soil basic conditions - crop growth response - topographic environmental characteristics." It overcomes the problems of fragmented indicator systems and focus on only a single dimension in existing technologies, enabling the quantitative results to comprehensively reflect the overall impact of conservation tillage on farmland ecosystems and providing a scientific basis for decision-making in technology promotion and application.
[0016] The method described in this application performs principal component analysis on multiple soil nutrient indicators and multiple crop growth indicators, extracting the first principal component as the corresponding comprehensive sub-index. This achieves effective fusion and condensation of information from multiple sources of the same type of indicators. While retaining the main information characteristics of the original indicators, it significantly reduces multicollinearity and information redundancy among indicators, and avoids the repeated amplification of relevant information by simple weighted summation. Through this processing, the constructed soil nutrient index and crop growth index have clear physical meaning and higher data quality, providing an objective and stable basic input for subsequent comprehensive quantification.
[0017] The method described in this application employs an entropy weighting method based on information entropy theory to adaptively assign weights to the soil nutrient index, crop growth index, and topographic location index. This enables the weight coefficients to be dynamically adjusted according to the distribution characteristics of the sample data, avoiding the human bias introduced by subjective weighting methods such as the analytic hierarchy process and the Delphi method, which rely on expert experience. It also overcomes the limitations of machine learning models, such as their sensitivity to sample size and insufficient interpretability. This mechanism allows the weight allocation to objectively reflect the true differences in the contributions of each sub-index in different regions and years, significantly improving the versatility and repeatability of the quantification method.
[0018] The method described in this application achieves hierarchical and structured processing from raw indicators to a comprehensive quantitative index through a two-level quantitative model: first, PCA fusion to construct sub-indices, and then entropy weighting for weighted fusion. The first-level PCA fusion eliminates information redundancy among similar indicators and improves data quality, while the second-level entropy weighting ensures the objectivity of weight allocation. This organically combined model architecture gives the comprehensive quantitative results strong fault tolerance to abnormal fluctuations in individual indicators, effectively avoiding distortion of evaluation results caused by single sampling errors or measurement noise, and significantly improving the robustness of conservation tillage effectiveness quantification.
[0019] The comprehensive farmland quantitative index constructed by the method described in this application is obtained by weighted summation of soil nutrient index, crop growth index and topographic location index. This comprehensive quantitative result is not only a quantitative value, but also reveals the dominant factors that limit the effectiveness of conservation tillage implementation—whether it is insufficient soil fertility, poor crop growth response, or topographic limitations. This high interpretability provides clear directions for improvement and scientific basis for subsequent precision fertilization, field management and technology optimization, and has important practical application value.
[0020] The invention described herein is applicable to fields such as agricultural ecological environment monitoring, agricultural information technology, and multi-source data fusion and quantification. Attached Figure Description
[0021] Figure 1 This is a flowchart of a method for constructing a comprehensive evaluation index for the effectiveness of conservation tillage technology as described in Implementation Method 1. Figure 2 This is a comprehensive evaluation index distribution map of the cultivated land area obtained in Implementation Method 7. Detailed Implementation
[0022] Various embodiments of the present invention will now be clearly and completely described with reference to the accompanying drawings. The embodiments described with reference to the drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.
[0023] Implementation Method 1: This implementation method provides a method for quantifying the effectiveness of conservation tillage techniques. The quantification method includes the following steps: Step 1: Obtain multi-source agricultural basic data within the cultivated land area. The multi-source agricultural basic data set includes soil nutrient data, crop vegetation index data, and topographic location index within the cultivated land area. Step 2: Preprocess the multi-source agricultural basic data; Step 3: Based on the soil nutrient data and crop vegetation index data, construct the soil nutrient index and crop growth index through principal component analysis; Step 4: The soil nutrient index, crop growth index, and topographic location index are used as sub-indices. The entropy weight method is used to assign weights to the sub-indices to construct a comprehensive evaluation index as a quantitative indicator for evaluating the effectiveness of conservation tillage technology implementation in the cultivated land area.
[0024] Implementation Method 2: This implementation method further defines Implementation Method 1 and provides an example of the soil nutrient data in step 1.
[0025] In constructing the soil nutrient index, this embodiment selects four key physicochemical indicators that can comprehensively reflect the soil fertility status. The soil nutrient data include soil organic matter (SOM) content, total nitrogen (TN) content, total phosphorus (TP) content, and total potassium (TK) content. Among them, SOM is an important indicator for measuring soil structural stability and nutrient supply capacity; TN reflects the soil nitrogen storage level; TP reflects the soil phosphorus supply potential; and TK characterizes the soil potassium nutrient base.
[0026] The soil nutrient data were derived from field sampling in cultivated areas. Sampling was conducted according to a unified sampling principle, obtaining surface soil samples, and standardized experimental analysis methods were used to determine the content of various physicochemical indicators to ensure the accuracy and comparability of the data. This ultimately forms a field-measured database of soil nutrients in cultivated areas, providing reliable data support for subsequent soil nutrient analysis.
[0027] Implementation Method 3: This implementation method further defines Implementation Method 1 and provides an example of the crop vegetation index data.
[0028] In constructing the crop growth index, to comprehensively and stably reflect the crop growth status in the study area, several typical vegetation indices that can effectively characterize vegetation vitality and canopy structure characteristics were selected as evaluation indicators. The crop vegetation index data types include Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Red Edge Normalized Difference Vegetation Index (NDRE), and Green Light Normalized Difference Vegetation Index (GNDVI). These indices reflect vegetation chlorophyll content, canopy coverage, and biomass levels from different spectral band combinations, and can comprehensively characterize crop growth features.
[0029] The crop vegetation index of cultivated land area is obtained from remote sensing image data, which can be achieved using existing technologies. To reduce the impact of climate bands and outliers in a single year on the results, this embodiment uses the crop vegetation index of the previous N consecutive years to obtain the crop vegetation index data. Specifically, the median of data from several consecutive years can be taken as the representative value. The median is the value in the middle of multiple data points. Using the median instead of the average value helps to reduce the interference caused by extreme weather or data noise, and improves the robustness and representativeness of the index. The calculation formulas for each vegetation index are shown in Table 1.
[0030] Table 1
[0031] Implementation Method Four: This implementation method further defines Implementation Method One and provides an example of the terrain location index.
[0032] The topographic location index is calculated based on the digital elevation model data of the cultivated land area.
[0033] Digital elevation models (DEMs) are datasets that express the undulations of the ground using elevation values of regular grid points. They are obtained through surveying and mapping technologies such as aerospace remote sensing and airborne lidar measurement, and are used to describe the topographic features of the Earth's surface. They are existing technologies known in this field.
[0034] The terrain location index is calculated as follows: First, the average elevation value within a set radius centered on the target grid is calculated using a neighborhood statistical method. Then, the neighborhood average elevation value is subtracted from the grid elevation value to obtain the terrain location index.
[0035] in, The target raster elevation value, The average elevation value of the area. >0 indicates that the point is higher than the surrounding area. <0 indicates that it is lower than the surrounding area. ≈0 indicates a slope or flat area.
[0036] This implementation uses the Focal Statistics tool in ArcGIS software to calculate the neighborhood average elevation and the Raster Calculator tool to calculate the raster difference to obtain the study area. The data will be used for the construction of a comprehensive evaluation index.
[0037] Implementation Method 5: This implementation method is a further limitation of Implementation Method 1, and provides an example of the preprocessing in step 2.
[0038] The preprocessing includes outlier identification and processing, missing value imputation, and standardization transformation of the multi-source agricultural basic data; The outlier identification method uses box plots. The processing method and the missing value imputation use the median. Compared with the mean, the median is more representative of the central trend of the data and will not be shifted by the outliers themselves. The standardization transformation employs range standardization and Z-score standardization to eliminate the influence of different dimensions.
[0039] Implementation Method Six: This implementation method is a further limitation of Implementation Method One, and provides an example of the construction of soil nutrient index and crop growth index in step 3.
[0040] Step 31: Construct a soil nutrient raw data matrix based on the soil nutrient data, and construct a crop growth raw data matrix based on the crop vegetation index data; Specifically: Constructing the original soil nutrient data matrix:
[0041] in, For the sample size, For soil nutrient data types, Indicates the first The sample at the th Observed values for each soil nutrient index; Constructing the original crop growth data matrix:
[0042] in, For the sample size, This represents the number of crop vegetation index types. Indicates the first The sample at the th Observed values on crop vegetation indices; Step 32: Perform Z-score standardization on the original soil nutrient data matrix and the original crop growth data matrix to obtain the corresponding standardized soil nutrient data matrix and standardized crop growth data matrix. Specifically, the original soil nutrient data matrix is Z-score standardized to obtain a standardized soil nutrient data matrix.
[0043] in, For the first The sample mean of each indicator, For the first Standard deviation of each indicator; The original crop growth data matrix was Z-score standardized to obtain the standardized crop growth data matrix:
[0044] in, For the first The sample mean of each indicator, For the first Standard deviation of each indicator; Step 33: Construct a soil nutrient covariance matrix based on the soil nutrient standardized data matrix, and construct a crop growth covariance matrix based on the crop growth standardized data matrix; Specifically: Construct a soil nutrient covariance matrix based on the aforementioned standardized soil nutrient data matrix:
[0045] Construct a crop growth covariance matrix based on the standardized crop growth data matrix:
[0046] Step 34: Perform eigenvalue decomposition on the soil nutrient covariance matrix and the crop growth covariance matrix respectively, solve the characteristic equations, obtain the corresponding eigenvalues and eigenvectors, and calculate the variance contribution rate of each principal component based on the eigenvalues. Specifically: For the soil nutrient covariance matrix Perform eigenvalue decomposition and solve the characteristic equation:
[0047] in, Represents eigenvalues. Represents the identity matrix with dimension . × ; Obtain eigenvalues and the corresponding feature vectors The variance contribution rate of each principal component is calculated based on the eigenvalues:
[0048] in Indicates the first The variance contribution rate of the principal component, i.e. the th principal component The proportion of variance explained by each principal component to the total variance explained by all principal components, in the denominator. The sum of all eigenvalues equals the total variance of the original data; The crop growth covariance matrix Perform eigenvalue decomposition and solve the characteristic equation:
[0049] in, Represents eigenvalues. Represents the identity matrix with dimension . × ; Obtain eigenvalues and the corresponding feature vectors The variance contribution rate of each principal component is calculated based on the eigenvalues:
[0050] in Indicates the first The variance contribution rate of the principal component, i.e. the th principal component The proportion of variance explained by each principal component to the total variance explained by all principal components, in the denominator. The sum of all eigenvalues equals the total variance of the original data; Step 35: Select the first principal component based on the variance contribution rate of the soil nutrient covariance matrix. Calculate the first principal component score by linearly combining the eigenvectors of the first principal component with the standardized soil nutrient data matrix. Use the first principal component score as the soil nutrient index.
[0051] in, The coefficients of the eigenvectors of the first principal component are... For the first Soil nutrient index of each sample; The first principal component is selected based on the variance contribution rate of the crop growth variance matrix. The eigenvectors of the first principal component are linearly combined with the standardized crop growth data matrix to calculate the score of the first principal component. The score of the first principal component is then used as the crop growth index.
[0052] in, The coefficients of the eigenvectors of the first principal component are... For the first Crop growth index of each sample.
[0053] Implementation Method Seven: This implementation method is a further limitation of Implementation Method Five, and provides an example of the construction of the comprehensive evaluation index in step 4.
[0054] The construction of the comprehensive evaluation index includes the following steps: Step 41: Construct the original evaluation matrix based on the multi-source agricultural basic data and the sub-indices:
[0055] in, To evaluate the sample size, The number of sub-indices, Indicates the first The sample at the th In this embodiment, the values of the sub-indices are... These are the soil nutrient index, crop growth index, and topographic location index, respectively. Step 42: Standardize the original evaluation matrix to obtain a standardized matrix; Since the sub-indices have different dimensions, they are first processed to be dimensionless, using the range standardization formula:
[0056] in, The standardized index value, and The first The maximum and minimum values of each indicator; after standardization, ; Step 43: Convert the standardized matrix into a scaling matrix; To calculate information entropy, the normalized matrix is converted into a scaling matrix:
[0057] in, Indicates the first The sample at the th The proportion of each indicator; Step 44: Calculate the sub-index information entropy based on the ratio matrix; The information entropy of the j-th indicator is defined as:
[0058] in, For the first The information entropy of each indicator, because the entropy value is processed by a coefficient, After standardization, it meets the requirements. .
[0059] Step 45: Calculate the difference index based on the information entropy:
[0060] in, Indicates the first For each indicator, the smaller the entropy value, the larger the difference coefficient, indicating that the indicator shows more obvious differences among samples and provides more information. Step 46: Calculate the weights of the sub-indices based on the difference index:
[0061] in, for The sub-index weights satisfy: ; Step 47: Based on the weights and weighted linear model, construct a comprehensive evaluation index:
[0062] in, For the first The comprehensive evaluation index of each sample , , These are the weights of the soil nutrient index, the crop growth index, and the topographic location index. , and These are the normalized soil nutrient index, crop growth index, and topographic location index; Figure 2 This is a distribution map of the comprehensive evaluation index for the study area.
[0063] Implementation Method 8: This implementation method verifies the rationality of the comprehensive evaluation index construction method for the implementation effectiveness of conservation tillage technology described in the above implementation methods.
[0064] To verify the scientific validity and rationality of the constructed comprehensive evaluation index, a geographic detector model was used, with the comprehensive evaluation index as the dependent variable and soil nutrient index, crop growth index and topographic location index as explanatory variables, to conduct factor detection and interaction detection analysis.
[0065] As shown in Table 2, the results indicate that the soil nutrient index (q=0.378), topographic location index (q=0.365), and crop growth index (q=0.217) all have significant explanatory power for the comprehensive evaluation index. Further interaction detection shows that the explanatory power of any two factors combined is significantly higher than that of a single factor, and all show a dual-factor enhancement effect, with the interaction between the soil index and the topographic index having the highest explanatory power (q=0.697).
[0066] This result indicates that the spatial differentiation of agricultural production systems originates from the synergistic coupling between soil conditions, crop growth status, and topographic patterns, rather than being driven by a single factor. Therefore, constructing a comprehensive evaluation index based on the three-dimensional structure of "soil-crop-topography" has a clear theoretical basis and statistical support, and can effectively characterize the synergistic mechanisms and comprehensive production potential of agricultural systems.
[0067] Table 2
Claims
1. A method for quantifying the effectiveness of conservation tillage techniques, characterized in that, The quantification method includes the following steps: Step 1: Obtain multi-source agricultural basic data within the cultivated land area. The multi-source agricultural basic data set includes soil nutrient data, crop vegetation index data, and topographic location index within the cultivated land area. Step 2: Preprocess the multi-source agricultural basic data; Step 3: Based on the soil nutrient data and crop vegetation index data, construct the soil nutrient index and crop growth index through principal component analysis; Step 4: The soil nutrient index, crop growth index, and topographic location index are used as sub-indices. The entropy weight method is used to assign weights to the sub-indices to construct a comprehensive evaluation index as a quantitative indicator for evaluating the effectiveness of conservation tillage technology implementation in the cultivated land area.
2. The method for quantifying the effectiveness of conservation tillage technology according to claim 1, characterized in that, In step 1, the soil nutrient data includes soil organic matter content, total nitrogen content, total phosphorus content, and total potassium content.
3. The method for quantifying the effectiveness of conservation tillage technology according to claim 2, characterized in that, The soil nutrient data are derived from the test results of on-site sampling in the cultivated land area.
4. The method for quantifying the effectiveness of conservation tillage technology according to claim 1, characterized in that, In step 1, the crop vegetation index data includes normalized vegetation index (NDI) values, enhanced vegetation index values, red-edge normalized vegetation index values, and green-light normalized vegetation index values.
5. The method for quantifying the effectiveness of conservation tillage technology according to claim 4, characterized in that, The crop vegetation index data is obtained based on remote sensing image data of the cultivated land area for the previous N years, where N is an integer greater than 2.
6. The method for quantifying the effectiveness of conservation tillage technology according to claim 1, characterized in that, In step 1, the topographic location index is calculated based on the digital elevation model data of the cultivated land area.
7. The method for quantifying the effectiveness of conservation tillage technology according to claim 1, characterized in that, In step 2, the preprocessing includes outlier identification and processing, missing value imputation, and standardization transformation of the multi-source agricultural basic data.
8. The method for quantifying the effectiveness of conservation tillage technology according to claim 1, characterized in that, Step 3, the construction of the soil nutrient index and crop growth index includes the following steps: Step 31: Construct a soil nutrient raw data matrix based on the soil nutrient data, and construct a crop growth raw data matrix based on the crop vegetation index data; Step 32: Perform Z-score standardization on the original soil nutrient data matrix and the original crop growth data matrix to obtain the corresponding standardized soil nutrient data matrix and standardized crop growth data matrix. Step 33: Construct a soil nutrient covariance matrix based on the soil nutrient standardized data matrix, and construct a crop growth covariance matrix based on the crop growth standardized data matrix; Step 34: Perform eigenvalue decomposition on the soil nutrient covariance matrix and the crop growth covariance matrix respectively, solve the characteristic equations, obtain the corresponding eigenvalues and eigenvectors, and calculate the variance contribution rate of each principal component based on the eigenvalues. Step 35: Select the first principal component based on the variance contribution rate of the soil nutrient covariance matrix, and use the eigenvector of the first principal component to perform a linear combination with the standardized soil nutrient data matrix to calculate the first principal component score, which is then used as the soil nutrient index; Select the first principal component based on the variance contribution rate of the crop growth variance matrix, and use the eigenvector of the first principal component to perform a linear combination with the standardized crop growth data matrix to calculate the first principal component score, which is then used as the crop growth index.
9. The method for quantifying the effectiveness of conservation tillage technology according to claim 1, characterized in that, Step 4, the construction of the comprehensive evaluation index includes the following steps: Step 41: Construct an original evaluation matrix based on the multi-source agricultural basic data and the sub-indices; Step 42: Standardize the original evaluation matrix to obtain a standardized matrix; Step 43: Convert the standardized matrix into a scaling matrix; Step 44: Calculate the sub-index information entropy based on the ratio matrix; Step 45: Calculate the difference index based on the information entropy; Step 46: Calculate the weights of the sub-indices based on the difference index; Step 47: Construct a comprehensive evaluation index based on the weights and weighted linear model.