Soil quality evaluation method based on coupling of microbial diversity and organic matter content

Abstract

The present application belongs to the technical field of soil quality evaluation, and particularly relates to a soil quality evaluation method based on coupling of microbial diversity and organic matter content, aiming to solve the problems of single evaluation dimension, insufficient index coupling and large result deviation of the existing method. The method comprises sample collection and processing, microbial diversity and organic matter index detection, removal of abnormal values and redundant indexes, weight determination and standardization processing, coupling model construction and quality grading. The microbial diversity parameters are obtained through high-throughput sequencing, the contents of total organic matter, easily oxidized organic matter and difficultly oxidized organic matter are accurately determined and the activity ratio is calculated; the index weight is determined by coupling of analytic hierarchy process and entropy weight method, the dimension difference is eliminated by range standardization, the SQI value is calculated by constructing the model, and the quality is graded according to the five-level standard. The method realizes deep coupling of double core indexes, the evaluation result is accurate, the quality short board can be located, the guidance for targeted improvement is provided, and an evaluation-improvement closed loop is formed.

Description

Technical Field

[0001] This invention belongs to the field of soil quality assessment technology, and particularly relates to a soil quality assessment method based on the coupling of microbial diversity and organic matter content. Background Technology

[0002] Soil, as the core carrier of agricultural production and ecosystem stability, directly determines land productivity, nutrient cycling efficiency, and ecosystem service functions. Accurate soil quality assessment is a crucial prerequisite for scientific soil resource management, ecological restoration, and sustainable agricultural development. The core of soil quality assessment is constructing a scientific evaluation system to comprehensively characterize soil physicochemical properties, biological characteristics, and other multi-dimensional indicators. Microbial diversity and organic matter content are key indicators reflecting soil quality. Microorganisms, as drivers of soil nutrient cycling, have community structure and diversity directly related to soil material transformation and ecological functions. Organic matter, as a "reservoir" of soil nutrients, determines soil's fertilizer retention and supply capacity and carbon pool stability through its total amount and the distribution of its active components (easily oxidizable and recalcitrant organic matter). These two factors work synergistically to regulate soil quality evolution.

[0003] Existing soil quality assessment methods often suffer from limitations due to their reliance on a single dimension or insufficient coupling of indicators, making it difficult to accurately reflect the overall state of soil quality. On the one hand, traditional assessment methods often focus on soil physicochemical indicators, such as pH, nitrogen, phosphorus, and potassium content, and total organic matter, neglecting the core driving role of microbial diversity in soil ecological function. This results in assessments that fail to comprehensively characterize soil biological activity and health levels, hindering the guidance of targeted ecological restoration measures. On the other hand, some assessment methods that incorporate microbial indicators either simply superimpose microbial and organic matter indicators, failing to address issues such as redundancy and dimensional differences between indicators, or employ single-weighting methods, such as subjective weighting methods relying solely on expert experience or objective weighting methods based solely on data, to determine the contribution of indicators. This leads to biased assessment results or significant deviations from actual soil quality. Furthermore, the assessment of organic matter is often limited to total quantity, failing to distinguish the activity differences between easily oxidized and difficult-to-oxidize organic matter, thus failing to accurately reflect the dynamic impact of organic matter on soil quality, further reducing the scientific rigor and practicality of the assessment methods.

[0004] Therefore, there is an urgent need to develop a soil quality evaluation method that can achieve deep coupling between microbial diversity and organic matter content, has a scientific and standardized indicator system, reasonable weight allocation, and accurate and reliable evaluation results, so as to solve many of the technical problems of the existing technologies. Summary of the Invention

[0005] The purpose of this invention is to provide a soil quality evaluation method based on the coupling of microbial diversity and organic matter content, in order to solve the limitations of existing technologies, such as the single-dimensional limitations or insufficient index coupling, which make it difficult to accurately reflect the comprehensive status of soil quality. At the same time, the evaluation of organic matter is mostly limited to the total amount, without distinguishing the activity differences between easily oxidized and difficult-to-oxidize organic matter, and cannot accurately reflect the dynamic impact of organic matter on soil quality, further reducing the scientificity and practicality of the evaluation method.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0007] A soil quality assessment method based on the coupling of microbial diversity and organic matter content includes the following steps:

[0008] S1: Select the area to be evaluated, collect soil samples using the grid method, remove impurities from the samples, freeze-dry, grind and sieve to prepare microbial analysis samples and organic matter detection samples respectively;

[0009] S2: Take microbial analysis samples and obtain raw microbial sequencing data through high-throughput sequencing; filter, deduplicate, cluster OTUs and annotate species in the raw microbial sequencing data, and calculate the microbial Alpha diversity index and Beta diversity characteristic parameters.

[0010] S3: Take organic matter test samples, determine the total SOM content of the soil, and at the same time determine the LOM content and ROM content. Calculate the LOM / SOM ratio and ROM / SOM ratio as characteristic parameters of organic matter activity.

[0011] S4: Based on microbial diversity parameters, organic matter content and activity parameters, redundant indicators and outliers are eliminated, and a coupled evaluation index system is constructed. This system includes microbial dimension indicators and organic matter dimension indicators.

[0012] S5: The comprehensive weight of each indicator in the coupled evaluation index system is determined by combining the analytic hierarchy process (AHP) with the entropy weight method. The AHP determines the subjective weight, and the entropy weight method determines the objective weight. The comprehensive weight is obtained by weighted summation. The standardized index values ​​are obtained by standardizing the detection data of each indicator.

[0013] S6: Based on comprehensive weights and standardized index values, construct a coupled evaluation model of microbial diversity and organic matter content, calculate the soil quality comprehensive evaluation value (SQI), determine the soil grade based on the SQI value, and complete the soil quality evaluation.

[0014] Preferably, the specific process of filtering, deduplication, OTU clustering, and species annotation of the raw microbial sequencing data in step S2 is as follows:

[0015] S21: Remove residual fragments of sequencing adapter and primer sequences; remove sequences with a base quality value of Q < 20 exceeding 5%; remove sequences shorter than 150 bp and sequences containing N bases; perform pairing and splicing on paired-end sequences, retaining effective sequences with a splicing success rate ≥ 90% and a spliced ​​length of 250-450 bp.

[0016] S22: Perform deduplication on the filtered valid sequences. Based on the principle of 100% sequence similarity, merge duplicate sequences and count the abundance of each unique sequence; remove low-abundance unique sequences with an abundance of less than 3 to obtain the deduplicated sequence set.

[0017] S23: Based on the deduplicated sequences, OTU clustering is performed according to 97% sequence similarity to generate representative OTU sequences; the representative OTU sequences are then filtered a second time to remove chimeric sequences, and finally a high-quality OTU dataset and OTU abundance matrix are obtained.

[0018] S24: Align and annotate the OTU representative sequences with microbial reference databases: bacterial sequences are aligned with the Silva database, and fungal sequences are aligned with the UNITE database, with the annotation threshold set to similarity ≥97%; based on the annotation results, generate a species taxonomy tree and species abundance table for each sample.

[0019] Preferably, the specific process for calculating the microbial Alpha diversity index and Beta diversity characteristic parameters in step S2 is as follows:

[0020] S25: Alpha diversity index calculation: Based on the OTU abundance matrix, calculate the Shannon index, Simpson index, and Chao1 index;

[0021] S26: Calculation of Beta diversity characteristic parameters: Based on the OTU abundance matrix, the quantitative calculation of Beta diversity characteristic parameters is completed through a four-step process: difference coefficient calculation, distance matrix construction, principal coordinate analysis, and feature parameter extraction.

[0022] Preferably, the specific process of step S3 is as follows:

[0023] S31: The content of easily oxidizable organic matter, i.e., SOM content, was determined by potassium dichromate oxidation-external heating method.

[0024] S32: The content of easily oxidizable organic matter, i.e., LOM content, was determined by potassium permanganate oxidation method;

[0025] S33: The content of recalcitrant organic matter, i.e., ROM content, is calculated using the difference method;

[0026] S34: Based on the SOM content, LOM content, and ROM content, calculate the LOM / SOM ratio and ROM / SOM ratio respectively, as core parameters characterizing soil organic matter activity.

[0027] Preferably, the specific process of step S4 is as follows:

[0028] S41: Integrate all the detection / calculation parameters from steps S2 and S3 into a basic index dataset for soil quality assessment, denoted as... Where m is the total number of soil samples to be evaluated, and n is the total number of initial indicators. x pq For the first p The first sample q The original measured or calculated value of the indicator. p= 1,2,..., m ; q =1,2,..., n ;

[0029] S42: Use the Z-test to independently detect outliers for each indicator in the basic indicator dataset, and determine and remove outliers through quantitative calculation.

[0030] S43: Pearson correlation analysis is used to test the correlation between indicators in the dataset of indicators without outliers, quantitatively determine the degree of redundancy between indicators, and remove highly redundant indicators.

[0031] S44: Integrate core indicators from the microbial and organic matter dimensions to construct a coupled evaluation index system for microbial diversity and organic matter content. This system has a two-dimensional, six-indicator structure, with no anomalies or redundancies. All index values ​​of this system are compiled into the final evaluation index dataset. , where 6 represents the final number of indicators.

[0032] Preferably, the specific process of step S42 is as follows:

[0033] S421: Calculate the... q The mean of all sample data for the item index μ q with standard deviation σ q ;

[0034] S422: Calculate the... p The first sample q Item index value Z value Z pq ;

[0035] S423: Set the Z-test threshold to 3. Z pqIf the value is greater than 3, the data is considered an outlier and will be removed; if... Z pq If the value is ≤3, it is considered normal data and will be retained for subsequent analysis;

[0036] S424: Recalculate the mean and standard deviation of the indicator dataset after removing outliers, and repeat the above steps until there are no outliers in the dataset, thus obtaining an outlier-free indicator dataset. ,in m 1 represents the number of valid samples after removing outliers.

[0037] Preferably, the specific process of step S43 is as follows:

[0038] S431: Calculate any two indicators q 1. q Pearson correlation coefficient between 2 r q1q2 Set the high correlation threshold to | r q1q2 If both indicators meet the condition of ≥0.75, they are determined to be highly redundant indicators and enter the indicator screening stage.

[0039] S432: For two highly redundant indicators, soil quality sensitivity analysis is used to determine the retention item, that is, to calculate the comprehensive correlation coefficient between the indicator and the basic physical and chemical properties of the soil, retain the indicator with the larger absolute value of the comprehensive correlation coefficient, and eliminate the other redundant indicator.

[0040] S433: Dimensional Redundancy Removal Execution:

[0041] Microbial dimension: Initial indicators were the Shannon index, Simpson index, Chao1 index, and PCoA first axis score (PC1). Pearson correlation analysis was performed, and the correlation coefficient between the Chao1 index and the Shannon index was [not specified]. r |≥0.75 indicates a highly redundant index; combined with sensitivity analysis, the Shannon index is more sensitive to soil quality, so the Chao1 index is removed, and three indicators are retained, including the Shannon index, the Simpson index, and the PCoA first axis score PC1.

[0042] Organic matter dimension: The initial indicators were SOM content, LOM / SOM ratio, and ROM / SOM ratio. Pearson correlation analysis showed that the LOM / SOM ratio and ROM / SOM ratio satisfy LOM / SOM+ROM / SOM=1, indicating a complete negative correlation. However, they represent organic matter activity and organic matter stability, respectively, and are different dimensions of soil quality assessment. Since there is no information overlap, both were retained. Finally, the three indicators of SOM content, LOM / SOM ratio, and ROM / SOM ratio were retained.

[0043] Preferably, the specific process of step S5 is as follows:

[0044] S51: The comprehensive weight is determined by a weighted fusion of subjective weight and objective weight;

[0045] S52: The original values ​​of the indicators in the final dataset X2 are standardized using the range standardization method to obtain standardized indicator values. :

[0046] Positive indicator: The higher the indicator value, the better the soil quality. Standardized formula:

[0047] ;

[0048] Negative index: The higher the index value, the worse the soil quality. Standardized formula:

[0049] ;

[0050] Neutral indicator: No clear positive or negative value; needs to be considered in conjunction with the significance of community differences. Standardized formula:

[0051] ;

[0052] In the formula: X i For the first i The original value of the indicator, X' i For standardized indicator values, X' i ∈[0,1], X i,max , X i,min The first i The maximum and minimum values ​​of the indicator. For the first i The mean of each indicator.

[0053] Preferably, the specific process of step S51 is as follows:

[0054] S511: Analytic Hierarchy Process (AHP) for Determining Subjective Weights Based on the scientific logic of soil quality assessment, a hierarchical structure is constructed. Subjective weights are determined through expert scoring and consistency checks. This includes constructing the hierarchical structure, building a judgment matrix, hierarchical single ranking, and weight calculation. Finally, the subjective weights of each indicator are obtained through weight synthesis. ;

[0055] S512: Determining Objective Weights Using the Entropy Weight Method Based on the final dataset, the objective weights of each indicator are calculated using information entropy, including: data normalization and calculation of indicator information entropy. e i Calculate objective weights ;

[0056] S513: Use a weighted summation method to combine subjective and objective weights to obtain a comprehensive weight. Coupled calculations are performed, and the results are obtained.

[0057] Preferably, the specific process of step S6 is as follows:

[0058] S61: Constructing a coupled evaluation model based on the weighted summation method: This model can achieve synergistic characterization of microbial and organic matter dimensions, taking into account the differences in the contribution of each indicator;

[0059] S62: Calculate SQI value: Call the comprehensive weight and standardized index value output from step S5, ensuring that the data corresponds one-to-one; calculate item by item according to the above formula. W i ×X' i The product of these six indicators yields the contribution of each indicator to soil quality. The sum of these contributions yields the SQI value for each soil sample to be evaluated, which is then organized into a set of SQI values ​​{SQI1, SQI2, ..., SQI...}. m1};

[0060] S63: Preset five-level soil quality grading standards, including: high-quality soil, good soil, medium soil, poor soil, and inferior soil, as well as the corresponding SQI value range. Soil quality grade is determined based on SQI value.

[0061] The beneficial effects of this invention include:

[0062] 1. Achieving deep coupling of microbial and organic matter dual core indicators for a more comprehensive evaluation: Breaking through the limitations of traditional single-dimensional evaluation methods, this approach uses microbial diversity indicators (Alpha diversity index, Beta diversity characteristic parameters) and organic matter indicators (total amount, ratio of active components) as core evaluation dimensions. Through scientific screening and coupled modeling, the two are synergistically characterized. Microbial indicators accurately reflect soil community structure, diversity, and inter-community differences, while organic matter indicators take into account both total amount and active component distribution. The combination of the two comprehensively covers soil biological functions, nutrient reserves, and stability characteristics, solving the problems of existing methods neglecting biological indicators or fragmented indicator superposition. The evaluation results are more consistent with the actual soil quality.

[0063] 2. A rigorous and standardized indicator system enhances the reliability of evaluation results: A coupled evaluation indicator system is constructed through a two-step process of outlier removal and redundant indicator screening. First, the Z-test is used to accurately remove outlier data, avoiding interference from detection errors in the evaluation results. Then, Pearson correlation analysis combined with sensitivity analysis is used to remove highly redundant indicators, retaining core indicators that are more sensitive to soil quality. This results in a standardized system of six indicators in two dimensions, ensuring that the indicators are free of redundancy and outliers, and strongly correlated with soil quality characteristics. Simultaneously, for organic matter indicators, easily oxidized and difficult-to-oxidize components are distinguished and their activity ratios are calculated to accurately reflect the dynamic impact of organic matter on soil quality, further enhancing the scientific rigor and relevance of the indicator system.

[0064] 3. Reasonable weight allocation and standardization to avoid biased evaluation: The model employs a weight determination method that couples the Analytic Hierarchy Process (AHP) and the entropy weight method. This approach integrates expert experience through the AHP method to ensure the weights align with the soil quality evaluation logic (subjective relevance), while the entropy weight method assigns weights based on measured data, reflecting the contribution of indicator dispersion to the evaluation (objectivity and impartiality), effectively avoiding the bias inherent in single-weight methods. Furthermore, the range standardization method is used to design differentiated formulas for different types of indicators (positive, negative, and neutral), eliminating the influence of dimensional differences and numerical ranges. This allows for direct coupling and calculation of dimensionless microbial indicators and dimensional organic matter indicators, ensuring the rigor of the model construction and the accuracy of the evaluation results.

[0065] 4. Precise evaluation results provide a basis for targeted improvement: The constructed coupled evaluation model can quantitatively calculate the Soil Quality Index (SQI) and clarify the soil quality level through a five-level grading standard. Furthermore, by combining the contribution of indicators, it can accurately pinpoint soil quality deficiencies, such as insufficient microbial diversity and imbalanced organic matter activity. Compared to existing methods, the evaluation results of this invention not only reflect the overall level of soil quality but also provide targeted guidance for specific improvement measures, such as the application of microbial agents and the directed input of organic materials. This achieves a closed-loop support system of evaluation and improvement, which has significant practical implications for improving soil quality, promoting sustainable agricultural development, and restoring ecosystems. Attached Figure Description

[0066] Figure 1 This is a schematic diagram of the process for the soil quality evaluation method based on the coupling of microbial diversity and organic matter content of the present invention.

[0067] Figure 2 This is a schematic diagram of the preprocessing process for raw microbial sequencing data according to the present invention.

[0068] Figure 3 This is a schematic diagram of the process for detecting soil organic matter content according to the present invention. Detailed Implementation

[0069] The following is in conjunction with the appendix Figures 1-3 The present invention will be further described in detail below:

[0070] Example 1

[0071] See appendix Figure 1 As shown, the soil quality assessment method based on the coupling of microbial diversity and organic matter content is characterized by the following steps:

[0072] S1: Soil sample collection and pretreatment: Select the area to be evaluated, collect soil samples according to the grid method, remove impurities such as gravel and plant residues from the samples, freeze-dry, grind and sieve, and prepare microbial analysis samples and organic matter detection samples respectively. The microbial analysis samples are stored in a sealed container at -80℃, and the organic matter detection samples are stored in a desiccator at room temperature.

[0073] S2: Soil Microbial Diversity Detection: Microbial samples were collected, and total DNA was extracted from the soil using the CTAB method. The purity and integrity of the DNA were verified. After successful verification, PCR amplification was performed targeting the V3-V4 region of the bacterial 16S rRNA gene and the ITS1 region of the fungus. The amplified products were purified, library constructed, and then subjected to high-throughput sequencing to obtain raw microbial sequencing data. The raw microbial sequencing data were filtered, deduplicated, OTU clustered, and species annotated. Microbial Alpha diversity index and Beta diversity characteristic parameters were calculated. The Alpha diversity index included the Shannon index, Simpson index, and Chao1 index. The Beta diversity characteristic parameters were obtained using principal coordinate analysis (PCoA) scores.

[0074] S3: Soil organic matter content detection: Take the organic matter test sample prepared in step S1, and use the potassium dichromate oxidation-external heating method to determine the total organic matter (SOM) content of the soil. At the same time, determine the content of easily oxidizable organic matter (LOM) and the content of difficult-to-oxidize organic matter (ROM), and calculate the LOM / SOM ratio and ROM / SOM ratio as characteristic parameters of organic matter activity.

[0075] S4: Construction of Coupled Evaluation Index System: Based on the microbial diversity parameters obtained in step S2 and the organic matter content and activity parameters obtained in step S3, redundant indicators and outliers are removed to construct a coupled evaluation index system. This coupled evaluation index system includes microbial dimension indicators and organic matter dimension indicators. The microbial dimension indicators selected are the Shannon index, Simpson index, and PCoA first axis score. The organic matter dimension indicators selected are SOM content, LOM / SOM ratio, and ROM / SOM ratio.

[0076] S5: Determining the weight of indicators: The comprehensive weight of each indicator in the coupled evaluation indicator system constructed in step S4 is determined by using the analytic hierarchy process (AHP) combined with the entropy weight method. The AHP determines the subjective weight, while the entropy weight method determines the objective weight based on the detection data in steps S2 and S3. The comprehensive weight is obtained by weighted summation. The detection data of each indicator are standardized to eliminate dimensional differences and obtain standardized indicator values.

[0077] S6: Construction of Coupled Evaluation Model and Soil Quality Grading: Based on the comprehensive weight and standardized index values ​​obtained in step S5, construct a coupled evaluation model of microbial diversity and organic matter content, and calculate the comprehensive soil quality evaluation value (SQI). According to the preset soil quality grading standard, divide it into five levels: excellent, good, medium, poor, and inferior. Obtain the soil quality grade to be evaluated based on the SQI value, and complete the soil quality evaluation.

[0078] In this embodiment, see Figure 2The specific process of filtering, deduplication, OTU clustering, and species annotation of the raw microbial sequencing data in step S2 is as follows:

[0079] S21: Data Filtering: First, remove residual fragments of sequencing adapter sequences and primer sequences; then, remove sequences with a base quality value Q < 20 that account for more than 5% of the total; next, remove sequences shorter than 150 bp and sequences containing N bases (unknown bases); finally, pair and splice the paired-end sequences, retaining effective sequences with a splicing success rate ≥ 90% and a spliced ​​length of 250-450 bp (bacterial 16S rRNA gene V3-V4 region) or 200-300 bp (fungal ITS1 region).

[0080] S22: Sequence Deduplication: The filtered valid sequences are deduplicated using USEARCH software based on a 100% sequence similarity principle. Repeated sequences are merged, and the abundance of each unique sequence is calculated. Low-abundance unique sequences with an abundance below 3 are removed to avoid interference from sequencing errors in subsequent analyses, resulting in a deduplicated sequence set.

[0081] S23: OTU Clustering: Based on the deduplicated sequences, UPARSE software was used to cluster OTUs according to a 97% sequence similarity, generating representative OTU sequences. These representative OTU sequences underwent secondary filtering, and UCHIME software, combined with the Gold database, was used for verification and removal of chimeric sequences. Finally, a high-quality OTU dataset and an OTU abundance matrix were obtained. The matrix rows represent OTU numbers, the columns represent sample numbers, and the values ​​represent the relative abundance of the corresponding OTU in the sample.

[0082] S24: Align and annotate the OTU representative sequences with microbial reference databases: bacterial sequences are aligned with the Silva database, and fungal sequences are aligned with the UNITE database, with the annotation threshold set to similarity ≥97%; based on the annotation results, generate species taxonomy trees and species abundance tables for each sample, covering six taxonomic levels: phylum, class, order, family, genus, and species, to clarify the composition and distribution characteristics of microbial species in the samples.

[0083] The specific process for calculating the microbial Alpha diversity index and Beta diversity characteristic parameters in step S2 is as follows:

[0084] S25: Alpha Diversity Index Calculation:

[0085] Based on the OTU abundance matrix, the Shannon index, Simpson index, and Chao1 index were calculated using QIIME software. The formulas and meanings of each index are as follows:

[0086] (1) Shannon index, which reflects species richness and evenness; the higher the value, the higher the diversity:

[0087] ;

[0088] Where: H is the Shannon index; S is the total number of OTUs in the sample; P i For the first i The relative abundance of the first OTU, i.e., the first i Number of sequences in each OTU / Total number of sequences in the sample; ln is the natural logarithm.

[0089] (2) Simpson index, which reflects species dominance. The closer the value is to 0, the higher the diversity; the closer the value is to 1, the more dominant the species.

[0090] ;

[0091] Where: D is the Simpson index; S is the total number of OTUs in the sample; P i For the first i The relative abundance of each OTU;

[0092] (3) Chao1 index, used to estimate species richness; the higher the value, the more species are estimated.

[0093] ;

[0094] In the formula: Chao1 represents the estimated species richness; S obs F1 represents the total number of OTUs actually observed; F2 represents the number of OTUs that appeared only once (single OTUs); and F3 represents the number of OTUs that appeared only twice (double OTUs). This formula improves the accuracy of estimation by correcting for the impact of rare OTUs on species richness and is suitable for species richness assessment of high-throughput sequencing data.

[0095] S26: Calculation of Beta diversity characteristic parameters, reflecting differences between communities:

[0096] Based on the high-quality OTU abundance matrix obtained in step S3, the quantitative calculation of Beta diversity characteristic parameters is completed through a four-step process: difference coefficient calculation, distance matrix construction, principal coordinate analysis, and feature parameter extraction. The specific process and formulas are as follows:

[0097] (1) Bray-Curtis distance coefficient calculation: The Bray-Curtis distance algorithm is used to quantitatively characterize the degree of difference in microbial communities between any two samples. This algorithm is sensitive to changes in community species abundance and can effectively avoid interference from rare species in high-throughput sequencing data. It is suitable for soil microbial community difference analysis. The calculation formula is as follows:

[0098] ;

[0099] In the formula: B ij For the sample i With the sample j The Bray-Curtis distance coefficient between the two samples ranges from [0,1]; B=0 indicates that the microbial community structure of the two samples is completely identical, and the closer B is to 1, the greater the difference in community structure; S is the total number of OTUs. x ik For the first k One OTU in the sample i The relative abundance in; x jk For the first k One OTU in the sample j The relative abundance in.

[0100] (2) Construction of the inter-sample distance matrix:

[0101] Based on the above formula, the Bray-Curtis distance coefficients between all pairs of samples within the evaluation area are calculated, and a system is constructed. m×m Inter-sample distance matrix, m The total number of soil samples to be evaluated is represented by the matrix. The diagonal elements are 0, indicating that the samples are not different from each other. The off-diagonal elements are the Bray-Curtis distance coefficients between two corresponding samples, providing a data basis for subsequent principal coordinate analysis.

[0102] (3) Principal Coordinate Analysis (PCoA):

[0103] Using the constructed inter-sample distance matrix as input data, the `cmdscale` function from the `vegan` package in R is employed for principal coordinate analysis. This function projects sample distances from a multi-dimensional space to a two- or three-dimensional low-dimensional space through linear transformation, while maximizing the preservation of community difference information in the original data. During the analysis, each principal coordinate axis is extracted through eigenvalue decomposition. Each principal coordinate axis corresponds to an eigenvalue, and the magnitude of the eigenvalue represents the explanatory power of that coordinate axis for community differences; the larger the eigenvalue, the higher the explanatory power.

[0104] (4) Extraction of Beta diversity feature parameters:

[0105] Based on the PCoA analysis results, the scores of the first principal axis (PC1) and the second principal axis (PC2) were extracted as core characteristic parameters of Beta diversity. PC1, with its largest eigenvalue, explains the largest proportion of differences in microbial communities among samples, typically with an explanatory power ≥30%, and most directly and fundamentally reflects the degree of difference in microbial community structure among different soil samples. PC2, as an auxiliary parameter, can supplement the explanation of the remaining community differences. Considering the needs of soil quality assessment, the PC1 score was prioritized for inclusion in the subsequent coupled evaluation index system to achieve accurate characterization of differences between communities.

[0106] Example 2

[0107] Based on Example 1, see Figure 3 The specific process of step S3 is as follows:

[0108] S31: Determination of total organic matter (SOM) content by potassium dichromate oxidation-external heating method:

[0109] The content of SOM was determined by potassium dichromate oxidation-external heating method. This method has stable oxidation efficiency and good reproducibility, and is suitable for quantitative analysis of organic matter in various soils. The specific operation is as follows:

[0110] (1) Oxidation reaction:

[0111] Accurately add 10.00 mL of 0.8000 mol / L potassium dichromate standard solution to the pretreated soil sample tube, then slowly add 20 mL of concentrated sulfuric acid (analytical grade, density 1.84). g / cm 3 After gently shaking, place the mixture in a wire cage and put it into an oil bath preheated to 170-180℃. Keep the oil bath temperature stable at 175±5℃ and heat to boiling for 5 minutes. Start timing from when the solution in the test tube boils. Then remove the wire cage and let the test tube cool naturally to room temperature.

[0112] (2) Titration reaction:

[0113] Transfer the cooled solution from the test tube to a 250 mL Erlenmeyer flask. Wash the inner wall of the test tube 3-4 times with distilled water, transferring the washings into the Erlenmeyer flask to ensure no residue remains. Add 3-4 drops of o-phenanthroline indicator to the Erlenmeyer flask and titrate with 0.2000 mol / L ferrous sulfate standard solution until the solution color changes from orange-red to blue-green and then to brick-red. Record the volume of ferrous sulfate standard solution consumed as V1 (mL). Simultaneously, titrate the blank control group using the same procedure and record the volume of ferrous sulfate standard solution consumed as V0 (mL).

[0114] The formula for calculating SOM content is as follows:

[0115] ;

[0116] In the formula: SOM is the total organic matter content of soil, expressed as a mass fraction (%). c V0 represents the concentration of the ferrous sulfate standard solution, in mol / L; V1 represents the volume of ferrous sulfate standard solution consumed by the blank control group, in mL; V2 represents the volume of ferrous sulfate standard solution consumed by the soil sample group, in mL. M The molar mass of carbon is taken as 12.01 g / mol; k As the oxidation correction factor, potassium dichromate oxidizes approximately 77% of soil organic matter. k =1.30, 1 / 0.77≈1.30; m The dry weight of the soil sample is in grams; 10 -3 1000 is the volume conversion factor, mL to L; 1000 is the mass conversion factor, g to kg. The final result is converted to mass fraction, in units of .

[0117] S32: Determination of LOM (Less Organic Matter) content by potassium permanganate oxidation method:

[0118] LOM content was determined using the 333 mmol / L potassium permanganate oxidation method. This method can specifically oxidize active organic matter components in soil that are easily decomposed and utilized by microorganisms. It is simple to operate and highly targeted. The specific operation and formula are as follows:

[0119] (1) Oxidation reaction:

[0120] Take another 1.000g soil sample, accurate to 0.0001g, and place it in a 250mL Erlenmeyer flask. Add 100.00mL of 333mmol / L potassium permanganate standard solution, prepared with 0.5mol / L sulfuric acid solution to maintain an acidic environment in the reaction system. After sealing the Erlenmeyer flask, place it in a constant temperature shaker at 25±1℃ and shake at 150r / min for 30min. Then let it stand for 10min to allow the soil particles to precipitate.

[0121] (2) Titration of the remaining potassium permanganate:

[0122] Take 25.00 mL of the supernatant after settling and transfer it to another 250 mL Erlenmeyer flask. Add 5 mL of 8 mol / L sulfuric acid solution, heat to 70-80℃, and then add 3-4 drops of o-phenylaminobenzoic acid indicator. Titrate with 0.1000 mol / L ferrous ammonium sulfate standard solution until the solution color changes from purple-red to bright green. Record the volume of ferrous ammonium sulfate standard solution consumed as V2 (mL). Simultaneously set up a blank control group, using quartz sand instead of soil sample, and titrate using the same procedure. Record the blank consumption volume V0' (mL).

[0123] The formula for calculating LOM content is as follows:

[0124] ;

[0125] In the formula: LOM is the content of easily oxidized organic matter in the soil, in mg / kg; c' V0 represents the concentration of the ferrous ammonium sulfate standard solution, in mol / L. ' V1 represents the volume of ferrous ammonium sulfate standard solution consumed by the blank control group, in mL; V2 represents the volume of ferrous ammonium sulfate standard solution consumed by the soil sample group, in mL; M represents the molar mass of carbon, taken as 12.01 g / mol. f Given the oxidation equivalence coefficient, the redox equivalence ratio of potassium permanganate to carbon in organic matter is 3:4. f =4 / 3; m 'This represents the dry weight of the soil sample, in grams; 25 / 100 is the sampling ratio of the supernatant, only 25 mL of supernatant was used for titration, the original system was 100 mL; 10 -3 This is a volume unit conversion factor, converting mL to L, 10 6 Here are conversion factors for mass units: g to mg, kg to g.

[0126] S33: Calculation of ROM content of recalcitrant organic matter using the difference method:

[0127] The recalcitrant organic matter in soil is the difference between total organic matter and readily oxidizable organic matter. It does not need to be measured separately; it can be calculated using the following formula to ensure data consistency:

[0128] ;

[0129] In the formula: ROM is the content of soil recalcitrant organic matter, in mg / kg; To convert the total organic matter content (%) to mg / kg, the conversion formula is as follows: ρ is the soil bulk density, taken as 1.2 g / cm³, which is the default value for conventional soils and can be adjusted according to the actual measured value; LOM is the content of easily oxidized organic matter determined in the above steps, in mg / kg.

[0130] S34: Calculation of Organic Matter Activity Characteristic Parameters: Based on the above measurement and calculation results, the LOM / SOM ratio and ROM / SOM ratio are calculated respectively as core parameters characterizing soil organic matter activity. The calculation formulas are as follows:

[0131] (1) The LOM / SOM ratio reflects the degree of organic matter activity. The higher the ratio, the higher the proportion of active organic matter and the stronger the soil's nutrient supply capacity.

[0132] ;

[0133] (2) The ROM / SOM ratio reflects the stability of organic matter. The larger the ratio, the higher the proportion of recalcitrant organic matter and the stronger the stability of the soil carbon pool.

[0134] ;

[0135] Both ratios are in the range of [0,1], and LOM / SOM+ROM / SOM=1.

[0136] Example 3

[0137] Based on Example 1 or Example 2, the specific process of step S4 is as follows:

[0138] S41: Integrate all the detection / calculation parameters from steps S2 and S3 into a basic index dataset for soil quality assessment, denoted as... ,in, m The total number of soil samples to be evaluated. n The initial total number of indicators includes 7 items: Shannon index, Simpson index, Chao1 index, PCoA first axis score (PC1), SOM content, LOM / SOM ratio, and ROM / SOM ratio. x pq For the first p The first sample q The original detection / calculated values ​​of the indicators, p= 1,2,..., m ; q =1,2,..., n .

[0139] S42: The Z-test method is used to independently detect outliers for each indicator in the basic indicator dataset. Outliers are identified and removed through quantitative calculation to ensure the normality and reliability of the indicator data.

[0140] S43: Pearson correlation analysis was used to test the correlation between indicators in the dataset without outliers, quantitatively determine the degree of redundancy between indicators, remove highly redundant indicators, retain core indicators that are more sensitive to soil quality, and avoid the bias of subsequent evaluation results caused by overlapping indicator information.

[0141] S44: Integrating core indicators from the microbial and organic matter dimensions, a coupled evaluation index system of microbial diversity and organic matter content is constructed. This system has a two-dimensional, six-indicator structure, with no anomalies or redundancies. The indicators have a strong correlation with soil quality characteristics and provide accurate characterization. See Table 1 below for the specific composition:

[0142] Table 1. Evaluation Index System of Microbial Diversity-Organic Matter Content Coupling

[0143] Indicator Dimensions Key Indicators Indicator Representation Meaning Microbial dimension Shannon Index Microbial community richness and evenness Microbial dimension Simpson index Microbial community dominance and diversity Microbial dimension PCoA First Axis Score (PC1) Core characteristics of structural differences among microbial communities Organic matter dimension SOM content Total soil organic matter reserves Organic matter dimension LOM / SOM ratio Soil organic matter activity level Organic matter dimension ROM / SOM ratio Soil organic matter stability and carbon pool retention capacity

[0144] All indicator values ​​of this indicator system are compiled into the final evaluation indicator dataset. 6 represents the final number of indicators. m 1 represents the number of valid samples after removing outliers. This dataset is directly used as input data for the indicator weight determination and standardization process in step S5, achieving seamless integration with subsequent steps.

[0145] In this embodiment, the specific process of step S42 is as follows:

[0146] S421: Calculate the mean of all sample data for the q-th index. μ q with standard deviation σ q The formula is:

[0147] ;

[0148] ;

[0149] in: μ q For the first q The mean of the indicators, σ q For the first q The standard deviation of the indicator x pq For the first p The first sample q Item index value, m This represents the total number of samples.

[0150] S422: Calculate the... p The first sample q Item index value Z value Z pq The formula is:

[0151] ;

[0152] in: Z pq For the first p The first sample q Item Indicator Z The statistic takes non-negative values.

[0153] S423: Outlier Identification and Removal: Set the Z-test threshold to 3 (generally statistically significant level). Z pq If the value is greater than 3, the data is considered an outlier and will be removed; if... Zpq If the value is ≤3, it is considered normal data and will be retained for subsequent analysis.

[0154] S424: Data Update: Recalculate the mean and standard deviation of the indicator dataset after removing outliers, and repeat the above steps until there are no outliers in the dataset, thus obtaining an outlier-free indicator dataset. ,in m 1 represents the number of valid samples after removing outliers.

[0155] The specific process of step S43 is as follows:

[0156] S431: Calculate any two indicators q 1. q Pearson correlation coefficient between 2 r q1q2 The formula is:

[0157] ;

[0158] in, r q1q2 For the first q Item 1 and the first q The Pearson correlation coefficient between the two indicators ranges from [−1, 1].

[0159] | r q1q2 The closer the correlation is to 1, the stronger the linear correlation between the two indicators; the closer it is to 0, the weaker the correlation.

[0160] x′ pq1 , x′ pq2 These are the first in the valid dataset. p The first sample q 1. q 2 indicator values;

[0161] μ q1 , μ q2 The first q 1. q The average of the two indicators;

[0162] m 1 represents the number of valid samples;

[0163] Set the height correlation threshold to | r q1q2 If both indicators meet the condition of ≥0.75, they are determined to be highly redundant indicators and enter the indicator screening stage.

[0164] S432: For the two highly redundant indicators, the retention item is determined by soil quality sensitivity analysis, that is, the comprehensive correlation coefficient between the indicator and the basic physicochemical properties of the soil is calculated. The basic physicochemical properties of the soil include pH, total nitrogen, available phosphorus and available potassium. The indicator with the larger absolute value of the comprehensive correlation coefficient is retained. This indicator is more sensitive to changes in soil quality and has a better characterization effect. The other redundant indicator is removed.

[0165] S433: Dimensional Redundancy Removal Execution:

[0166] Microbial dimension: Initial indicators were the Shannon index, Simpson index, Chao1 index, and PCoA first axis score (PC1). Pearson correlation analysis was performed, and the correlation coefficient between the Chao1 index and the Shannon index was [not specified]. r |≥0.75 indicates a highly redundant index; combined with sensitivity analysis, the Shannon index is more sensitive to soil quality, so the Chao1 index is removed, and three indicators are retained, including the Shannon index, the Simpson index, and the PCoA first axis score PC1.

[0167] Organic matter dimension: The initial indicators were SOM content, LOM / SOM ratio, and ROM / SOM ratio. Pearson correlation analysis showed that the LOM / SOM ratio and ROM / SOM ratio satisfy LOM / SOM+ROM / SOM=1, indicating a complete negative correlation. However, they represent organic matter activity and organic matter stability, respectively, and are different dimensions of soil quality assessment. Since there is no information overlap, both were retained. Finally, the three indicators of SOM content, LOM / SOM ratio, and ROM / SOM ratio were retained.

[0168] Example 4

[0169] Based on Example 1, Example 2, or Example 3, the specific process of step S5 is as follows:

[0170] S51: The comprehensive weight is determined by a combination of subjective and objective weights. This approach not only reflects the specificity of expert experience in soil quality assessment but also ensures the objectivity of the weights based on measured data, thus avoiding the one-sidedness of a single weight method.

[0171] S52: The original values ​​of the indicators in the final dataset X2 from step 4 are standardized using the range standardization method to eliminate differences in units and numerical ranges, allowing each indicator to directly participate in the coupled model calculation, thus obtaining standardized indicator values. :

[0172] Positive indicators:

[0173] The higher the index value, the better the soil quality. These indicators include the Shannon index, SOM content, and LOM / SOM ratio. The standardized formula is:

[0174] ;

[0175] Negative indicators:

[0176] The higher the index value, the worse the soil quality. This includes the Simpson index; the closer the value is to 1, the more dominant the species and the lower the diversity. Standardized formula:

[0177] ;

[0178] Neutral indicator: No clear positive or negative value; needs to be considered in conjunction with the significance of community differences, including the PCoA first axis score, reflecting differences in community structure, standardized using the mean as the benchmark. Formula:

[0179] ;

[0180] In the formula: For the first i The original value of the indicator, For standardized indicator values, ∈[0,1], , Let be the maximum and minimum values ​​of the i-th indicator, respectively. Let be the mean of the i-th indicator.

[0181] In this embodiment, the specific process of step S51 is as follows:

[0182] S511: Analytic Hierarchy Process (AHP) for Determining Subjective Weights :

[0183] Based on the scientific logic of soil quality assessment, a hierarchical structure is constructed, and subjective weights are determined through expert scoring and consistency checks. The steps are as follows:

[0184] (1) Constructing a hierarchical structure:

[0185] Establish a three-level hierarchical structure: target layer (comprehensive evaluation of soil quality) → criterion layer (microbial dimension, organic matter dimension) → indicator layer (6 core indicators: Shannon index, Simpson index, PCoA first axis score, SOM content, LOM / SOM ratio, ROM / SOM ratio).

[0186] (2) Construct the judgment matrix:

[0187] Invite 5-7 experts in soil ecology to score the relative importance of indicators under two dimensions within the criterion layer and each dimension within the indicator layer using a 1-9 scale, where 1 indicates that two indicators are of equal importance, 9 indicates that one indicator is extremely more important than the other, and the intermediate value is a transitional value. Construct a judgment matrix. ,in k The number of indicators at the corresponding level, criterion level k =2, Microbial dimension of the indicator layer k =3. Organic matter dimension k =3, a ij Indicates the first i The first indicator for the first j The importance scale of each indicator, and meets the following requirements. a ij =1 / a ji a ii =1.

[0188] (3) Hierarchical single sorting and weight calculation:

[0189] Perform eigenvalue decomposition on each judgment matrix and calculate the largest eigenvalue λ. max The corresponding feature vectors are then normalized to obtain the weights of the criterion layer and the weights of the indicator layer relative to the criterion layer. Finally, the subjective weights of each indicator are obtained through weight synthesis. The normalization formula is:

[0190] ;

[0191] in: v i The eigenvector components corresponding to the largest eigenvalue. n For the total number of indicators (n=6), Σ W 1i =1.

[0192] S512: Determining Objective Weights Using the Entropy Weight Method :

[0193] Based on the measured data from steps 2 and 3, the final dataset after removing outliers and redundant indicators in step 4. The objective weights of each indicator are calculated using information entropy, reflecting the degree of dispersion of the indicator data. The higher the dispersion, the lower the information entropy, the greater the weight, and the higher the contribution to the evaluation.

[0194] (1) Data preprocessing and normalization:

[0195] First, the original data of the indicators are normalized to eliminate the influence of dimensions, resulting in a normalized matrix. The formula is:

[0196] Positive indicators, with higher values ​​indicating better soil quality, include the Shannon index and SOM content:

[0197] ;

[0198] There are no clearly defined positive or negative indicators; their meaning must be considered, such as the PCoA first axis score.

[0199] ;

[0200] In the formula: For the p-th sample q The original value of the indicator, , The first q The minimum and maximum values ​​of the indicator. For the first q The mean of the indicators;

[0201] Calculate the information entropy of the indicator e i :

[0202] No. i The formula for the information entropy of an indicator is:

[0203] ;

[0204] in: m 1 represents the number of valid samples. y' pi For the normalized first p The first sample i The value of the indicator, if y' pi =0, take y' pi =10 -6 To avoid ln0 being meaningless, e i ∈[0,1], e i The closer it is to 0, the higher the dispersion of the indicator and the greater its information value;

[0205] (3) Calculate the objective weights :

[0206] First calculate the coefficient of difference of the indicators g i =1- e i The larger the difference coefficient, the stronger the indicator's discriminative ability. Further normalization yields the objective weights.

[0207] ;

[0208] S513: Overall Weighting Coupled computation:

[0209] A weighted summation method is used to integrate subjective and objective weights, and the proportion of subjective weights is set. α= 0.4, objective weighting percentage β =0.6. After multiple verifications, this ratio can balance expert experience and data objectivity, and is suitable for soil quality assessment scenarios. The formula is:

[0210] ;

[0211] In the formula: α+β =1, W i For the first i The overall weight of each indicator, Σ W i =1; Adjustable to suit different evaluation scenarios. α、β Values, α ∈[0.3,0.5], β ∈[0.5,0.7].

[0212] In this embodiment, the specific process of step S6 is as follows:

[0213] S61: Constructing a coupled evaluation model based on the weighted summation method: This model can achieve synergistic characterization of microbial and organic matter dimensions, taking into account the differences in the contribution of each indicator. The specific formula is as follows:

[0214] ;

[0215] Among them, SQI is the comprehensive evaluation value of soil quality, with a value range of [0,1]. The closer the SQI value is to 1, the better the soil quality, and vice versa. n To represent the total number of indicators in the coupled evaluation index system, in this method... n =6, including 3 microbial dimension indicators + 3 organic matter dimension indicators; For the first i The comprehensive weight of each indicator is calculated by coupling the AHP method and the entropy weight method in step S5, ΣW i =1; X' i For the first i The standardized values ​​of the indicators are obtained through range standardization in step S5. X' i ∈[0,1];

[0216] S62: Calculate the SQI value: Call the comprehensive weight output from step S5 ( W 1 to W 6) and standardized index values ​​(X' 1 to X ' 6) Ensure that the data corresponds one-to-one. i The weights of each indicator are precisely matched with their standardized values; each indicator is calculated according to the formula above. W i × X' i The product of these six indicators yields the contribution of each indicator to soil quality. The sum of these contributions yields the SQI value for each soil sample to be evaluated, which is then organized into a set of SQI values ​​{SQI1, SQI2, ..., SQI...}. m1}, m 1 represents the number of valid samples;

[0217] S63: Based on regional soil background values ​​and soil function requirements of different land use types (farmland, forest, grassland, etc.), and combined with statistical verification (analysis of variance, multiple comparisons) of a large amount of measured data, a five-level soil quality grading standard is pre-set, including:

[0218] High-quality soil: SQI ≥ 0.8. At this level, the soil microbial community structure is stable and highly diverse, with sufficient organic matter content and a reasonable balance between activity and stability. The soil has excellent fertility retention, nutrient supply, and ecological functions, which can meet the needs of high-standard agricultural production or the maintenance of a healthy natural ecosystem, and there are no obvious quality limiting factors.

[0219] Good soil: 0.6≤SQI<0.8. The soil has high microbial diversity, and the organic matter content can meet the needs of routine production or ecology. The community structure and organic matter cycling are well coordinated. The soil quality has no major shortcomings, only slight fluctuations, and can maintain normal function without deliberate improvement.

[0220] Medium-quality soil: 0.4 ≤ SQI < 0.6. Soil microbial diversity is average, with a slight deficiency in organic matter content or activity. The synergy between community structure and organic matter cycling is average. Potential limiting factors exist in soil quality, such as the absence of some microbial groups and a low proportion of active organic matter. Light improvement measures are needed, such as increasing the application of organic fertilizer and adjusting soil pH, to improve soil quality.

[0221] Poor soil: 0.2≤SQI<0.4. This soil exhibits low microbial diversity, insufficient organic matter content, or imbalanced activity, such as an excessively high or low LOM / SOM ratio, disordered community structure, weak soil fertility retention capacity, and significant quality deficiencies, including over-enrichment of dominant species and an abnormally high proportion of recalcitrant organic matter. Targeted improvements are needed, such as the application of microbial agents and organic matter regulation, to meet production or ecological needs.

[0222] Poor soil quality: SQI < 0.2. The soil has extremely low microbial diversity, lacks organic matter, has impaired microbial community function, and degrades soil ecological function, making it unable to meet normal production or ecological needs. It is prone to problems such as soil desertification and compaction, requiring intensive improvement and long-term restoration, such as vegetation reconstruction and targeted input of organic materials.

Claims

1. A soil quality assessment method based on the coupling of microbial diversity and organic matter content, characterized in that, Includes the following steps: S1: Select the area to be evaluated, collect soil samples using the grid method, remove impurities from the samples, freeze-dry, grind and sieve to prepare microbial analysis samples and organic matter detection samples respectively; S2: Take microbial analysis samples and obtain raw microbial sequencing data through high-throughput sequencing; filter, deduplicate, cluster OTUs and annotate species in the raw microbial sequencing data, and calculate the microbial Alpha diversity index and Beta diversity characteristic parameters. S3: Take organic matter test samples, determine the total SOM content of the soil, and at the same time determine the LOM content and ROM content. Calculate the LOM / SOM ratio and ROM / SOM ratio as characteristic parameters of organic matter activity. S4: Based on microbial diversity parameters, organic matter content and activity parameters, redundant indicators and outliers are eliminated, and a coupled evaluation index system is constructed. This system includes microbial dimension indicators and organic matter dimension indicators. S5: The comprehensive weight of each indicator in the coupled evaluation index system is determined by combining the analytic hierarchy process (AHP) with the entropy weight method. The AHP determines the subjective weight, and the entropy weight method determines the objective weight. The comprehensive weight is obtained by weighted summation. The standardized index values ​​are obtained by standardizing the detection data of each indicator. S6: Based on comprehensive weights and standardized index values, construct a coupled evaluation model of microbial diversity and organic matter content, calculate the comprehensive soil quality evaluation value (SQI), determine the soil grade based on the SQI value, and complete the soil quality evaluation. The specific process of step S6 is as follows: S61: Constructing a coupled evaluation model based on the weighted summation method: This model can achieve synergistic characterization of microbial and organic matter dimensions, taking into account the differences in the contribution of each indicator; S62: Calculate SQI value: Call the comprehensive weight and standardized index value output from step S5 to ensure that the data corresponds one-to-one; calculate item by item. W i ×X' i The product of these factors yields the contribution of each indicator to soil quality. The contribution values ​​of the six indicators are summed to obtain the SQI value for each soil sample to be evaluated, which is then organized into a set of SQI values ​​{SQI1, SQI2, ..., SQI...}. m1 }; in, W i Let be the comprehensive weight of the i-th indicator; X' i For the first i The standardized index values ​​are obtained after standardizing the data of each index; m1 is the number of valid samples after removing outliers. The six indicators include the Shannon index, Simpson index, PCoA first axis score (PC1), total organic matter (SOM) content, LOM / SOM ratio, and ROM / SOM ratio. S63: Preset five-level soil quality grading standards, including: high-quality soil, good soil, medium soil, poor soil, and inferior soil, as well as the corresponding SQI value range. Soil quality grade is determined based on SQI value.

2. The soil quality evaluation method based on the coupling of microbial diversity and organic matter content according to claim 1, characterized in that, The specific process of filtering, deduplication, OTU clustering, and species annotation of the raw microbial sequencing data in step S2 is as follows: S21: Remove residual fragments of sequencing adapter and primer sequences; remove sequences with a base quality value of Q < 20 exceeding 5%; remove sequences shorter than 150 bp and sequences containing N bases; perform pairing and splicing on paired-end sequences, retaining effective sequences with a splicing success rate ≥ 90% and a spliced ​​length of 250-450 bp. S22: Perform deduplication on the filtered valid sequences. Based on the principle of 100% sequence similarity, merge duplicate sequences and count the abundance of each unique sequence; remove low-abundance unique sequences with an abundance of less than 3 to obtain the deduplicated sequence set. S23: Based on the deduplication sequence, perform OTU clustering according to 97% sequence similarity to generate OTU representative sequences; The OTU representative sequences were filtered twice to remove chimeric sequences, and finally a high-quality OTU dataset and OTU abundance matrix were obtained. S24: Align and annotate the OTU representative sequences with microbial reference databases: bacterial sequences are aligned with the Silva database, and fungal sequences are aligned with the UNITE database, with the annotation threshold set to similarity ≥97%; based on the annotation results, generate a species taxonomy tree and species abundance table for each sample.

3. The soil quality evaluation method based on the coupling of microbial diversity and organic matter content according to claim 2, characterized in that, The specific process for calculating the microbial Alpha diversity index and Beta diversity characteristic parameters in step S2 is as follows: S25: Alpha diversity index calculation: Based on the OTU abundance matrix, calculate the Shannon index, Simpson index, and Chao1 index; S26: Calculation of Beta diversity characteristic parameters: Based on the OTU abundance matrix, the quantitative calculation of Beta diversity characteristic parameters is completed through a four-step process: difference coefficient calculation, distance matrix construction, principal coordinate analysis, and feature parameter extraction.

4. The soil quality evaluation method based on the coupling of microbial diversity and organic matter content according to claim 1, characterized in that, The specific process of step S3 is as follows: S31: The content of easily oxidizable organic matter, i.e., SOM content, was determined by potassium dichromate oxidation-external heating method. S32: The content of easily oxidizable organic matter, i.e., LOM content, was determined by potassium permanganate oxidation method; S33: The content of recalcitrant organic matter, i.e., ROM content, is calculated using the difference method; S34: Based on the SOM content, LOM content, and ROM content, calculate the LOM / SOM ratio and ROM / SOM ratio respectively, as core parameters characterizing soil organic matter activity.

5. The soil quality evaluation method based on the coupling of microbial diversity and organic matter content according to claim 1, characterized in that, The specific process of step S4 is as follows: S41: Integrate all the detection / calculation parameters from steps S2 and S3 into a basic index dataset for soil quality assessment, denoted as... Where m is the total number of soil samples to be evaluated, and n is the total number of initial indicators. x pq For the first p The first sample q The original measured or calculated value of the indicator. p= 1,2,..., m ; q =1,2,..., n ; S42: Use the Z-test to independently detect outliers for each indicator in the basic indicator dataset, and determine and remove outliers through quantitative calculation. S43: Pearson correlation analysis is used to test the correlation between indicators in the dataset of indicators without outliers, quantitatively determine the degree of redundancy between indicators, and remove highly redundant indicators. S44: Integrate core indicators from the microbial and organic matter dimensions to construct a coupled evaluation index system for microbial diversity and organic matter content. This system has a two-dimensional, six-indicator structure, with no anomalies or redundancies. All index values ​​of this system are compiled into the final evaluation index dataset. , where 6 represents the final number of indicators.

6. The soil quality evaluation method based on the coupling of microbial diversity and organic matter content according to claim 5, characterized in that, The specific process of step S42 is as follows: S421: Calculate the... q The mean of all sample data for the item index μ q with standard deviation σ q ; S422: Calculate the... p The first sample q Item index value Z value Z pq ; S423: Set the Z-test threshold to 3. Z pq If the value is greater than 3, the data is considered an outlier and will be removed; if... Z pq If the value is ≤3, it is considered normal data and will be retained for subsequent analysis; S424: Recalculate the mean and standard deviation of the indicator dataset after removing outliers, and repeat steps S421-S423 until there are no outliers in the dataset, thus obtaining the outlier-free indicator dataset. ,in m 1 represents the number of valid samples after removing outliers.

7. The soil quality evaluation method based on the coupling of microbial diversity and organic matter content according to claim 5, characterized in that, The specific process of step S43 is as follows: S431: Calculate any two indicators q 1. q Pearson correlation coefficient between 2 r q1q2 Set the high correlation threshold to | r q1q2 If both indicators meet the condition of ≥0.75, they are determined to be highly redundant indicators and enter the indicator screening stage. S432: For two highly redundant indicators, soil quality sensitivity analysis is used to determine the retention item, that is, to calculate the comprehensive correlation coefficient between the indicator and the basic physical and chemical properties of the soil, retain the indicator with the larger absolute value of the comprehensive correlation coefficient, and eliminate the other redundant indicator. S433: Dimensional Redundancy Removal Execution: Microbial dimension: Initial indicators were the Shannon index, Simpson index, Chao1 index, and PCoA first axis score (PC1). Pearson correlation analysis was performed, and the correlation coefficient between the Chao1 index and the Shannon index was […]. r |≥0.75 indicates a highly redundant index; combined with sensitivity analysis, the Shannon index is more sensitive to soil quality, so the Chao1 index is removed, and three indicators are retained, including the Shannon index, the Simpson index, and the PCoA first axis score PC1. Organic matter dimension: The initial indicators were SOM content, LOM / SOM ratio, and ROM / SOM ratio. Pearson correlation analysis showed that the LOM / SOM ratio and ROM / SOM ratio satisfy LOM / SOM+ROM / SOM=1, indicating a complete negative correlation. However, they represent organic matter activity and organic matter stability, respectively, and are different dimensions of soil quality assessment. Since there is no information overlap, both were retained. Finally, the three indicators of SOM content, LOM / SOM ratio, and ROM / SOM ratio were retained.

8. The soil quality evaluation method based on the coupling of microbial diversity and organic matter content according to claim 7, characterized in that, The specific process of step S5 is as follows: S51: The comprehensive weight is determined by a weighted fusion of subjective weight and objective weight; S52: The original values ​​of the indicators in the final dataset X2 are standardized using the range standardization method to obtain the standardized indicator values ​​X'. ᵢ .

9. The soil quality evaluation method based on the coupling of microbial diversity and organic matter content according to claim 8, characterized in that, The specific process of step S51 is as follows: S511: Analytic Hierarchy Process (AHP) for Determining Subjective Weights W 1 ᵢ Based on the scientific logic of soil quality assessment, a hierarchical structure is constructed. Subjective weights are determined through expert scoring and consistency checks. This includes constructing the hierarchical structure, building a judgment matrix, hierarchical single ranking, and weight calculation. Finally, the subjective weights of each indicator are obtained through weight synthesis. W 1 ᵢ ; S512: Determining Objective Weights Using the Entropy Weight Method W 2 ᵢ Based on the final dataset, the objective weights of each indicator are calculated using information entropy, including: data normalization and calculation of indicator information entropy. eᵢ Calculate objective weights W 2 ᵢ ; S513: Use a weighted summation method to combine subjective and objective weights to obtain a comprehensive weight. Wᵢ Coupled calculations are performed, and the results are obtained.

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