Cunninghamia lanceolata near-natural transformation tree species selection method based on soil quality index analysis

Abstract

The invention provides a cunninghamia lanceolata close-to-nature transformed tree species selection method based on soil quality index analysis, and provides the cunninghamia lanceolata close-to-nature transformed tree species selection method based on the soil quality index by quantifying soil quality change conditions in different transformation modes. According to the method, through deep analysis of changes of key indexes such as soil physicochemical properties after mixing of cunninghamia lanceolata and different indigenous tree species, a tree species combination most beneficial to improvement of soil fertility is determined, and an optimal cunninghamia lanceolata close-to-nature transformation operation mode is formulated accordingly.

Description

Technical Field

[0001] The present application belongs to the field of forest ecosystem management and scientific assessment of soil quality, and in particular, relates to an innovative method for optimizing soil quality by replanting a variety of native tree species and soil quality evaluation model technology in Chinese fir plantations; specifically, it is a method for selecting Chinese fir tree species for near-natural transformation based on soil quality index analysis. Background Art

[0002] At present, the scale of plantations and their contribution to forest resources are significant, becoming the key force for the growth of forest resources. However, the accompanying problems cannot be ignored, such as low forest quality, declining soil fertility, low volume per unit area and annual growth. These problems have posed a huge challenge to the sustainable development of plantations. Cunninghamia lanceolata (Lamb.) Hook. is one of the most widely planted plantation tree species in China. It is promoted on a large scale because of its beautiful trunk shape, tall tree body, excellent material and rapid growth. The area of ​​Cunninghamia lanceolata plantations reaches 100 million hectares, and the volume is as high as 755 million cubic meters, which makes Cunninghamia lanceolata occupy a core position in China's plantation system.

[0003] As an important foundation of artificial forest ecosystem, forest soil not only provides the necessary material support for forest growth, but also plays a vital role in maintaining forest ecological balance, supporting biodiversity and regulating ecosystem functions. Soil quality is affected by many factors, including soil physical and chemical properties, soil microbial activity and enzyme activity, aboveground tree species selection and forest management model. Therefore, how to restore and improve soil quality through effective soil management technology, and then improve the productivity of Chinese fir plantations, has become an urgent problem to be solved by contemporary forestry workers. Due to long-term pure forest continuous planting and predatory management, the fragility of artificial forest ecosystems has become more and more obvious, and complex ecological problems such as soil degradation, biodiversity reduction, productivity reduction, and ecosystem stability have emerged one after another. In addition, the unique biological characteristics of Chinese fir have caused the phenomenon of soil fertility decline in Chinese fir plantations to be very serious, which in turn affects productivity and sustainability. How to maintain the long-term productivity of Chinese fir plantations has become one of the urgent problems to be solved by current forestry workers. Soil quality is affected by many factors such as soil physical and chemical properties, soil microbial and enzyme activity, and aboveground tree species selection.

[0004] Therefore, how to maintain the existing and future forest productivity, soil fertility of forest systems and biodiversity in forest systems has become an important part of forest management practices around the world. Currently, Chinese fir plantations are facing problems such as soil degradation and declining productivity. How to improve soil quality through reasonable forest structure and management measures has become a key technical problem that needs to be solved urgently.

[0005] The above statements are only used to provide background technical information related to the present application. Unless otherwise stated herein, the contents described in this section are not prior art for the contents of other parts of the present application. Summary of the invention

[0006] The present invention proposes a method for selecting tree species for near-natural transformation of Chinese fir based on soil quality index by quantifying the changes in soil quality under different transformation modes. This method analyzes the changes in key indicators such as soil physical and chemical properties after Chinese fir is mixed with different native tree species, identifies the tree species combination that is most conducive to improving soil fertility, and formulates the best near-natural transformation management model for Chinese fir accordingly.

[0007] According to a first aspect of an embodiment of the present application, a method for selecting Chinese fir tree species for near-natural transformation based on soil quality index analysis is provided, comprising: According to different tree species and mixed proportions, the fir plantations are divided into a variety of near-natural transformation models; Collect soil samples in various near-natural transformation plots; Detect the chemical composition data of soil samples under different near-natural transformation modes, and calculate the stoichiometric ratio of the soil based on the chemical composition data; The minimum data set (MDS) for soil quality evaluation was selected based on the soil chemical composition data and stoichiometric ratio, and a soil quality index (SQI) model was established to calculate the soil quality index of soil samples under different near-natural transformation modes. The tree species for near-natural transformation of Chinese fir are selected according to the soil quality index under different near-natural transformation modes.

[0008] In some embodiments of the present application, soil samples are collected in a variety of near-natural transformation model plots, including: Remove the surface litter and humus layer and conduct distributed sampling along an “S”-shaped sampling path; At the sampling point, the soil profile was divided into three soil layers according to depth, including the surface soil, the middle soil and the bottom soil, and soil samples were collected from the three soil layers respectively; The soil samples were processed and stored.

[0009] In some embodiments of the present application, the chemical composition data of soil samples under different near-natural transformation modes are measured, and the stoichiometric ratio of the soil is calculated according to the chemical composition data, including: Accurately measure soil organic matter, total phosphorus, inorganic phosphorus, organic phosphorus and other chemical components to obtain chemical composition data; The stoichiometric ratio of the soil is calculated based on the data of various chemical components in the soil; the stoichiometric ratio includes the relative proportions between the three major elements of carbon, nitrogen and phosphorus in the soil.

[0010] In some embodiments of the present application, a minimum data set (MDS) for soil quality evaluation is selected based on the chemical composition data and stoichiometric ratio of the soil, including: The collected soil nutrient data need to be standardized; The Bray-Curtis distance matrix was used to calculate the similarity between soil samples; The original minimum data set (MDS) was screened out by principal component analysis (PCA) method.

[0011] The comprehensive load (Norm) of each indicator is calculated by the comprehensive load (Norm) formula, and the original minimum data set (MDS) is filtered according to the size of the comprehensive load of each indicator to obtain the final minimum data set (MDS).

[0012] In some embodiments of the present application, the following steps are also included: The correlation coefficients between the selected indicators in the final minimum data set (MDS) were evaluated using the correlation analysis method; If the correlation coefficient is less than the threshold, the corresponding indicator is retained in the minimum data set (MDS); If the correlation coefficient is greater than or equal to the threshold, only the indicators with larger comprehensive loadings are retained in the minimum data set (MDS), and the rest are discarded.

[0013] In some embodiments of the present application, a soil quality index (SQI) model is established and the soil quality index of soil samples under different near-natural transformation modes is calculated, including: The weight of each indicator in the minimum data set (MDS) is calculated through the standardized Bray-Curtis distance matrix; The weighted scores of each index were calculated using the soil standard scoring functions (SSF) formula; According to the weight and score of each indicator, the soil quality index of the soil sample is calculated using the soil quality index (SQI) formula. The soil quality index (SQI) formula is: ; Among them, W i is the weight of the i-th indicator, Q i is the index score, n is the number of parameters in the small data set (MDS); The higher the soil quality index (SQI) value obtained by the formula, the better the soil quality and the stronger the soil fertility.

[0014] In some embodiments of the present application, the weighted scores of each indicator are calculated using the soil standard scoring functions (SSF) formula, including: The SSF (excluding pH) calculation formula is as follows: (1); In formula (1), x is the measured value of the indicator; f(x) is the score of the indicator, ranging from 0.1 to 1.0; x1 and x2 are the lower and upper limits of the indicator, respectively; (2); In formula (2), x is the monitoring value of the indicator; f(x) is the score of the indicator; x 1、 x 2、 x 3、 x4 is the set threshold.

[0015] According to a second aspect of the embodiment of the present application, a system for applying the above method for selecting tree species for near-natural transformation of Chinese fir is provided, comprising: A soil chemical composition detection module is used to detect the chemical composition data of the soil samples under different near-natural transformation modes, and calculate the stoichiometric ratio of the soil based on the chemical composition data; A soil quality evaluation model is used to select a minimum data set (MDS) for soil quality evaluation based on the chemical composition data and stoichiometric ratio of the soil, establish a soil quality index (SQI) model, and calculate the soil quality index of the soil samples under different near-natural transformation modes; The tree species selection module is used to select the Chinese fir near-natural transformation tree species according to the soil quality index under different near-natural transformation modes.

[0016] According to the third aspect of an embodiment of the present application, a device for selecting near-naturally transformed Chinese fir tree species is provided, comprising: a storage unit for storing executable instructions; and a processing unit for connecting to the memory to execute the executable instructions to complete a near-naturally transformed Chinese fir tree species selection method based on soil quality index analysis.

[0017] According to a fourth aspect of an embodiment of the present application, a computer-readable storage medium is provided, on which a computer program is stored; the computer program is executed by a processor to implement a method for selecting tree species for near-natural transformation of Chinese fir based on soil quality index analysis.

[0018] The method, system and device for selecting Chinese fir near-natural transformation tree species based on soil quality index analysis of the present application include dividing Chinese fir plantations into multiple near-natural transformation modes according to different tree species and mixed planting ratios; collecting soil samples in sample plots of multiple near-natural transformation modes; detecting the chemical composition data of soil samples under different near-natural transformation modes, and calculating the stoichiometric ratio of the soil based on the chemical composition data; selecting a minimum data set (MDS) for soil quality evaluation based on the chemical composition data and the stoichiometric ratio of the soil, establishing a soil quality index (SQI) model and calculating the soil quality index of soil samples under different near-natural transformation modes; and selecting Chinese fir near-natural transformation tree species based on the soil quality index under different near-natural transformation modes.

[0019] This application selects tree species for near-natural transformation of Chinese fir by quantifying the changes in soil quality under different transformation models. This method deeply analyzes the changes in key indicators such as soil physical and chemical properties after mixed planting of Chinese fir with different native tree species, identifies the tree species combination that is most conducive to improving soil fertility, and formulates the best near-natural transformation management model for Chinese fir based on this.

[0020] This application plan not only focuses on the ecological balance of artificial forests and the sustainable improvement of soil quality, but also covers the technical means of transforming Chinese fir artificial forests by introducing native tree species. It aims to improve soil fertility, ecosystem stability and productivity, thereby achieving sustainable development of forest land. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings: Figure 1 : A schematic diagram of the steps of the method for selecting tree species for near-natural transformation of Chinese fir based on soil quality index analysis according to an embodiment of the present application is shown; Figure 2 A schematic diagram of the steps of selecting a minimum data set (MDS) for soil quality evaluation according to an embodiment of the present application is shown in FIG. Figure 3 : A schematic diagram of the steps of establishing a soil quality index (SQI) model and calculating the soil quality index according to an embodiment of the present application is shown; Figure 4 The box plots of some soil chemical properties under different transformation modes are shown in; Figure 5 The box plots of the stoichiometric ratios of N, P, and K under different transformation modes are shown in the figure; Figure 6 ] shows a soil quality index diagram of different transformation modes according to an embodiment of the present application; Figure 7 The structure schematic diagram of the system for selecting a tree species for near-natural transformation of Chinese fir according to an embodiment of the present application is shown in FIG. Figure 8 The figure shows a schematic structural diagram of a device for selecting a tree species for near-natural transformation of Chinese fir according to an embodiment of the present application. DETAILED DESCRIPTION

[0022] Regarding this application, considering that replanting native tree species for mixed planting, actively carrying out near-natural forestry is to promote the transformation of artificial forests into multi-species mixed forests and uneven-aged forests. This model not only helps to improve the composition of vegetation communities, but also increases the amount and chemical composition of litter and fine roots in the soil, thereby optimizing the soil's physical and chemical properties such as water retention capacity, organic matter content, pH and quick-acting nutrient supply.

[0023] Considering that mixed afforestation can not only significantly improve the stability of forest stands and soil fertility, but also provide favorable conditions for the rapid and sustained growth of trees, this application conducts in-depth research on the role of soil quality in near-natural forestry, which will provide a theoretical basis for guiding the sustainable development of Chinese fir plantations.

[0024] At present, fir plantations are facing problems such as soil degradation and declining productivity. How to improve soil quality through reasonable forest structure and management measures has become a key technical problem that needs to be solved urgently. The selection of mixed tree species is the core link in the transformation of fir plantations, playing an important role in improving soil properties, soil fertility and forest productivity.

[0025] The present invention aims to propose the most suitable Chinese fir near-natural transformation tree species based on soil fertility maintenance by comparing the changes in forest soil quality under different Chinese fir near-natural transformation modes.

[0026] To achieve the above-mentioned purpose, the method for selecting Chinese fir species for near-natural transformation based on soil quality index analysis provided by the present invention mainly comprises the following steps: 1. Divide the Chinese fir plantations into multiple near-natural transformation modes according to different tree species and mixed planting ratios; 2. Collect soil samples in the sample plots of multiple near-natural transformation modes; 3. Detect the chemical composition data of soil samples under different near-natural transformation modes, and calculate the stoichiometric ratio of the soil based on the chemical composition data; 4. Select the minimum data set (MDS) for soil quality evaluation based on the chemical composition data and stoichiometric ratio, establish a soil quality index (SQI) model, and calculate the soil quality index of soil samples under different near-natural transformation modes; 5. Select Chinese fir near-natural transformation tree species based on the soil quality index under different near-natural transformation modes.

[0027] In addition, the present application also provides a system and device for selecting tree species for near-natural transformation of Chinese fir.

[0028] This application selects tree species for near-natural transformation of Chinese fir by quantifying the changes in soil quality under different transformation models. This method deeply analyzes the changes in key indicators such as soil physical and chemical properties after mixed planting of Chinese fir with different native tree species, identifies the tree species combination that is most conducive to improving soil fertility, and formulates the best near-natural transformation management model for Chinese fir based on this.

[0029] This application plan not only focuses on the ecological balance of artificial forests and the sustainable improvement of soil quality, but also covers the technical means of transforming Chinese fir artificial forests by introducing native tree species. It aims to improve soil fertility, ecosystem stability and productivity, thereby achieving sustainable development of forest land.

[0030] In order to make the technical solutions and advantages in the embodiments of the present application more clearly understood, the exemplary embodiments of the present application are further described in detail below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than an exhaustive list of all the embodiments. It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

[0031] Example 1 Figure 1 Detailed description is given in the figure which is a schematic diagram of the steps of the method for selecting tree species for near-natural transformation of Chinese fir based on soil quality index analysis according to an embodiment of the present application.

[0032] like Figure 1 As shown in the figure, the tree species selection method for near-natural transformation of Chinese fir includes: S1: Based on different tree species and mixed proportions, the fir plantations are divided into a variety of near-natural transformation patterns; S2: Collect soil samples in plots with various near-natural transformation patterns; S3: Detect the chemical composition data of soil samples under different near-natural transformation modes, and calculate the stoichiometric ratio of the soil based on the chemical composition data; S4: Select the minimum data set (MDS) for soil quality evaluation based on the chemical composition data and stoichiometric ratio of the soil, establish a soil quality index (SQI) model, and calculate the soil quality index of soil samples under different near-natural transformation modes; S5: Select Chinese fir tree species for near-natural transformation based on soil quality index under different near-natural transformation modes.

[0033] In the specific implementation, the process of collecting soil samples in various near-natural transformation patterns in S2 mainly includes the following steps: 1) Remove the litter and humus layer on the ground and conduct distributed sampling along an “S”-shaped sampling path; 2) At the sampling point, the soil profile is divided into three soil layers according to depth, including the surface soil, the middle soil and the bottom soil, and soil samples are collected from the three soil layers respectively; 3) The soil samples are preliminarily processed and stored.

[0034] For a detailed description, it is necessary to obtain the soil environmental factor data after the near-natural transformation of Chinese fir plantations.

[0035] First, the fir plantations were divided into a variety of near-natural transformation modes according to different tree species and mixed planting ratios. Each transformation mode formed a corresponding standard plot based on the preset mixed planting design, combined with local ecological conditions and soil conditions. In addition, a pure forest plot was set up as a control group to conduct a systematic soil quality comparison.

[0036] In order to ensure that the collection of soil samples is representative and scientific, detailed soil sampling work is carried out in each near-natural transformation model and pure forest plot according to standardized sampling procedures.

[0037] When collecting soil samples in the sample plot, first ensure that the soil surface is clean and remove all surface litter and humus layers, which will help obtain more accurate soil data. In the sample plot, distributed sampling is carried out according to the "S"-shaped sampling path to ensure that the samples cover different areas of the entire sample plot. In specific operations, three small sample plots need to be randomly selected in each near-natural transformation model sample plot and pure forest sample plot to reduce sampling bias and improve the representativeness of the sample.

[0038] In each small sample plot, a soil profile with a vertical depth of 60 cm was dug using a soil sampling tool. The soil profile was divided into three soil layers according to depth, namely the surface soil (0-20 cm), the middle soil (20-40 cm) and the bottom soil (40-60 cm). Three soil samples were collected at these depths respectively, and the soil samples of the same depth were evenly mixed to ensure the homogeneity of the samples. According to the different depths, these mixed samples were marked as T (surface layer), M (middle layer) and B (bottom layer) for subsequent analysis and recording.

[0039] Then, after the collected soil samples have been initially processed, they need to be further sorted. Optionally, the soil is sieved through a 2 mm sieve to remove large roots, stones, plant debris and other debris, ensuring that the remaining soil samples are purer and suitable for further analysis. This process helps reduce the interference of impurities in the soil samples and improves the accuracy of the analysis results.

[0040] In order to conduct an in-depth study of the chemical properties of the soil, some of the processed soil samples need to be stored. A portion of the screened soil samples are sealed and stored in a refrigerated environment at 4°C to ensure that the soil samples remain in their original state before entering the laboratory to avoid changes in chemical properties caused by improper storage. These preserved soil samples will then be used for a series of chemical analyses in the laboratory, including but not limited to the determination of key indicators such as soil organic matter, pH value, and nutrient content. Through this systematic sampling and analysis, the present invention can comprehensively evaluate the impact of different near-natural transformation modes of Chinese fir on soil quality, thereby providing a scientific basis for improving soil fertility and forest productivity.

[0041] Through this rigorous soil sampling procedure and precise sample processing method, the present invention ensures that the soil environmental factor data obtained have a high degree of scientific reliability and repeatability, laying a solid foundation for subsequent soil quality analysis and transformation model optimization.

[0042] The next specific implementation is to measure the chemical composition data of soil samples under different near-natural transformation modes in S3, and calculate the stoichiometric ratio of the soil based on the chemical composition data, mainly including: 1) Accurately measure soil organic matter, total phosphorus, inorganic phosphorus, organic phosphorus and other chemical components to obtain chemical composition data; 2) Calculate the stoichiometric ratio of the soil based on the data of various chemical components in the soil; the stoichiometric ratio includes the relative proportions between the three major elements of carbon, nitrogen and phosphorus in the soil.

[0043] A detailed description is given, that is, to explore the effects of different near-natural transformation modes on the physical and chemical properties of soil.

[0044] After the soil samples are screened and processed in step S2, the soil samples will be used to measure a series of chemical property indicators.

[0045] Laboratory analysis involves the precise determination of soil organic matter, total phosphorus, inorganic phosphorus, organic phosphorus and other chemical components. The specific operation methods are shown in Table 1 for the determination methods of various soil chemical indicators.

[0046] Table 1 Determination methods of various chemical indicators of soil

[0047] The determination of soil organic matter (SOM) content uses standard laboratory analysis methods, and the measured organic matter content needs to be further converted into soil organic carbon (SOC) content. This conversion process uses the van Bemmelen factor, which is often used to assume that the carbon content in soil organic matter is 58%, that is, multiplying the organic matter content by 0.58 to obtain the corresponding organic carbon content. This method is widely used in the field of soil science and ensures the accurate calculation of SOC content.

[0048] After completing the basic determination of various chemical components in the soil, the present invention will also calculate the stoichiometric ratio of the soil, focusing on analyzing the relative ratios between the three major elements of carbon, nitrogen and phosphorus in the soil.

[0049] By calculating the soil stoichiometric ratio, the balance of soil nutrients and their mechanism of action in the ecosystem can be revealed.

[0050] The specific calculation method for the relative proportions of the three major elements of carbon, nitrogen and phosphorus in the soil is as follows: 1. Soil carbon-nitrogen ratio (C / N): The ratio of the measured soil organic carbon content (SOC) to the soil nitrogen content is taken as the soil C / N ratio. This ratio reflects the carbon-nitrogen balance in the soil and has an important impact on soil microbial decomposition activities and soil nutrient cycles.

[0051] 2. Soil carbon-phosphorus ratio (C / P): The soil C / P ratio is calculated by the ratio of soil organic carbon content to soil total phosphorus content. This ratio helps to evaluate the relative supply capacity of carbon and phosphorus in the soil and its impact on plant growth.

[0052] 3. Soil nitrogen-phosphorus ratio (N / P): The soil N / P ratio is calculated based on the ratio of nitrogen content to total phosphorus content in the soil. This ratio can reflect the balance of soil nutrient supply, especially in the context of different plant nutrient requirements, the N / P ratio is one of the important indicators for evaluating nutrient limitation. Through in-depth analysis of soil stoichiometric ratios, the present invention can better understand the role of soil nutrient balance in maintaining soil fertility during the near-natural transformation of Chinese fir plantations, and provide theoretical support for optimizing tree species selection and transformation models.

[0053] Figure 2 A schematic diagram of the steps of selecting a minimum data set (MDS) for soil quality evaluation according to an embodiment of the present application is shown in FIG.

[0054] like Figure 2 As shown, further, when the step S4 is specifically implemented, a minimum data set (MDS) for soil quality evaluation is selected according to the chemical composition data and stoichiometric ratio of the soil, including: S41: The collected soil nutrient data need to be standardized; S42: Calculate the similarity between soil samples using the Bray-Curtis distance matrix; S43: The original minimum data set (MDS) was screened by principal component analysis (PCA) method; S44: Calculate the comprehensive load (Norm) of each indicator through the comprehensive load (Norm) formula, filter the original minimum data set (MDS) according to the size of the comprehensive load of each indicator, and obtain the final minimum data set (MDS); The comprehensive load (Norm) formula is as follows: ; Among them, N ik is the comprehensive loading of each variable on the principal component k with eigenvalue ≥ 1; is the loading of the variable on principal component k, λ k is the eigenvalue of the kth principal component.

[0055] Preferably, it also includes: using a correlation analysis method to evaluate the correlation coefficient between the selected indicators in the final minimum data set (MDS); if the correlation coefficient is less than a threshold, retaining the corresponding indicator in the minimum data set (MDS); if the correlation coefficient is greater than or equal to the threshold, only retaining the indicator with a larger comprehensive load in the minimum data set (MDS), and discarding the remaining indicators.

[0056] Figure 3 A schematic diagram of the steps of establishing a soil quality index (SQI) model and calculating the soil quality index according to an embodiment of the present application is shown in FIG.

[0057] like Figure 3 As shown, further, in step S4, a soil quality index (SQI) model is established and the soil quality index of soil samples under different near-natural transformation modes is calculated, including: S401: Calculate the weight of each indicator in the minimum data set (MDS) through the standardized Bray-Curtis distance matrix; S402: Calculate the weighted scores of each index using the soil standard scoring functions (SSF) formula; S403: According to the weight and score of each indicator, the soil quality index of the soil sample is calculated using the soil quality index (SQI) formula. The soil quality index (SQI) formula is: ; Among them, W i is the weight of the i-th indicator, Q i is the index score, n is the number of parameters in the small data set (MDS); The higher the soil quality index (SQI) value obtained by the formula, the better the soil quality and the stronger the soil fertility.

[0058] The soil standard scoring functions (SSF) formula is used to calculate the weighted scores of each indicator, including: The SSF (excluding pH) calculation formula is as follows: (1); In formula (1), x is the measured value of the indicator; f(x) is the score of the indicator, ranging from 0.1 to 1.0; x1 and x2 are the lower and upper limits of the indicator, respectively; (2); In formula (2), x is the monitoring value of the indicator; f(x) is the score of the indicator; x 1、 x 2、 x 3、 x4 is the set threshold.

[0059] A detailed description is given, i.e., a comprehensive evaluation of soil fertility under different near-natural transformation models.

[0060] The construction of soil quality evaluation model is the core of the whole study, which mainly includes two key steps: one is the selection of minimum data set (MDS) for soil quality evaluation, and the other is the establishment and calculation of soil quality index (SQI) model.

[0061] Through these steps of the soil quality evaluation model, a scientific quantitative assessment of soil quality under different near-natural transformation modes of Chinese fir can be achieved, thus providing a basis for forest management.

[0062] First, the key step is to standardize the collected soil nutrient data in the initial stage of model construction. This step ensures that all soil nutrient indicators are compared on the same scale, thereby eliminating the impact of unit differences on subsequent analysis.

[0063] After data standardization, 2. the similarity between soil samples was calculated using the Bray-Curtis distance matrix, which laid the foundation for principal component analysis (PCA) of soil quality.

[0064] Next, 3. Principal component analysis (PCA) was performed to screen out the minimum data set (MDS). In the principal component analysis, the principal components with eigenvalues ​​(i.e., the contribution rate of each principal component) greater than or equal to 1 were selected as key principal components, which are important and representative in explaining the changes in soil quality. For the soil indicators in each principal component, only those with factor loading values ​​greater than or equal to 0.3 were considered to have significant contributions to the principal component and were taken into consideration. If an indicator has a high loading value (all greater than 0.3) in multiple principal components, it will be classified into the group corresponding to the principal component with the highest load to ensure the rationality of the indicator allocation.

[0065] To further improve the explanatory power of the selected soil indicators, next, 4. Calculate the comprehensive load (Norm) of each indicator to measure its importance in the model. The larger the comprehensive load value, the stronger the contribution of the indicator in explaining the change of soil quality.

[0066] The Norm calculation formula is as follows, where N ik is the comprehensive loading of each variable on the principal component k with eigenvalue ≥ 1; is the loading of the variable on principal component k, λ k is the eigenvalue of the kth principal component.

[0067] .

[0068] After the principal components were grouped, all soil indices with a difference of less than 10% from the highest loading value were further screened, and these indices were considered to be the most representative MDS candidates.

[0069] After the initial screening of the MDS, 5. Use correlation analysis to evaluate the correlation coefficients between the selected indicators to determine whether there is multicollinearity between them. If the correlation coefficients between multiple indicators are small (cor < 0.4), all of these indicators are retained in the MDS. However, if the correlation coefficient is high (cor ≥ 0.4), only the indicators with larger comprehensive loads are retained, and the remaining indicators are discarded. The purpose of this is to reduce redundant information and ensure the simplicity and effectiveness of the model.

[0070] After determining the final MDS index, we proceed to the key step 2, assigning weights to each index. The weights of each index are calculated using the standardized Bray-Curtis distance matrix, and the weighted scores of each index are calculated using the soil standard scoring functions (SSF). The score range for each index is 0.1-1.0. The selection of different index thresholds refers to the relevant research on soil fertility in subtropical fir plantations in my country.

[0071] The calculation formula of SSF (excluding pH value) is as follows: (1); In the formula, x is the measured value of the indicator; f(x) is the score of the indicator, ranging from 0.1 to 1.0; x1 and x2 are the lower and upper limits of the indicator, respectively. This formula ensures the uniformity and comparability of different soil indicators in soil quality evaluation.

[0072] The SSF formula for soil pH is calculated as follows: (2); In the formula, x is the monitoring value of the indicator; f(x) is the score of the indicator; according to the research of Shang and Wang et al., x1, x2, x3, x4 are 3.5, 5.3, 6.5, 9.5 respectively. These thresholds are set according to the results of the soil fertility research of subtropical fir plantations, ensuring the accurate contribution of pH value to soil quality evaluation.

[0073] Finally, the selection of Chinese fir near-natural transformation tree species is carried out according to the soil quality index under different near-natural transformation modes, that is, the near-natural transformation tree species are screened according to the soil quality index.

[0074] The soil quality index (SQI) directly reflects the quality of soil.

[0075] The soil quality index (SQI) calculation formula is as follows: ; Among them, SQI is soil quality index, W i is the weight of the i-th indicator, Q i is the index score, and n is the number of parameters in MDS.

[0076] The higher the SQI obtained by this formula, the better the soil quality and the stronger the soil fertility. This index provides us with an intuitive and scientific quantitative tool to systematically evaluate the changes in soil quality under different near-natural transformation modes of Chinese fir. This method of constructing a soil quality evaluation model can not only objectively and scientifically evaluate soil quality, but also provide an effective decision-making basis for soil improvement and ecological management of Chinese fir plantations.

[0077] Based on a good experimental design, the present invention proposes a mixed tree species selection based on the near-natural transformation mode of Chinese fir plantations currently commonly used in my country's subtropical areas. It can not only comprehensively evaluate the soil quality after transformation based on comprehensive consideration of soil chemical properties, but also propose a suitable transformation mode and select the best mixed replanting tree species. The invention explores the changes in soil after the near-natural transformation of Chinese fir plantations and determines the optimal tree species mixing ratio corresponding to the positive succession of the forest ecosystem, in order to construct a scientific near-natural transformation system for Chinese fir plantations, and provide a scientific basis for maintaining the sustainable productivity and stable development of the Chinese fir plantation ecosystem.

[0078] In summary, the method for selecting Chinese fir near-natural transformation tree species based on soil quality index analysis of the present application includes dividing Chinese fir plantations into multiple near-natural transformation modes according to different tree species and mixed planting ratios; collecting soil samples in sample plots of multiple near-natural transformation modes; detecting the chemical composition data of soil samples under different near-natural transformation modes, and calculating the stoichiometric ratio of the soil based on the chemical composition data; selecting a minimum data set (MDS) for soil quality evaluation based on the chemical composition data and the stoichiometric ratio of the soil, establishing a soil quality index (SQI) model and calculating the soil quality index of soil samples under different near-natural transformation modes; and selecting Chinese fir near-natural transformation tree species based on the soil quality index under different near-natural transformation modes.

[0079] The embodiment of the present application quantifies the changes in soil quality under different transformation modes to select tree species for the near-natural transformation of Chinese fir. This method deeply analyzes the changes in key indicators such as soil physical and chemical properties after mixed planting of Chinese fir with different native tree species, identifies the tree species combination that is most conducive to improving soil fertility, and accordingly formulates the optimal near-natural transformation management model for Chinese fir.

[0080] This application plan not only focuses on the ecological balance of artificial forests and the sustainable improvement of soil quality, but also covers the technical means of transforming Chinese fir artificial forests by introducing native tree species. It aims to improve soil fertility, ecosystem stability and productivity, thereby achieving sustainable development of forest land.

[0081] Example 2 This embodiment further illustrates Embodiment 1 in conjunction with a specific implementation scenario.

[0082] The research object of this embodiment is a fir plantation in Lake Forest Farm, City A, which was transformed into a near-natural forest by mixing different species of fir.

[0083] City A is located at 27°51′-28°34′ north latitude and 113°10′-114°15′ east longitude. It is mainly mountainous and hilly. Its forest resources are very rich. It is a key forest area with an existing forest area of ​​337,300 hm2, a forest stock of 15.58 million m3 and a forest coverage rate of 66.2%.

[0084] The experimental sites are located in the forest farm of City A (113°55´E, 28°23´N). The study site has a mid-subtropical monsoon humid climate, the main zonal vegetation is evergreen broad-leaved forest, the annual average temperature is 17.2℃, and the annual average precipitation is about 1500-1600 mm and 1680 mm. The altitude of the study area ranges from 120 to 200 meters, the terrain is mainly hilly, and the soil type is red soil.

[0085] The experimental site selected seedlings of Chinese fir and established a pure artificial forest in 1988 with an initial density of 2,500 trees / hectare, followed by three consecutive years of tending. In 2014, tending thinning was carried out, and the retention density of all sample plots was adjusted to 1,350 trees / hectare. In 2015, the cultivated pure Chinese fir forest was transformed into a near-natural forest, and different native tree species were replanted under the Chinese fir plantation, including Phoebe bournei, Taxus wallichiana var. chinensis and Schimasuperba. The initial planting density of the replanted tree species was 900 trees / hectare, and eventually a multi-layer broad-leaved forest of different ages was formed. In 2022, each tree will be measured and surveyed, and the retention density of all sample plots will be maintained at 250-400 trees / hectare.

[0086] In order to study the effects of different tree species and tree species combinations introduced in near-natural transformation on soil quality, we established nine forest plots (20 m × 20 m) transformed with different mixed structures, and set up three pure forest plots (20 m × 20 m) (M0) as the control group. According to the mixed pattern of Chinese fir and other tree species, the experimental plots were divided into three different mixed types (M1-M3). Only Phoebe bournei was introduced in the M1 plot, Phoebe bournei and Taxus chinensis were replanted in the M2 plot, and Phoebe bournei, Taxus chinensis and Schima superba were planted in the M3 plot. The basic characteristics of each plot, including tree species, location, altitude, slope aspect and slope, are shown in Table 2. Note: M0-M3 are different near-natural management modes, M0 is a pure Chinese fir forest, M1 is only Phoebe bournei, M2 is Phoebe bournei and Taxus chinensis, and M3 is Phoebe bournei, Taxus chinensis and Schima superba. The species composition ratio is arranged as Chinese fir: Phoebe bournei: Taxus chinensis: Schima superba.

[0087] Table 2 Overview of near-natural transformation plots in forest farms

[0088] In each standard plot, three small plots were randomly selected in an "S" shape from nine near-natural transformation plots and three pure forest plots. Before sampling, fallen matter and humus on the soil surface were removed.

[0089] A 60-cm-deep vertical soil profile was dug in each small sample plot, and then three soil samples were collected at depths of 0-20 cm, 20-40 cm, and 40-60 cm, respectively. The samples collected from the same soil layer were evenly mixed and represented by T, M, and B. A total of 108 soil samples were collected in the experiment (12 plots × 3 soil layers × 3 repetitions).

[0090] After sampling, the soil was sorted through a 2 mm sieve to remove roots, stones, plant tissues and other debris. Part of the samples were then stored in a refrigerator at 4°C for analysis of the physical and chemical properties of the soil in the laboratory, and the other samples were frozen in liquid nitrogen at -80°C for subsequent genomic and bioinformatics research and analysis.

[0091] The chemical properties of relevant indicators were measured in the laboratory after the soil was air-dried, ground and sieved. The soil organic matter content (SOM) was converted into soil organic carbon content (SOC) using the van Bemmelen factor, and the organic phosphorus (OP) content was calculated by the soil total phosphorus (TP) and inorganic phosphorus (IP) contents.

[0092] Soil stoichiometric ratios were calculated using the measured indicators, namely: soil carbon-nitrogen ratio (C / N), soil carbon-phosphorus ratio (C / P), and soil nitrogen-phosphorus ratio (N / P). Two-way ANOVA was performed on the chemical properties of different soil layers and modes using R (version 4.3.2). Kruskal-Wallis and Tukey-HSD tests were performed on the indicators of different near-natural management modes to explore whether there were significant differences between the modes. Finally, the above content was analyzed and visualized in R.

[0093] like Figure 4 and Figure 5 The box plots of some soil chemical properties and some N, P, K stoichiometric ratios under different transformation modes are shown in Figure 1. Note: In different modes (M0, M1, M2, M3), soil layers T, M and B are shown from left to right.

[0094] The results show that, except for available phosphorus, there are significant differences in soil chemical properties among different modes, and the carbon-nitrogen ratio in stoichiometric ratio is significantly different among modes (P < 0.05). The soil nutrient content decreases with increasing soil depth, and total nitrogen, alkaline nitrogen, available phosphorus, available potassium, pH, nitrogen-phosphorus ratio, and carbon-nitrogen ratio vary significantly in different soil layers (P < 0.05).

[0095] Table 3 Two-factor difference test of soil properties under different transformation modes and soil layers

[0096] Notes: * = p < 0.05, **= p < 0.01, *** = p < 0.001.

[0097] Table 4 Comparison of soil properties between near-natural forest and pure forest

[0098] Note: * = p < 0.05, ** = p < 0.01, *** = p < 0.001.

[0099] Combining Tables 3 and 4, compared with pure Chinese fir forests, the pH, total nitrogen, alkaline nitrogen, total phosphorus, available phosphorus, available potassium, SOC, carbon-nitrogen ratio, and carbon-phosphorus ratio in the three near-natural transformation models increased, while the nitrogen-phosphorus ratio decreased.

[0100] Among the three modes, the average contents of total phosphorus, available phosphorus, total nitrogen, alkaline nitrogen, available potassium, pH, and SOC in the soil surface layer of M2 were the highest. The comparison of soil properties among different near-natural transformation modes in Table 7 showed that the soil alkaline nitrogen content and soil carbon-nitrogen ratio were significantly increased in the three transformation modes (P < 0.05). Compared with M0, the soil total nitrogen and alkaline nitrogen content in M1 mode, the soil total nitrogen, total phosphorus and alkaline nitrogen content in M3 mode, and the soil total phosphorus, alkaline nitrogen, soil pH, and nitrogen-phosphorus ratio in M2 mode changed significantly.

[0101] The changes in tree species in different transformation patterns significantly changed soil properties, especially affecting soil organic matter accumulation and acidity regulation. SOC is a key factor in determining carbon and nutrient cycles, and pH regulates the adsorption and desorption balance of inorganic and organic matter. Both were selected into MDS in this study. Forest stand changes caused significant differences in ecosystem diversity, which was mainly driven by soil pH and SOC. The pH value of red soil in the study area was 4.7-5.2, while the pH range suitable for the growth of Chinese fir was 4.5-6.5. Soil acidification was alleviated after near-natural transformation. pH value mainly affects the changes in soil TP and AN content. The SOC and pH values ​​in near-natural transformed forests, especially in the M2 model, were significantly higher than those in pure forests, and the corresponding TP and AN contents were also significantly increased. In this study, TP was selected as an MDS indicator. AP, TP and N / P were all strongly correlated with different transformation patterns, among which TP and N / P significantly affected soil quality. Phosphorus is essential for energy transfer, photosynthesis and nutrient absorption. A large number of studies have explored the role of phosphorus in soil fertility in Chinese fir forests. Phosphorus deficiency significantly limits the growth and productivity of Chinese fir forests and is a key factor limiting timber production.

[0102] In this study, AP was mainly concentrated in the soil surface and was easily depleted by erosion. As the monoculture cycle increased, the supply of AP decreased and it was difficult to meet the rapid growth needs of Chinese fir species. The introduction of different tree species in the near-natural transformation alleviated the phosphorus limitation in the soil and improved its effectiveness. Overall, the soil quality of the near-natural transformation forest was better than that of the pure forest, especially in M2. The near-natural transformation increased the soil nutrient level, reduced acidification, and enhanced soil productivity and ecological service functions.

[0103] The construction of soil quality evaluation model mainly includes two steps: the selection of soil quality evaluation minimum data set (MDS) and the establishment and calculation of soil quality index (SQI) model. First, the soil nutrient data was standardized, then the Bray-Curtis distance matrix was established, and principal component analysis (PCA) was selected to screen the minimum data set. The components with principal component eigenvalues ​​greater than or equal to 1 were taken as principal components, and soil indicators with factor loadings greater than or equal to 0.3 were selected from them. If the load of the same indicator in both principal components was greater than 0.3, it was classified into the principal component group with the highest corresponding principal component load.

[0104] Table 5 Norm value calculation results

[0105] Grouped by principal component, all indicators with a difference of less than 10% from the highest loading of the group were screened as MDS.

[0106] After the initial screening, correlation analysis was performed on all selected indicators, and the correlation coefficients between them were observed to determine whether there was multicollinearity.

[0107] When multiple variables were retained in a group, the correlations among these indicators were examined, and if the correlation coefficient was small (cor < 0.4), all variables were selected for MDS.

[0108] When the correlation coefficients between different indicators are strong (cor ≥ 0.4), the indicators corresponding to the larger Norm are selected to enter the MDS, and the remaining indicators are discarded.

[0109] Table 6 Correlation coefficients between different indicators

[0110] The final MDS indicators were determined, and weights were assigned to each indicator. The weights of each indicator were calculated using the standardized Bray-Curtis distance matrix. The indicators selected for MDS were TP, AK, pH, and SOC, with corresponding weight values ​​of 0.56, 0.16, 0.26, and 0.02.

[0111] The soil SSF equation was used to calculate the weighted scores of each index.

[0112] Table 7 Weight values ​​of indicators selected into MDS

[0113] Table 8 SQI index values ​​of different soil layers and modes

[0114] The soil quality of the three near-natural transformation models was improved compared with that of pure forest soil. The soil quality index of M2 soil at a depth of 0-20 cm was the highest, the soil quality index of M0 soil at a depth of 40-60 cm was the lowest, and the average soil quality index of M1 was the highest.

[0115] Figure 6 The soil quality index diagram of different transformation modes according to the embodiments of the present application is shown in FIG.

[0116] In this study, all patterns were mixed with Phoebe bournei. The soil quality was further improved after the addition of Taxus chinensis, but it decreased after the addition of Schima superba, so it is of great significance to pay attention to the mixed planting pattern and analyze the soil after the stand structure adjustment.

[0117] Many studies have shown that the rhizosphere soil nutrients increased significantly after the introduction of Phoebe nanmu, which is consistent with our results. The mixed forest formed a more reasonable community vertical structure, and the mutual promotion effect between tree species was more obvious, which was conducive to the circulation of soil nutrients and the decomposition of organic matter. In the M3 mode, TP was higher but AP was the lowest, and the available phosphorus was reduced. Therefore, the phosphorus limitation of the M3 soil microbial community was more serious than that of M1. In the pure fir forest, the introduction of Schima superba aggravated the phosphorus limitation and hindered the circulation and replenishment of soil phosphorus. In the M3 mode, soil SOC, C / N and C / P decreased. These factors may explain why M3 is not as effective as M1 and M2 in improving soil quality.

[0118] The rhizosphere soil of Taxus chinensis showed the highest TP content, total PLFAs and microbial community diversity due to its unique root microecology. This study showed that the M2 model had higher TP and AP contents, reduced microbial phosphorus limitation, increased AN and SOC contents, alleviated phosphorus limitation, and enhanced biochemical cycles. Taxus chinensis in M2 is a nitrogen-fixing tree species that can improve nitrogen supply. Mixed planting of nitrogen-fixing tree species effectively increased soil moisture, TN, TP content, productivity potential and microbial biomass nitrogen. After long-term nitrogen fertilizer application, the C / N and C / P ratios of the fir forest decreased, while the N / P ratio increased. All of the above explain why M2 is superior to M1 in nutrient utilization.

[0119] In summary, M2 is the best mixed planting model, and the introduction of Taxus chinensis and Phoebe bournei are the best tree species choices for near-natural transformation of Chinese fir plantations. In-depth research on tree species selection will help to effectively apply these models for forest transformation in the future.

[0120] Example 3 The present embodiment provides a system for selecting Chinese fir species for near-natural transformation. For details not disclosed in the system for selecting Chinese fir species for near-natural transformation of the present embodiment, please refer to the specific implementation contents of the method for selecting Chinese fir species for near-natural transformation based on soil quality index analysis in other embodiments.

[0121] Figure 7 The structure schematic diagram of the Chinese fir near-natural transformation tree species selection system according to an embodiment of the present application is shown in FIG.

[0122] like Figure 7 As shown, the Chinese fir near-natural transformation tree species selection system includes a soil chemical composition detection module 10, a soil quality evaluation model 20 and a tree species selection module 30.

[0123] Specifically described, A soil chemical composition detection module 10 is used to detect the chemical composition data of the soil sample under different near-natural transformation modes, and calculate the stoichiometric ratio of the soil according to the chemical composition data; A soil quality evaluation model 20, for selecting a minimum data set (MDS) for soil quality evaluation according to the chemical composition data and stoichiometric ratio of the soil, and establishing a soil quality index (SQI) model and calculating the soil quality index of the soil sample under different near-natural transformation modes; The tree species selection module 30 is used to select the Chinese fir near-natural transformation tree species according to the soil quality index under different near-natural transformation modes.

[0124] The Chinese fir near-natural transformation tree species selection system based on soil quality index analysis of the present application is used to select Chinese fir near-natural transformation tree species by quantifying the changes in soil quality under different transformation modes. The method is to deeply analyze the changes in key indicators such as soil physical and chemical properties after Chinese fir is mixed with different native tree species, identify the tree species combination that is most conducive to improving soil fertility, and formulate the best Chinese fir near-natural transformation management model accordingly.

[0125] This application plan not only focuses on the ecological balance of artificial forests and the sustainable improvement of soil quality, but also covers the technical means of transforming Chinese fir artificial forests by introducing native tree species. It aims to improve soil fertility, ecosystem stability and productivity, thereby achieving sustainable development of forest land.

[0126] Example 4 The present embodiment provides a device for selecting a near-naturally transformed Chinese fir tree species. For details not disclosed in the device for selecting a near-naturally transformed Chinese fir tree species in the present embodiment, please refer to the specific implementation contents of the near-naturally transformed Chinese fir tree species selection method or system based on soil quality index analysis in other embodiments.

[0127] Figure 8 Schematic diagram of the structure of the device 400 for selecting a tree species for near-natural transformation of Chinese fir according to an embodiment of the present application is shown.

[0128] like Figure 8 As shown, the device 400 for selecting near-naturally transformed Chinese fir tree species includes: a storage unit 402: used to store executable instructions; and a processing unit 401: used to connect to the storage unit 402 to execute the executable instructions to complete the near-naturally transformed Chinese fir tree species selection method based on soil quality index analysis.

[0129] Those skilled in the art will understand that Figure 8 It is merely an example of the device 400 for selecting a tree species for near-natural transformation of Chinese fir and does not constitute a limitation of the device 400 for selecting a tree species for near-natural transformation of Chinese fir. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the device 400 for selecting a tree species for near-natural transformation of Chinese fir may also include input and output devices, network access devices, buses, etc.

[0130] The processing unit 401 (Central Processing Unit, CPU) may also be other general processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general processor may be a microprocessor or the processing unit 401 may also be any conventional processor, etc. The processing unit 401 is the control center of the Chinese fir near-natural transformation tree species selection device 400, and uses various interfaces and lines to connect various parts of the entire Chinese fir near-natural transformation tree species selection device 400.

[0131] The storage unit 402 can be used to store computer-readable instructions. The processing unit 401 realizes various functions of the Chinese fir near-natural transformation tree species selection device 400 by running or executing the computer-readable instructions or modules stored in the storage unit 402 and calling the data stored in the storage unit 402. The storage unit 402 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required by at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the Chinese fir near-natural transformation tree species selection device 400, etc. In addition, the storage unit 402 can include a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, a read-only memory (Read-Only Memory, ROM), a random access memory (Random Access Memory, RAM) or other non-volatile / volatile storage devices.

[0132] If the modules integrated in the device 400 for selecting tree species for near-natural transformation of Chinese fir are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also instruct the relevant hardware to complete through computer-readable instructions. The computer-readable instructions can be stored in a computer-readable storage medium, and when the computer-readable instructions are executed by the processor, the steps of each of the above-mentioned method embodiments can be implemented.

[0133] Example 5 This embodiment provides a computer-readable storage medium having a computer program stored thereon; the computer program is executed by a processor to implement the method for selecting tree species for near-natural transformation of Chinese fir based on soil quality index analysis in other embodiments.

[0134] Those skilled in the art will appreciate that the terms used in the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The singular forms of "a", "said" and "the" used in the present invention and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0135] It should be understood that although the terms first, second, third, etc. may be used in the present invention to describe various information, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present invention, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0136] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0137] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for selecting Chinese fir species for near-natural transformation based on soil quality index analysis, characterized in that: include: According to different tree species and mixed proportions, the fir plantations are divided into a variety of near-natural transformation models; Collect soil samples in the sample plots of the various near-natural transformation models; detecting chemical composition data of the soil samples under different near-natural transformation modes, and calculating the stoichiometric ratio of the soil according to the chemical composition data; A minimum data set (MDS) for soil quality evaluation was selected based on the chemical composition data and stoichiometric ratio of the soil, and a soil quality index (SQI) model was established to calculate the soil quality index of the soil samples under different near-natural transformation modes; The tree species for near-natural transformation of Chinese fir are selected according to the soil quality index under different near-natural transformation modes.

2. The method for selecting Chinese fir species for near-natural transformation according to claim 1, characterized in that: The collecting of soil samples in the sample plots of the multiple near-natural transformation modes includes: Remove the surface litter and humus layer and conduct distributed sampling along an "S"-shaped sampling path; At the sampling point, the soil profile is divided into three soil layers according to depth, including a surface soil layer, a middle soil layer and a bottom soil layer, and soil samples are collected from the three soil layers respectively; The soil samples were subjected to preliminary processing and storage treatment.

3. The method for selecting Chinese fir species for near-natural transformation according to claim 1, characterized in that: The measuring of the chemical composition data of the soil samples under different near-natural transformation modes and calculating the stoichiometric ratio of the soil according to the chemical composition data include: Accurately measure soil organic matter, total phosphorus, inorganic phosphorus and / or organic phosphorus to obtain chemical composition data; The stoichiometric ratio of the soil is calculated based on various chemical component data in the soil; the stoichiometric ratio includes the relative proportions between the three major elements of carbon, nitrogen and phosphorus in the soil.

4. The method for selecting Chinese fir species for near-natural transformation according to claim 1, characterized in that: The soil quality evaluation minimum data set (MDS) is selected based on the chemical composition data and stoichiometric ratio of the soil, including: The collected soil nutrient data need to be standardized; The Bray-Curtis distance matrix was used to calculate the similarity between soil samples; The original minimum data set (MDS) was screened out by the principal component analysis (PCA) method; The comprehensive load (Norm) of each indicator is calculated by the comprehensive load (Norm) formula, and the original minimum data set (MDS) is filtered according to the size of the comprehensive load (Norm) of each indicator to obtain the final minimum data set (MDS); The comprehensive load (Norm) formula is as follows: ； Among them, N ik is the comprehensive loading of each variable on the principal component k with eigenvalue ≥ 1; is the loading of the variable on principal component k, λ k is the eigenvalue of the kth principal component.

5. The method for selecting Chinese fir species for near-natural transformation according to claim 4, characterized in that: The following steps are also included: The correlation coefficients between the selected indicators in the final minimum data set (MDS) were evaluated using correlation analysis methods; If the correlation coefficient is less than the threshold, the corresponding indicator is retained in the minimum data set (MDS); If the correlation coefficient is greater than or equal to the threshold, only the indicators with larger comprehensive loadings are retained in the minimum data set (MDS), and the rest are discarded.

6. The method for selecting Chinese fir species for near-natural transformation according to claim 1, characterized in that: The method of establishing a soil quality index (SQI) model and calculating the soil quality index of the soil samples under different near-natural transformation modes includes: The weight of each indicator in the minimum data set (MDS) is calculated by using the standardized Bray-Curtis distance matrix; The weighted scores of each index were calculated using the soil standard scoring functions (SSF) formula; According to the weights and scores of the above indicators, the soil quality index (SQI) formula is used to calculate the soil quality index of the soil sample. The soil quality index (SQI) formula is: ； Among them, W i is the weight of the i-th indicator, Q i is the index score, n is the number of parameters in the small data set (MDS); The higher the soil quality index (SQI) value obtained by the formula, the better the soil quality and the stronger the soil fertility.

7. The method for selecting Chinese fir species for near-natural transformation according to claim 6, characterized in that: The soil standard scoring functions (SSF) formula is used to calculate the weighted scores of each index, including: The SSF (excluding pH) calculation formula is as follows: （1）； In formula (1), x is the measured value of the indicator; f(x) is the score of the indicator, ranging from 0.1 to 1.0; x1 and x2 are the lower and upper limits of the indicator, respectively; （2）； In formula (2), x is the monitoring value of the indicator; f(x) is the score of the indicator; x 1、 x 2、 x 3、 x4 is the set threshold.

8. A system using the method for selecting Chinese fir species for near-natural transformation according to any one of claims 1 to 7, characterized in that: include: A soil chemical composition detection module is used to detect the chemical composition data of the soil samples under different near-natural transformation modes, and calculate the stoichiometric ratio of the soil based on the chemical composition data; A soil quality evaluation model is used to select a minimum data set (MDS) for soil quality evaluation based on the chemical composition data and stoichiometric ratio of the soil, establish a soil quality index (SQI) model, and calculate the soil quality index of the soil samples under different near-natural transformation modes; The tree species selection module is used to select the Chinese fir near-natural transformation tree species according to the soil quality index under different near-natural transformation modes.

9. A device for selecting tree species for near-natural transformation of Chinese fir, characterized in that: include: A storage unit for storing executable instructions; as well as A processing unit, configured to be connected to the memory to execute executable instructions to complete the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon; the computer program is executed by a processor to implement the method according to any one of claims 1 to 7.

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