A meta-analysis-based parameter extraction method for soil conditioning measures

Abstract

The application provides a soil regulation measure parameter extraction method based on Meta analysis, and belongs to the technical field of water and soil conservation. The method screens relevant literatures from a database, and obtains experimental data in the relevant literatures by using a GetData tool, takes OpenMEE software as a tool to quantize logarithmic response rates of soil regulation measures to rainfall-runoff and soil moisture under different environmental factor conditions, then analyzes average effect values of the soil regulation measures to key water cycle elements, and quantizes influence contribution rate sizes of the soil regulation measures to rainfall-runoff and soil moisture amplitude changes of a slope surface before and after construction of typical soil regulation measures. The application makes exploration for improving farmland soil structure and reducing water and soil loss, and promoting water and soil conservation and sustainable development of agriculture.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of water and soil protection, and particularly relates to a soil regulation measure parameter extraction method based on Meta analysis. BACKGROUND

[0002] At present, under the influence of climate change and human activities, global water cycle processes are intensified, natural rhythms are changed, and extreme hydrological events such as drought and flood occur frequently, which brings serious challenges to regional water resources security.

[0003] At present, in order to alleviate the above problems, scholars have carried out a lot of exploration. Typical soil regulation measures such as deep ploughing / cultivation, straw returning, biochar addition, ridge and furrow and terrace can effectively improve the water storage capacity of soil, realize the goal of peak reduction and dryness reduction in a year, improve water use efficiency and promote vegetation growth, so as to reduce the risk of drought.

[0004] However, the current research on soil regulation measures is mostly concentrated on the field scale, and the applicability and application effect of each soil regulation measure in complex environment are not clear, so corresponding research is urgently needed. SUMMARY

[0005] In view of the above problems in the prior art, the application provides a soil regulation measure parameter extraction method based on Meta analysis, which obtains a large amount of experimental data through literature research, and carries out statistical analysis to extract the influence parameters of soil regulation measures on key water cycle processes.

[0006] In order to achieve the above purpose, the technical scheme adopted by the application is:

[0007] The present application provides a soil regulation measure parameter extraction method based on Meta analysis, comprising the following steps:

[0008] S1, based on the existing soil regulation measures, searching the related literature and recording the key words, finding the correlation between the research hotspots, so as to determine the correlation between the soil regulation measures and the soil parameters;

[0009] S2, based on the correlation between the soil regulation measures and the soil parameters, taking the soil regulation measures as the theme key words and taking the soil parameters or research hotspots as the secondary key words, searching the related information of the influence of the soil regulation measures on the key elements of water cycle process;

[0010] S3, setting the screening conditions, and screening the literature and test point samples required by the soil regulation parameters in the related information;

[0011] S4, integrate different environmental factors, and based on climate zoning, classify and arrange the required literature and test point samples to obtain experimental sample data;

[0012] S5, according to the experimental sample data, obtain experimental data between the soil regulation measure experimental group and the related blank treatment group, and construct a Meta-analysis database;

[0013] S6, based on the Meta-analysis database, quantifying the logarithmic response rate LRR of the soil regulation measures to the key elements of the water cycle process under different environmental factors, and obtaining the average effect value of the influence of the soil regulation measure construction on the key elements of the water cycle process;

[0014] S7, according to the average effect value, analyzing the influence of different soil regulation measure constructions on the slope rainfall-runoff process and different soil layers of soil moisture;

[0015] S8, according to the analysis result, performing significance test on the average effect value, and quantifying the influence contribution rate of different environmental factors on the slope rainfall-runoff and soil moisture amplitude before and after the soil regulation measure construction, and completing the extraction of the soil regulation measure parameters.

[0016] The beneficial effects of the present application are: the present application extracts related soil parameters based on soil regulation measures through literature research, quantifies the influence degree and contribution rate of different soil regulation measures on key water cycle processes, identifies the regulation and storage influence mechanism of soil regulation measures on key water cycle processes, and completes the influence degree research of soil regulation measures on key water cycle processes. The present application makes exploration for improving farmland soil structure and reducing water and soil loss, promoting water and soil conservation and sustainable agricultural development.

[0017] Further, the step S1 comprises the following steps:

[0018] S101, based on existing soil regulation measures, retrieving and recording keywords;

[0019] S102, setting a time span, setting the keyword appearance frequency to n times, retrieving and recording the keywords;

[0020] S103, using VOSviewer to perform coupling cluster analysis on the keywords in each database, and automatically clustering into k populations, and differentiating the relevance of research hotspots by different colors;

[0021] S104, visualizing the coupling clustering analysis result, finding the relevance between the research hotspots, and determining the relevance between the soil conditioning measures and the soil parameters.

[0022] The beneficial effect of the further scheme is that the relevance between the soil conditioning measures and the soil parameters is determined based on the soil conditioning measures and the existing soil conditioning measures, the published literature is determined, the search keywords are determined, and the direction of the literature search in step S2 is indicated.

[0023] Further, the step S2 comprises the following steps:

[0024] S201, based on the relevance between the soil conditioning measures and the soil parameters, taking the soil conditioning measures as the theme keywords and taking the soil parameters or research hotspots as the secondary keywords;

[0025] S202, according to the theme keywords and the secondary keywords, retrieving the relevant information about the influence of the soil conditioning measures on the key element processes of the water cycle, and eliminating the repeated information.

[0026] The beneficial effect of the further scheme is that the keywords are determined and the relevant literature is downloaded by determining the relevance between the soil conditioning measures and the soil parameters.

[0027] Further, the step S5 comprises the following steps:

[0028] S501, reading the pictures of the screened experimental sample data by using the GetData tool, and obtaining the experimental data of the soil conditioning measure experimental group and the related blank control group;

[0029] S502, constructing a Meta-analysis database according to the experimental data.

[0030] The beneficial effect of the further scheme is that the data support is provided for the subsequent analysis of the influence of the soil conditioning measures on the soil parameters and the key water cycle processes by collecting and sorting the data from the downloaded literature.

[0031] Further, the step S6 comprises the following steps:

[0032] S601, based on the Meta-analysis database, quantifying the logarithmic response rate LRR of the soil conditioning measures to the key element processes of the water cycle under different environmental factor conditions by using the OpenMEE software as a tool and by using the obtained experimental data;

[0033] S602, according to the logarithmic response rate calculated in step S601, establishing a random effect model by using a maximum likelihood method;

[0034] S603. Based on the random effect model, the average effect value of the impact of soil conditioning measures on the key elements of the water cycle is obtained.

[0035] Furthermore, the expression of the logarithmic response rate is as follows:

[0036]

[0037] Where LRR stands for logarithmic response rate, and are the means of water cycle process indices for the single-sample typical soil conditioning measures construction group and the traditional tillage control group, respectively, and ln(·) represents the logarithmic function.

[0038] The beneficial effect of the above further scheme is: based on the data obtained from the literature, by calculating the logarithmic response rate (LRR) of the effects of typical soil conditioning measures on soil water retention and water conductivity characteristics and their effects on soil water and rainfall-runoff processes, the effect value of soil conditioning measures on key elements of the water cycle under different environmental factors is quantified, and the average effect value is obtained. This is conducive to further theoretical analysis.

[0039] Furthermore, step S7 includes the following steps:

[0040] S701, based on the average effect size According to different environmental factors, the effects of different soil conditioning measures on the slope rainfall-runoff process were analyzed and compared;

[0041] S702. Based on the average effect value and different environmental factors, analyze and compare the impact of different soil conditioning measures on soil moisture in different soil layers.

[0042] The beneficial effects of the above further scheme are: through different environmental factors, the effect values ​​of soil conditioning measures on soil water retention and water conductivity characteristics and their impact on key water cycle elements such as soil water and slope rainfall-runoff are calculated, and the impact of soil conditioning measures on soil water retention and water conductivity characteristics and their impact on water cycle such as soil water and rainfall-runoff is compared and analyzed.

[0043] Furthermore, step S8 includes the following steps:

[0044] S801. Based on the analysis results, use variance to conduct a significance test on the average effect value;

[0045] S802. Based on the significance test results, a linear model was established using SPSS software;

[0046] S803, according to the linear model, quantifying the contribution rate of different environmental factors to the slope rainfall-runoff and soil moisture amplitude before and after the construction of the soil regulation measure, and extracting the parameters of the soil regulation measure.

[0047] The beneficial effect of the further scheme is that the contribution rate of different environmental factors to the slope rainfall-runoff and soil moisture amplitude before and after the construction of the soil regulation measure is quantified, the influence degree of different environmental factors on the soil moisture regulation capacity before and after the construction of the typical soil regulation measure is compared and analyzed, and the influence degree of the soil regulation measure on the key water cycle process is researched.

[0048] Further, the expression of the contribution rate is as follows:

[0049] CT i =(SS 因 -f 因 ×MSE) / SST

[0050] Wherein, CT i represents the contribution rate of the i environmental factor to the change of the average effect value of the soil regulation measure construction on the key element process of water cycle, SS 因 represents the sum of squares of deviations of the experimental sample data under the influence of the i environmental factor, f represents the degree of freedom of the i environmental factor, MSE represents the root mean square error of the experimental sample data under the influence of the i environmental factor, and SST represents the total sum of squares of deviations of the experimental sample data under the influence of each environmental factor and the interaction thereof.

[0051] The beneficial effect of the further scheme is that the contribution rate parameters are extracted to reflect the influence of the soil regulation measure on the soil regulation under different environmental conditions, and the influence weight of each environmental factor on the target is determined when the subsequent target scheme is optimized. BRIEF DESCRIPTION OF DRAWINGS

[0052] Figure 1 The method flowchart of the present application. DETAILED DESCRIPTION

[0053] The specific embodiments of the present application are described below to facilitate the understanding of the present application by those skilled in the art, but it should be clear that the present application is not limited to the scope of the specific embodiments, and for those skilled in the art, it is obvious that various changes are within the spirit and scope of the present application defined and determined by the appended claims, and all the inventions utilizing the concept of the present application are within the scope of protection.

[0054] EMBODIMENT

[0055] The application provides a soil regulation measure parameter extraction method based on Meta analysis, through literature research and screening, relevant experimental data in the literature is obtained by using Getdate software, based on the Meta-analysis method, the OpenMEE software is used as a tool to quantify the influence effect value size of typical soil regulation measure construction on the key element process of water cycle under different environmental factor conditions, and the influence mechanism is identified. Figure 1 As shown in the application, a soil regulation measure parameter extraction method based on Meta analysis is provided, and the implementation method is as follows:

[0056] S1, based on the existing soil regulation measures, relevant literature is searched and key words are recorded, the correlation between research hotspots is found, and the correlation between the soil regulation measures and the soil parameters is determined, and the implementation method is as follows:

[0057] S101, based on the existing soil regulation measures, key words are searched and recorded;

[0058] S102, a time span is set, the frequency of the key words is set to n times, the key words are searched and recorded;

[0059] S103, VOSviewer is used to perform coupling cluster analysis on the key words in each database, and the key words are automatically clustered into k groups, and different colors are used to distinguish the correlation between research hotspots;

[0060] S104, the coupling cluster analysis result is visualized, the correlation between research hotspots is found, and the correlation between the soil regulation measures and the soil parameters is determined.

[0061] In this embodiment, combined with the existing soil regulation measures such as straw returning, biochar, furrow, subsoiling / cultivation and terrace, the Web of Science core collection is used as a literature retrieval database, and "Subsoiling, Straw, Biochar, Ridge, Terrace, Soil and Water Conservation and Best Management Practices" are used as the basic retrieval conditions of the subject retrieval, and "Water cycle, Soil, Physical, Runoff, Topography" are used as the secondary retrieval conditions.

[0062] In this embodiment, the time span is set to 1900-2020, the frequency of the key words is set to 80 times, 1947 relevant literatures are searched, and 6458 key words are recorded.

[0063] In this embodiment, the frequency of occurrence is not less than 30 times as the threshold, the coupling clustering analysis of each keyword is carried out by using VOSviewer, and the visual processing is carried out, 62 keywords meeting the conditions are obtained, and the 62 keywords are automatically clustered into 4 groups, which are distinguished by different colors, and the relevance of research hotspots is indicated.

[0064] In this embodiment, the cluster analysis result shows that the blue group indicates that the current research on soil regulation measures mainly uses the simulated rainfall method, and focuses on the influence of the simulated rainfall method on runoff, infiltration and soil erosion, and more experiments are carried out in the Loess Plateau; the red group indicates that the related research focuses on the influence of the construction of soil regulation measures on soil physical properties including hydraulic conductivity, organic matter composition and soil compaction.

[0065] S2, based on the relevance of soil regulation measures and soil parameters, taking soil regulation measures as the theme keyword and taking soil parameters or research hotspots as the secondary keyword, searching the related information of the influence of soil regulation measures on the key element process of water cycle, and the implementation method is as follows:

[0066] S201, based on the relevance of soil regulation measures and soil parameters, taking soil regulation measures as the theme keyword and taking soil parameters or research hotspots as the secondary keyword;

[0067] S202, according to the theme keyword and the secondary keyword, searching the related information of the influence of soil regulation measures on the key element process of water cycle, and eliminating the repeated information.

[0068] In this embodiment, based on the relevance of specific measures of soil regulation measures and soil parameters, taking “terraces, ridge cultivation, horizontal ridge, deep loosening / cultivation, straw returning and biochar” as the theme keyword, and containing “soil moisture” or “runoff” or “soil physical properties” or “soil hydraulic characteristics” as the search keyword. Through clustering analysis structure, the soil regulation measures such as terraces, ridge cultivation, horizontal ridge, deep loosening / cultivation, straw returning and biochar and the soil physical parameters such as “soil moisture” or “runoff” or “soil physical properties” or the key water cycle process are set as keywords for searching.

[0069] In this embodiment, based on the determined search keywords, the related academic papers and journals of the influence of soil regulation measures construction on the key element process of water cycle in 2000-2020 are searched in Wanfang, China National Knowledge Infrastructure (CNKI), Web of Science and Elsevier Science Direct database.

[0070] In this embodiment, Endnote is used for preliminary screening, and the repeated downloaded literatures are removed, and a total of 40280 academic papers and journals related to the influence of typical soil regulation measures on the key element process of water cycle in 2000-2020 are obtained.

[0071] S3, set the screening conditions, and screen the literature and test point samples required for the soil regulation parameters in the related information;

[0072] In this embodiment, first, based on the downloaded related theses and journals, the corresponding screening conditions are set according to the specific research content. In order to study the influence of soil regulation measures on the key water cycle process, the screening conditions are set as follows: ① in the literature, the influence of the construction of typical soil regulation measures on the soil physical properties or the key element processes of soil water and runoff generation is quantified by means of field prototype observation or controlled experiment, and the traditional flat cultivation (without measures) is taken as the control; ② the data records of at least one group of indicators including soil physical properties, soil water or rainfall-runoff changes before and after the construction of facilities; ③ the records including the number of experiment or sampling repetitions, mean and standard deviation of indicators; ④ the records including the latitude and longitude of the experimental site, multi-year average precipitation, soil texture, land use, slope of the experimental plot and tillage method; ⑤ the experimental points are mainly located in China, and the soil texture and underlying surface characteristics are similar to those in the Sihe River Basin.

[0073] Secondly, the literature and test point samples required for the related soil regulation parameters are further selected according to the above screening conditions and the abstracts or full texts in the literature. In this study, a total of 417 related literatures are screened, and a total of 3062 experimental point samples are obtained.

[0074] S4, the experimental sample data are classified and arranged based on the division of climate zones, and the required literature and test point samples are classified and arranged, and the experimental sample data are obtained;

[0075] In this embodiment, different environmental factors are considered, and the information of climate, geology and soil in the literature is arranged and classified based on the required literature data requirements: the classified and arranged related literatures are classified and arranged, and the specific binary classification conditions are the information of experimental point latitude and longitude, multi-year average rainfall, soil texture, land use, slope and tillage method.

[0076] S5, according to the experimental sample data, the experimental data between the experimental group of soil regulation measures and the related blank treatment group are obtained, and a Meta-analysis database is constructed, and the implementation method is as follows:

[0077] S501, the pictures of the screened experimental sample data are read by using the GetData tool, and the experimental data of the experimental group of soil regulation measures and the related blank treatment group are obtained;

[0078] S502, according to the experimental data, a Meta-analysis database is constructed.

[0079] In this embodiment, the experimental sample data after sorting and classification is used to obtain the experimental data related to soil physical properties, soil water and rainfall-runoff process between the experimental group of soil regulation measures and the related blank control group by using GetData tool. The blank control group refers to the traditional farming (without treatment) farmland, and the experimental group refers to the farmland with soil regulation measures construction.

[0080] In this embodiment, the experimental data related to soil physical properties, soil water and rainfall-runoff process of the control group and the typical soil regulation measures construction treatment group are obtained by using GetData tool, including mean, number of experiments and standard deviation. At the same time, the Meta-analysis database is established based on the data obtained from the existing literature.

[0081] S6, based on the Meta-analysis database, the logarithmic response rate (LRR) of the soil regulation measures to the key elements of water cycle process under different environmental factor conditions is quantified by the obtained experimental data, and the average effect value of the soil regulation measures construction on the key elements of water cycle process is obtained The implementation method is as follows:

[0082] S601, based on the Meta-analysis database, the logarithmic response rate (LRR) of the soil regulation measures to the key elements of water cycle process under different environmental factor conditions is quantified by the obtained experimental data, and the average effect value of the soil regulation measures construction on the key elements of water cycle process is obtained

[0083] S602, according to the logarithmic response rate calculated in step S601, a random effect model is established by maximum likelihood method;

[0084] S603, according to the random effect model, the average effect value of the soil regulation measures construction on the key elements of water cycle process is obtained

[0085] In this embodiment, based on the experimental data obtained by the above process, the OpenMEE software is used as a tool to quantify the influence effect value of the soil regulation measures on the key elements of water cycle process under different environmental factor conditions.

[0086] In this embodiment, based on the Meta-analysis method, the OpenMEE software is used as a tool to quantify the influence effect value of the typical soil regulation measures construction on the key elements of water cycle process under different environmental factor conditions.

[0087] In this embodiment, the random effect model is established by maximum likelihood method to calculate the logarithmic response rate (LRR) of the typical soil regulation measures construction to the soil water holding and water conducting characteristics index and its influence on the key elements of water cycle process such as soil water and slope rainfall-runoff, and the average effect value calculation method is as follows:

[0088]

[0089] wherein X t and respectively represent the mean value of water cycle element process index of single sample typical soil regulation measure construction treatment group and traditional tillage (no measure) control group, LRR represents the logarithmic response rate, and ln(·) represents a logarithmic function.

[0090] In this embodiment, the logarithmic response rate (LRR) which is a commonly used index of the Meta-analysis method is taken as the effect value of the typical soil regulation measure construction on the soil water holding and water conducting properties and its influence on the soil water and the rainfall-runoff and other key element processes of water cycle, and then the average effect value is calculated by the random effect model established by the maximum likelihood method When the value is negative, it indicates that the soil regulation measure reduces the corresponding index value. Here, the effect value of the soil regulation measure facility on the index element is expressed in the form of percentage

[0091] S7, according to the average effect value, the influence of different soil regulation measure constructions on the rainfall-runoff process of slope and the soil moisture of different soil layers is analyzed, and the implementation method is as follows:

[0092] S701, according to the average effect value, the influence of different soil regulation measure constructions on the rainfall-runoff process of slope is analyzed and compared according to different environmental factors;

[0093] S702, according to the average effect value, the influence of different soil regulation measure constructions on the soil moisture of different soil layers is analyzed and compared according to different environmental factors.

[0094] In this embodiment, based on the effect value of the soil regulation measure construction on the soil water holding and water conducting properties and its influence on the soil water and the rainfall-runoff and other key element processes of water cycle, the influence of different soil regulation measure constructions on the rainfall-runoff process and the soil moisture of different soil layers is analyzed and compared.

[0095] In this embodiment, based on the average effect value of the soil regulation measure on the soil water holding and water conducting properties and its influence on the soil water and the rainfall-runoff and other key element processes of water cycle, the influence of different soil regulation measure constructions on the rainfall-runoff process is analyzed and compared according to the situation division of different environmental factors.

[0096] In this embodiment, based on the effect value of the soil regulation measure on the soil water holding and water conducting properties and its influence on the soil water and the rainfall-runoff and other key element processes of water cycle, the influence of different soil regulation measure constructions on the soil moisture of different soil layers is analyzed and compared according to the situation division of different environmental factors. ​​

[0097] S8、According to the analysis result, the average effect value is subjected to significance test, and the contribution rate of different environmental factors to the influence of soil regulation measures on the slope rainfall-runoff and soil moisture amplitude before and after the construction of soil regulation measures is quantified, and the parameter extraction of soil regulation measures is completed, and the implementation method is as follows:

[0098] S801、According to the analysis result, the average effect value is subjected to significance test by using variance;

[0099] S802、According to the significance test result, a linear model is established by using SPSS software;

[0100] S803、According to the linear model, the contribution rate of different environmental factors to the influence of soil regulation measures on the slope rainfall-runoff and soil moisture amplitude before and after the construction of soil regulation measures is quantified, and the parameter extraction of soil regulation measures is completed.

[0101] In this embodiment, the calculated LRR value is subjected to significance test. A general linear model is established by using SPSS software, and the contribution rate of different environmental factors to the influence of typical soil regulation measures on the slope rainfall-runoff and soil moisture amplitude before and after the construction of soil regulation measures is quantified according to the situation of different environmental factors, and the mechanism identification of the regulation and storage influence of soil regulation measures on key water cycle processes is completed.

[0102] In this embodiment, single factor variance analysis (ANOVA) is used to compare the difference of the average effect value of the influence of typical soil regulation measures construction on key element process indicators of water cycle under different climate and underlying surface conditions, and P<0.05 is taken as the standard for judging significant difference.

[0103] In this embodiment, a general linear model is established based on SPSS software tool, and the contribution rate of each factor and its interaction to the change of the average effect value of the influence of typical soil regulation measures construction on key element processes of water cycle is quantified by using formula. The contribution rate of the influencing factor of the regulation and storage ability of soil regulation measures on key water cycle processes is analyzed and compared, and the mechanism identification of the regulation and storage influence of soil regulation measures on key water cycle processes is completed. The calculation formula is as follows:

[0104] CT i =(SS 因 -f 因 ×MSE) / SST

[0105] Wherein, CT i represents the contribution rate of i environmental factor to the change of the average effect value of the influence of soil regulation measures construction on key element processes of water cycle, SS 因SST is the total sum of squares of the experimental sample data under the influence of each environmental factor and the interaction thereof.

[0106] The application identifies the influence of the construction of typical soil regulation measures facilities on the water cycle element process in a manner of literature research, and integrates key parameters; secondly, the influence of the construction of typical soil regulation measures under the influence of different environmental factors on key water cycle elements is analyzed, and the influence contribution rate of the construction of typical soil regulation measures before and after the slope rainfall-runoff and the soil moisture amplitude is quantified, so as to make exploration for improving farmland soil structure and reducing water and soil loss, promoting water and soil conservation and sustainable development of agriculture.

Claims

1. A soil conditioning measure parameter extraction method based on Meta-analysis, characterized in that: The following steps are involved: S1. Based on existing soil conditioning measures, search for relevant literature and record keywords to find the correlation between research hotspots to determine the correlation between soil conditioning measures and soil parameters; S2. Based on the correlation between soil conditioning measures and soil parameters, soil conditioning measures were used as the main keyword, and soil parameters or research hotspots were used as secondary keywords to retrieve relevant information on the impact of soil conditioning measures on key elements of the water cycle. S3. Set screening conditions and select the literature and test point samples required for soil adjustment parameters from the relevant information; S4. Comprehensively consider different environmental factors and classify the required literature and test site samples based on climate zone division to obtain experimental sample data; S5. Based on the experimental sample data, obtain the experimental data between the soil conditioning measure experimental group and the relevant blank treatment group, and build a meta-analysis database; S6. Based on the Meta-analysis database, quantify the logarithmic response rate (LRR) of soil regulation measures to key water cycle processes under different environmental factors through the obtained experimental data, and obtain the average effect value of the impact of soil regulation measures on key water cycle processes; S7. Based on the average effect value, analyze the impact of different soil conditioning measures on the slope rainfall-runoff process and soil moisture in different soil layers; S8. Based on the analysis results, the significance test of the average effect value is conducted, and the contribution rate of different environmental factors to the slope rainfall-runoff and soil moisture variation before and after the construction of soil conditioning measures is quantified to complete the extraction of soil conditioning measure parameters.

2. The soil conditioning measure parameter extraction method based on Meta analysis according to claim 1 is characterized in that: The step S1 comprises the following steps: S101. Search and record keywords based on existing soil conditioning measures; S102, set the time span, set the keyword occurrence frequency to n times, search and record the keywords; S103. Use VOSviewer to perform coupled cluster analysis on the keywords in each database, automatically cluster them into k groups, and use different colors to represent the relevance of research hotspots; S104. Visualize the results of the coupled cluster analysis to find the correlation between research hotspots and determine the correlation between soil conditioning measures and soil parameters.

3. The soil conditioning measure parameter extraction method based on Meta analysis according to claim 1 is characterized in that: The step S2 comprises the following steps: S201. Based on the correlation between soil conditioning measures and soil parameters, soil conditioning measures are used as the main keywords, and soil parameters or research hotspots are used as secondary keywords; S202. Retrieve relevant information on the impact of soil conditioning measures on key elements of the water cycle based on the subject keywords and secondary keywords, and eliminate duplicate information.

4. The soil conditioning measure parameter extraction method based on Meta-analysis according to claim 1 is characterized in that: The step S5 comprises the following steps: S501, using the GetData tool to read the image of the screened experimental sample data to obtain the experimental data of the soil conditioning measure experimental group and the relevant blank control group; S502. Construct a meta-analysis database based on experimental data.

5. The soil conditioning measure parameter extraction method based on Meta analysis according to claim 1 is characterized in that: The step S6 comprises the following steps: S601. Based on the Meta-analysis database and experimental data, the OpenMEE software was used to quantify the logarithmic response rate (LRR) of soil conditioning measures to key elements of the water cycle under different environmental factors. S602. Establishing a random effects model using the maximum likelihood method based on the logarithmic response rate calculated in step S601; S603. Based on the random effect model, the average effect value of the impact of soil conditioning measures on the key elements of the water cycle is obtained.

6. The soil conditioning measure parameter extraction method based on Meta-analysis according to claim 5 is characterized in that: The expression for the logarithmic response rate is as follows: Where LRR stands for logarithmic response rate, and are the means of water cycle process indices for the single-sample typical soil conditioning measures construction group and the traditional tillage control group, respectively, and ln(·) represents the logarithmic function.

7. The soil conditioning measure parameter extraction method based on Meta-analysis according to claim 1 is characterized in that: The step S7 comprises the following steps: S701, based on the average effect size According to different environmental factors, the effects of different soil conditioning measures on the slope rainfall-runoff process were analyzed and compared; S702. Based on the average effect value and different environmental factors, analyze and compare the impact of different soil conditioning measures on soil moisture in different soil layers.

8. The soil conditioning measure parameter extraction method based on Meta-analysis according to claim 1 is characterized in that: The step S8 comprises the following steps: S801. Based on the analysis results, use variance to conduct a significance test on the average effect value; S802. Based on the significance test results, a linear model was established using SPSS software; S803. Based on the linear model, quantify the contribution rate of different environmental factors to slope rainfall-runoff and soil moisture variation before and after the construction of soil conditioning measures, and complete the extraction of soil conditioning measure parameters.

9. The soil conditioning measure parameter extraction method based on Meta-analysis according to claim 8, characterized in that: The expression of the impact contribution rate is as follows: CT i =(SS 因 -f 因 ×MSE) / SST Among them, CT i It represents the contribution rate of environmental factor i to the change of the average effect value of the key elements of water cycle during the construction of soil regulation measures, SS 因 represents the sum of squares of the experimental sample data under the influence of environmental factor i, f represents the degree of freedom of environmental factor i, MSE represents the root mean square error of the experimental sample data under the influence of environmental factor i, and SST represents the total square of the experimental sample data under the joint influence of each environmental factor and their interaction.

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