Soil quality evaluation method and system based on few soil indexes

Abstract

The invention relates to the technical field of soil monitoring, and discloses a soil quality evaluation method and system based on a small amount of soil indexes, and the method comprises the following steps: S1, collection: collecting a plurality of soil samples, respectively determining the soil indexes, and constructing a total data set; s2, principal component analysis: performing principal component analysis and Pearson correlation analysis on each sub-data set, and constructing a minimum data set and a revised minimum data set through screening; s3, calculation: calculating a soil quality score through a weighted summation method; s4, performing linear regression fitting, namely performing linear regression fitting on the soil quality score to obtain a linear fitting model of the soil quality scores of different data sets; and S5, constructing a new data set, and constructing the new data set for replacing the total data set to perform soil quality evaluation according to the determination coefficient R2 of the linear fitting model. Soil quality evaluation is carried out by screening a small number of key soil indexes, evaluation accuracy is guaranteed, data collection and processing steps are simplified, and the soil quality evaluation process is more efficient and convenient.

Description

Technical Field

[0001] The present invention relates to the field of soil monitoring, and in particular to a soil quality evaluation method and system based on a small amount of soil indicators. Background Art

[0002] As a vital component of ecosystems, soil plays a key role in maintaining ecological balance and supporting biodiversity. Assessing soil quality is crucial for the sustainable development of ecosystems. Soil quality directly affects plant growth, ecosystem structure and function, and nutrient cycling and energy flow within the soil. Good soil quality helps increase crop yields, maintain ecosystem stability and resilience, and promote soil biodiversity and microbial activity. However, with increasing population growth, urbanization, and industrialization, soils are being impacted by various pressures, such as chemical pollution, land degradation, biological invasions, and climate change. These pressures have negatively impacted soil quality, exacerbating soil depletion, acidification, and salinization, threatening the health and sustainability of ecosystems. Under the concept of sustainable development, protecting and improving soil quality has become a key measure for maintaining ecological balance and achieving sustainable resource use. Scientific soil quality assessment can help promptly identify soil problems, formulate appropriate soil management measures, and promote the restoration and protection of soil ecosystems. Assessing soil quality is crucial for the sustainable development of ecosystems. It not only affects the maintenance of biodiversity and the rational use of land resources, but also directly impacts human health and sustainable socioeconomic development. Therefore, conducting in-depth research and formulating effective soil quality evaluation methods and management strategies are of great significance for achieving healthy and sustainable development of ecosystems.

[0003] Traditional soil quality assessment methods typically require a large number of soil indicators, making the evaluation process cumbersome and time-consuming, limiting their efficiency and feasibility in practical applications. Furthermore, existing methods are often limited to assessing a single indicator or specific aspects, making it difficult to fully reflect the overall quality of the soil, thereby hindering a comprehensive understanding of soil health and ecosystem function. Summary of the Invention

[0004] In order to overcome or alleviate the problems of the existing technology mentioned in the background technology, which is huge in workload and low in efficiency in soil quality evaluation, the purpose of the present invention is to provide a soil quality evaluation method based on a small number of soil indicators. Through data analysis methods, a small number of key indicators are screened out to comprehensively reflect soil quality. This method reduces the workload of soil quality evaluation and improves the efficiency of soil quality evaluation. In the fields of agricultural management, soil protection and environmental monitoring, soil quality can be evaluated more quickly, comprehensively and efficiently, so as to better guide soil management practices, protect the ecological environment and promote sustainable development.

[0005] The present invention provides the following technical solutions:

[0006] In one aspect, the present invention provides a soil quality evaluation method based on a small number of soil indicators, comprising the following steps:

[0007] S1: Collection: Collect multiple soil samples and measure soil indicators respectively to construct a total data set, and then group the total data set according to soil properties to obtain multiple sub-data sets;

[0008] S2: principal component analysis, performing principal component analysis and Pearson correlation analysis on each of the sub-datasets, and constructing a minimum data set and a revised minimum data set after screening;

[0009] S3: Calculate the soil quality scores of the total dataset, the minimum dataset, and the revised minimum dataset by a weighted summation method;

[0010] S4: linear regression fitting, performing linear regression fitting on the soil quality scores of the total data set, the minimum data set and the revised minimum data set to obtain the linear fitting model of the soil quality scores of different data sets;

[0011] S5: Construct a new data set and determine the coefficient R of the linear fitting model 2 A new dataset was constructed to replace the total dataset for soil quality evaluation.

[0012] Preferably, the soil indicators of the total dataset in step S1 include soil moisture, soil bulk density, saturated water content, field holding capacity, capillary porosity, non-capillary porosity, macroaggregates, microaggregates, average weight diameter, sand, clay, silt, organic carbon, particulate carbon, mineral carbon, dissolved carbon, total nitrogen, dissolved nitrogen, total phosphorus, available phosphorus, ammoniacal nitrogen, nitrate nitrogen, soil pH, soil C / N, microbial biomass carbon, microbial biomass nitrogen, microbial biomass phosphorus, microbial respiration rate, microbial metabolic moisture, catalase, polyphenol oxidase, β-glucosidase, cellobiohydrolase, leucine aminopeptidase, alkaline phosphatase, bacterial residual carbon, and fungal residual carbon; the soil properties include soil physical, chemical, and biological properties, and the corresponding sub-datasets obtained are a soil physical dataset, a soil chemical dataset, and a soil biological dataset, respectively.

[0013] Preferably, step S2 specifically includes the following steps:

[0014] S21: performing principal component analysis on the soil physical dataset, the soil chemical dataset, and the soil biological dataset, respectively, to find a soil index with a higher load coefficient for each principal component to represent the corresponding principal component; if the absolute value of the load coefficient is greater than 0.6, and if the absolute values of the load coefficients are all less than 0.6, then selecting the soil index with the highest load coefficient, and selecting multiple indexes to represent the corresponding principal components;

[0015] S22: Using Pearson correlation analysis, eliminate indicators with strong correlation in soil physical, chemical and biological data sets, and retain soil indicators with high load coefficients, and organize the screened soil indicators into the minimum data set; in each principal component, using Pearson correlation analysis, eliminate indicators with strong correlation, and retain soil indicators with high load coefficients, and organize the screened soil indicators into the revised minimum data set.

[0016] Preferably, step S3 includes the following steps: calculating the weight of each soil indicator in the total data set, the minimum data set, and the revised minimum data set by principal component analysis, and then calculating the score of each indicator by formula 1 and formula 2 respectively; finally, calculating the soil quality score of the total data set, the minimum data set, and the revised minimum data set by formula 3;

[0017]

[0018] In Equations 1 and 2, the scores of soil indicators that are positively correlated with soil quality are calculated using Equation 1; the scores of soil indicators that are negatively correlated with soil quality are calculated using Equation 2; a is the lower limit of the soil indicator in the study area, and b is the upper limit of the soil indicator; x is the value of each indicator measured by the soil sample, and u(x) is the score of each indicator measured by the soil sample;

[0019]

[0020] In the above formula (3), n is the number of indicators, Wi is the weight of the i-th soil indicator, Si is the score of the i-th soil indicator, and CSQI is the soil quality score.

[0021] On the other hand, the present invention also provides a soil quality evaluation system based on a small number of soil indicators, which includes:

[0022] The acquisition module is used to collect multiple soil samples to measure soil indicators respectively, construct a total data set, and group the total data set according to soil properties to obtain multiple sub-data sets;

[0023] A principal component analysis module is used to perform principal component analysis and Pearson correlation analysis on each of the sub-datasets, and construct a minimum data set and a revised minimum data set after screening;

[0024] a calculation module, configured to calculate the soil quality scores of the total dataset, the minimum dataset, and the revised minimum dataset by a weighted summation method;

[0025] A linear regression fitting module is used to perform linear regression fitting on the soil quality scores of the total data set, the minimum data set and the revised minimum data set to obtain linear fitting models of the soil quality scores of different data sets;

[0026] Construct a new dataset module for determining the coefficient R of the linear fitting model 2 A new dataset was constructed to replace the total dataset for soil quality evaluation.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] The soil quality evaluation method provided by the present invention, based on a small number of soil indicators, not only considers the comprehensiveness of soil quality evaluation but also emphasizes the simplicity and practicality of the evaluation process. By carefully screening and optimizing key soil indicators, this method significantly simplifies the data collection and processing steps while ensuring evaluation accuracy, making the soil quality evaluation process more efficient and convenient. This reduces the manpower and time consumed in collecting soil samples to determine soil indicators, and also reduces the testing costs of traditional soil laboratory physical and chemical analysis, providing a new approach for low-cost, high-precision, and high-efficiency soil quality evaluation.

[0029] The soil quality evaluation method based on a small number of soil indicators provided by this invention not only fills the gaps and shortcomings of existing methods, but also provides a simple, fast and comprehensive evaluation tool for soil managers, farmers and environmental monitoring agencies. It is expected to play an important role in promoting soil health, protecting the ecological environment and achieving sustainable development. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 Table 1 provides the distribution characteristics of soil physical, chemical and biological indicators of different ecosystem types in the embodiment of the present invention.

[0031] Figure 2 Table 2 Principal component analysis of soil physical, chemical and biological data sets provided for embodiments of the present invention.

[0032] Figure 3 Table 3 provides the maximum and minimum values of soil indicators and the weights of soil indicators in different data sets provided by the embodiment of the present invention.

[0033] Figure 4 Table 4 shows the soil quality scores of the total dataset and the minimum dataset provided in the embodiment of the present invention.

[0034] Figure 5Pearson correlation analysis of soil physical and chemical indicators provided by the embodiment of the present invention

[0035] Figure 6 Linear regression analysis of soil quality scores of the total dataset, minimum dataset, and revised minimum dataset provided in the embodiment of the present invention.

[0036] Figure 7 This is a flow chart of a soil quality evaluation method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0037] The present invention is described in detail below with reference to the embodiments and accompanying drawings. However, it should be understood that the embodiments and accompanying drawings are merely exemplary descriptions of the present invention and do not constitute any limitation on the scope of protection of the present invention. All reasonable variations and combinations within the scope of the inventive concept of the present invention fall within the scope of protection of the present invention.

[0038] The present invention will be further described below with reference to the accompanying drawings.

[0039] Example 1

[0040] like Figure 7 The implementation steps of the soil quality evaluation method provided in this implementation are specifically the implementation process of the soil quality evaluation score after vegetation restoration. The study area is located in the central part of the Loess Plateau and belongs to the northern temperate semi-humid and semi-arid region. This embodiment is located in Ansai District (latitude and longitude: 36°30′-37°19′N, 108°5′-109°26′E, soil type is loess soil). Farmland was selected as a control, and three vegetation restoration types, namely naturally restored grassland, artificial shrub and artificial tree, were selected for sampling. The main crop in the farmland is corn (Zea mays), the dominant species in the naturally restored grassland are Artemisia capillaris, Artemisia capillaris, Stipa bungeana, and Artemisia sacrorum, the artificial shrub is Caragana korshinskii, and the artificial tree is Robinia pseudoacacia.

[0041] During the S1 collection phase, S11, soil samples were collected from farmland, naturally restored grassland, artificial shrubland, and artificial tree forest at a depth of 0-20 cm. Five 1 m × 1 m soil quadrats were randomly selected, and three soil columns were randomly collected from each quadrats. The soil was mixed evenly, and approximately 1 kg of the sample was selected by quartering. The sample was then passed through a 2 mm sieve to remove debris and combine into one sample.

[0042] S12, pre-process the sample. This includes removing large particles of debris such as rocks and plant roots from the sample, dividing the collected sample into two portions; one portion is refrigerated for measurement of fresh soil parameters such as soil moisture; the other portion is placed in a cool, ventilated area, dried at room temperature, ground, and passed through a 100-mesh sieve for testing.

[0043] S13, soil moisture, soil bulk density, saturated water content, field holding capacity, total porosity, capillary porosity, non-capillary porosity, macroaggregates, microaggregates, clay-size aggregates, average weight diameter, sand, clay, silt, soil structure stability index, organic carbon, particulate carbon, mineral carbon, dissolved organic carbon, total nitrogen, dissolved organic nitrogen, total phosphorus, available phosphorus, ammonia nitrogen, nitrate nitrogen, soil pH, soil C / N, soil N / P, soil The comprehensive soil quality score is calculated based on soil C / P, carbon mineralization ratio, microbial biomass carbon, microbial biomass nitrogen, microbial biomass phosphorus, microbial carbon mineralization, microbial respiration rate, microbial metabolic moisture, catalase, polyphenol oxidase, β-glucosidase, cellobiohydrolase, N-acetylglucosidase, leucine aminopeptidase, alkaline phosphatase, microbial carbon utilization efficiency, bacterial residue carbon, fungal residue carbon, microbial residue carbon, and the ratio of fungal residue carbon to microbial residue carbon. The distribution characteristics of soil physical, chemical, and biological properties in the 0-20 cm depth of farmland, natural restoration grassland, artificial shrubland, and artificial tree forest are shown in the table. Figure 1 Table 1.

[0044] S14, based on the total data set collected, divide it into soil physical data set, soil chemical data set, and soil biological data set according to soil physical, chemical, and biological properties. Principal component analysis was performed on the soil physical, chemical, and biological parameter data sets to find the indicators with higher principal component loading coefficients (absolute values > 0.6) for each principal component, representing the corresponding principal component.

[0045] In the soil physical data set, principal component 1 is represented by soil bulk density, soil water content, saturated water holding capacity, field water holding capacity, total porosity, capillary porosity, silt, macroaggregates, microaggregates, clay-sized aggregates, average weight diameter, and soil structure stability index; principal component 2 is represented by saturated water holding capacity, field water holding capacity, sand, and clay; principal component 3 is represented by non-capillary porosity (see Figure 2 Table 2).

[0046] In the soil chemistry dataset, principal component 1 was represented by organic carbon, mineral carbon, particulate carbon, dissolved organic carbon, total nitrogen, total phosphorus, available phosphorus, pH, carbon mineralization ratio, soil N / P, and soil C / P; principal component 2 was represented by dissolved organic nitrogen and soil C / N; principal component 3 was represented by nitrate nitrogen (see Figure 2 Table 2).

[0047] In the soil biological dataset, principal component 1 was represented by microbial biomass carbon, microbial biomass phosphorus, microbial carbon mineralization, microbial respiration rate, microbial metabolic moisture, catalase, polyphenol oxidase, β-glucosidase, cellobiohydrolase, N-acetylglucosidase, alkaline phosphatase, bacterial residue carbon, fungal residue carbon, microbial residue carbon, and fungal residue / bacterial residue carbon; principal component 2 was represented by microbial carbon mineralization and leucine aminopeptidase. Principal component 3 was represented by catalase; principal component 4 was represented by microbial biomass nitrogen and microbial utilization efficiency (see Figure 2 Table 2).

[0048] In Table 2, red and blue boldfaced elements indicate soil indicators with absolute values of loading coefficients > 0.6 or with the highest loading coefficient within each principal component. Blue boldfaced elements indicate soil indicators retained after removing highly correlated factors from the Pearson correlation analysis within each sub-dataset. Underlined and boldfaced elements (including blue and red boldfaced elements) indicate soil indicators retained after removing highly correlated factors from the Pearson correlation analysis within each principal component.

[0049] In the S2 principal component analysis stage, S21, Pearson correlation analysis was performed on soil physical, chemical and biological indicators to eliminate factors with strong correlation, such as Figure 5 In the figure, red indicates a positive correlation between soil indicators, while blue indicates a negative correlation. The depth of the color represents the strength of the correlation. *p<0.05, ***p<0.01***, p<0.001. The results showed that in the soil physical dataset, the average weight diameter was closely correlated with soil bulk density, soil water content, saturated water holding capacity, field capacity, total porosity, capillary porosity, silt, macroaggregates, microaggregates, clay-sized aggregates, and soil structural stability index; clay was closely correlated with sand; and saturated water holding capacity was closely correlated with field capacity. In the soil chemical dataset, organic carbon was closely correlated with mineral carbon, particulate carbon, dissolved organic carbon, total nitrogen, total phosphorus, available phosphorus, pH, carbon mineralization ratio, soil C / N, soil N / P, and soil C / P. In the soil biological dataset, fungal residue carbon was closely related to microbial biomass carbon, microbial biomass nitrogen, microbial biomass phosphorus, microbial carbon mineralization, microbial respiration rate, microbial metabolic moisture, catalase, N-acetylglucosidase, alkaline phosphatase, microbial carbon utilization efficiency, microbial residue carbon, and fungal residue / bacterial residue carbon; bacterial residue carbon was closely related to polyphenol oxidase, β-glucosidase, cellobiohydrolase, N-acetylglucosidase, and alkaline phosphatase; microbial biomass nitrogen was closely related to microbial carbon utilization efficiency, such as Figure 5 shown.

[0050] In addition, when removing soil indicators with strong correlation, soil indicators with higher loading coefficients were retained. After removing indicators with strong correlation in each sub-dataset, soil physical indicators were represented by average weight diameter, clay particles, and non-capillary porosity. Soil chemical indicators were represented by organic carbon, dissolved organic nitrogen, and nitrate nitrogen. Soil biological indicators were represented by fungal residue carbon, bacterial residue carbon, and leucine aminopeptidase (e.g. Figure 2 Table 2). These nine selected physical and chemical indicators constitute the minimum data set. After removing the indicators with strong correlation in each principal component in the sub-dataset, the soil physical indicators selected average weight diameter, clay, saturated water holding capacity and non-capillary porosity as representatives. The soil chemical indicators selected organic carbon, dissolved organic nitrogen, soil C / N and nitrate nitrogen as representatives. The soil biological indicators selected fungal residue carbon, bacterial residue carbon, leucine aminopeptidase, microbial carbon mineralization, catalase and microbial biomass nitrogen as representatives (such as Figure 2 These 14 selected physicochemical indicators constitute the revised minimum data set.

[0051] Then, the calculation phase of step S3 is carried out to confirm the minimum and maximum values of each soil index in the study area. Then, the weights of each index of the total data set, the minimum data set and the revised minimum data set are calculated by principal component analysis (e.g. Figure 3 Table 3).

[0052] The scores of each indicator are calculated by formula (1) and (2), as follows:

[0053]

[0054]

[0055] The scores of soil indicators that are positively correlated with soil quality are calculated using formula (1). The scores of soil indicators that are negatively correlated with soil quality are calculated using formula (2). a is the lower limit of the soil indicator in the study area, and b is the upper limit of the soil indicator. x is the value of each indicator measured by the soil sample, and u(x) is the score of each indicator measured by the soil sample; since the upper and lower limits of each soil indicator in the study area are difficult to determine, in formula (1), the maximum and minimum values of each soil indicator are selected as the upper and lower limits of the study area, respectively. In formula (2), the minimum and maximum values of each soil indicator are selected as the upper and lower limits of the study area, respectively.

[0056] The soil quality scores of the total dataset, minimum dataset and revised minimum dataset were calculated by weighted summation using formula (3) (see Figure 4 Table 4)

[0057]

[0058] In the above formula, n is the number of indicators, Wi is the weight of the i-th soil indicator, Si is the score of the i-th soil indicator, and CSQI is the soil quality score.

[0059] In the S4 linear regression fitting stage, it was found through linear regression fitting that the soil quality scores calculated from the minimum dataset and the revised minimum dataset had a good fitting relationship with the soil quality scores of the total dataset, and the determination coefficient R 2 are 0.82 and 0.91 respectively. Figure 6 However, the soil quality score ranking calculated by the minimum dataset is different from that of the total dataset. The soil quality scores calculated by the total dataset are artificial shrubs (0.65) > artificial arbor forests (0.61) > natural grasslands (0.59) > farmland (0.20), while the soil quality scores calculated by the minimum dataset are artificial arbor forests (0.73) > natural grasslands (0.55) > artificial shrubs (0.54) > farmland (0.17) (see Figure 4 Table 4). The revised minimum dataset soil quality scores were as follows: artificial shrubland (0.60) > artificial arbor forest (0.56) > natural grassland (0.54) > farmland (0.22), which was consistent with the order of the total dataset soil quality scores. The linear fitting coefficient of determination between the minimum and total dataset soil quality scores was as high as 0.98 ( Figure 2 ). Get the selected indicators of the revised minimum data set.

[0060] Therefore, in the subsequent soil quality evaluation, by measuring the indicators selected in the revised minimum data set (physical indicators: average weight diameter, clay, saturated water holding capacity and non-capillary porosity; soil chemical indicators: organic carbon, dissolved organic nitrogen, soil C / N and nitrate nitrogen; soil biological indicators: fungal residue carbon, bacterial residue carbon, leucine aminopeptidase, microbial carbon mineralization, catalase and microbial biomass nitrogen), the dynamic changes of soil quality in the area can be evaluated quickly, comprehensively and efficiently.

[0061] Example 2

[0062] This embodiment provides a soil quality evaluation device, which includes:

[0063] The acquisition module is used to collect multiple soil samples to measure soil indicators respectively, construct a total data set, and group the total data set according to soil properties to obtain multiple sub-data sets;

[0064] A principal component analysis module is used to perform principal component analysis and Pearson correlation analysis on each of the sub-datasets, and construct a minimum data set and a revised minimum data set after screening;

[0065] a calculation module, configured to calculate the soil quality scores of the total dataset, the minimum dataset, and the revised minimum dataset by a weighted summation method;

[0066] A linear regression fitting module is used to perform linear regression fitting on the soil quality scores of the total data set, the minimum data set and the revised minimum data set to obtain linear fitting models of the soil quality scores of different data sets;

[0067] A new data set construction module is used to construct a new data set to replace the total data set for soil quality evaluation based on the determination coefficient R2 of the linear fitting model.

[0068] The above embodiments are merely preferred embodiments of the present invention, and the scope of protection of the present invention is not limited to the above embodiments. All technical solutions that fall within the scope of protection of the present invention are within the scope of protection of the present invention. It should be noted that improvements and modifications that can be made by a person skilled in the art without departing from the principles of the present invention are also considered to be within the scope of protection of the present invention.

Claims

1. A soil quality evaluation method based on a small number of soil indicators, characterized by: The following steps are involved: S1: Collection: Collect multiple soil samples and measure soil indicators respectively to construct a total data set, and then group the total data set according to soil properties to obtain multiple sub-data sets; S2: principal component analysis, performing principal component analysis and Pearson correlation analysis on each of the sub-datasets, and constructing a minimum data set and a revised minimum data set after screening; S3: Calculate the soil quality scores of the total dataset, the minimum dataset, and the revised minimum dataset by a weighted summation method; S4: linear regression fitting, performing linear regression fitting on the soil quality scores of the total data set, the minimum data set and the revised minimum data set to obtain the linear fitting model of the soil quality scores of different data sets; S5: Construct a new data set and determine the coefficient R of the linear fitting model 2 A new dataset was constructed to replace the total dataset for soil quality evaluation.

2. The soil quality evaluation method according to claim 1, characterized in that: The soil indicators of the total dataset in step S1 include soil moisture, soil bulk density, saturated water content, field holding capacity, capillary porosity, non-capillary porosity, macroaggregates, microaggregates, average weight diameter, sand, clay, silt, organic carbon, particulate carbon, mineral carbon, dissolved carbon, total nitrogen, dissolved nitrogen, total phosphorus, available phosphorus, ammoniacal nitrogen, nitrate nitrogen, soil pH, soil C / N, microbial biomass carbon, microbial biomass nitrogen, microbial biomass phosphorus, microbial respiration rate, microbial metabolic moisture, catalase, polyphenol oxidase, β-glucosidase, cellobiohydrolase, leucine aminopeptidase, alkaline phosphatase, bacterial residual carbon, and fungal residual carbon; the soil properties include soil physical, chemical, and biological properties, and the corresponding sub-datasets obtained are a soil physical dataset, a soil chemical dataset, and a soil biological dataset, respectively.

3. The soil quality evaluation method according to claim 2, characterized in that: Step S2 specifically includes the following steps: S21: performing principal component analysis on the soil physical dataset, the soil chemical dataset, and the soil biological dataset, respectively, to find a soil index with a higher load coefficient for each principal component to represent the corresponding principal component; if the absolute value of the load coefficient is greater than 0.6, and if the absolute values of the load coefficients are all less than 0.6, then selecting the soil index with the highest load coefficient, and selecting multiple indexes to represent the corresponding principal components; S22: Using Pearson correlation analysis, eliminate indicators with strong correlation in soil physical, chemical and biological data sets, and retain soil indicators with high load coefficients, and organize the screened soil indicators into the minimum data set; in each principal component, using Pearson correlation analysis, eliminate indicators with strong correlation, and retain soil indicators with high load coefficients, and organize the screened soil indicators into the revised minimum data set.

4. The soil quality evaluation method according to claim 3, characterized in that: Step S3 includes the following steps: calculating the weight of each soil indicator in the total dataset, the minimum dataset, and the revised minimum dataset by principal component analysis, and then calculating the score of each indicator by equation 1 and equation 2 respectively; finally, calculating the soil quality score of the total dataset, the minimum dataset, and the revised minimum dataset by equation 3; In Equations 1 and 2, the scores of soil indicators that are positively correlated with soil quality are calculated using Equation 1; the scores of soil indicators that are negatively correlated with soil quality are calculated using Equation 2; a is the lower limit of the soil indicator in the study area, and b is the upper limit of the soil indicator; x is the value of each indicator measured by the soil sample, and u(x) is the score of each indicator measured by the soil sample; In the above formula (3), n is the number of indicators, Wi is the weight of the i-th soil indicator, Si is the score of the i-th soil indicator, and CSQI is the soil quality score.

5. A soil quality evaluation system based on a small number of soil indicators, characterized by: include: The acquisition module is used to collect multiple soil samples to measure soil indicators respectively, construct a total data set, and group the total data set according to soil properties to obtain multiple sub-data sets; A principal component analysis module is used to perform principal component analysis and Pearson correlation analysis on each of the sub-datasets, and construct a minimum data set and a revised minimum data set after screening; a calculation module, configured to calculate the soil quality scores of the total dataset, the minimum dataset, and the revised minimum dataset by a weighted summation method; A linear regression fitting module is used to perform linear regression fitting on the soil quality scores of the total data set, the minimum data set and the revised minimum data set to obtain linear fitting models of the soil quality scores of different data sets; Construct a new dataset module for determining the coefficient R of the linear fitting model 2 A new dataset was constructed to replace the total dataset for soil quality evaluation.

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