CN116341984A - Method for evaluating soil quality of larch - Google Patents
Method for evaluating soil quality of larch Download PDFInfo
- Publication number
- CN116341984A CN116341984A CN202310351513.6A CN202310351513A CN116341984A CN 116341984 A CN116341984 A CN 116341984A CN 202310351513 A CN202310351513 A CN 202310351513A CN 116341984 A CN116341984 A CN 116341984A
- Authority
- CN
- China
- Prior art keywords
- soil
- indexes
- index
- data set
- evaluating
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 239000002689 soil Substances 0.000 title claims abstract description 173
- 238000000034 method Methods 0.000 title claims abstract description 39
- 241000218652 Larix Species 0.000 title claims abstract description 20
- 235000005590 Larix decidua Nutrition 0.000 title claims abstract description 20
- 238000011160 research Methods 0.000 claims abstract description 19
- 238000012545 processing Methods 0.000 claims abstract description 5
- 238000013441 quality evaluation Methods 0.000 claims description 8
- 238000004458 analytical method Methods 0.000 claims description 7
- 238000010219 correlation analysis Methods 0.000 claims description 7
- 238000011835 investigation Methods 0.000 claims description 6
- 241000894007 species Species 0.000 claims description 5
- 239000000126 substance Substances 0.000 claims description 5
- 210000000481 breast Anatomy 0.000 claims description 3
- 230000004069 differentiation Effects 0.000 claims description 3
- 238000012165 high-throughput sequencing Methods 0.000 claims description 3
- 244000005700 microbiome Species 0.000 claims description 3
- 238000002156 mixing Methods 0.000 claims description 3
- 238000000513 principal component analysis Methods 0.000 claims description 3
- 238000005070 sampling Methods 0.000 claims description 3
- 238000013077 scoring method Methods 0.000 claims description 3
- 238000012216 screening Methods 0.000 claims description 3
- 238000005527 soil sampling Methods 0.000 claims description 3
- 238000001035 drying Methods 0.000 claims description 2
- 238000012360 testing method Methods 0.000 claims description 2
- 238000011156 evaluation Methods 0.000 abstract description 5
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 description 24
- 229910052757 nitrogen Inorganic materials 0.000 description 12
- OAICVXFJPJFONN-UHFFFAOYSA-N Phosphorus Chemical compound [P] OAICVXFJPJFONN-UHFFFAOYSA-N 0.000 description 11
- 229910052698 phosphorus Inorganic materials 0.000 description 11
- 239000011574 phosphorus Substances 0.000 description 11
- ZLMJMSJWJFRBEC-UHFFFAOYSA-N Potassium Chemical compound [K] ZLMJMSJWJFRBEC-UHFFFAOYSA-N 0.000 description 7
- 239000011591 potassium Substances 0.000 description 7
- 229910052700 potassium Inorganic materials 0.000 description 7
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 4
- 235000008582 Pinus sylvestris Nutrition 0.000 description 4
- 241000218626 Pinus sylvestris Species 0.000 description 4
- 229910052799 carbon Inorganic materials 0.000 description 4
- 230000035558 fertility Effects 0.000 description 4
- 230000036541 health Effects 0.000 description 4
- 239000001839 pinus sylvestris Substances 0.000 description 4
- 241000196324 Embryophyta Species 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 230000009467 reduction Effects 0.000 description 3
- 241000894006 Bacteria Species 0.000 description 2
- 241000628997 Flos Species 0.000 description 2
- 241000233866 Fungi Species 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 235000008216 herbs Nutrition 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 230000008635 plant growth Effects 0.000 description 2
- 230000001105 regulatory effect Effects 0.000 description 2
- 238000012876 topography Methods 0.000 description 2
- 244000274847 Betula papyrifera Species 0.000 description 1
- 235000009113 Betula papyrifera Nutrition 0.000 description 1
- 235000009109 Betula pendula Nutrition 0.000 description 1
- 235000010928 Betula populifolia Nutrition 0.000 description 1
- 235000002992 Betula pubescens Nutrition 0.000 description 1
- 241001465754 Metazoa Species 0.000 description 1
- 241000219093 Rhamnus Species 0.000 description 1
- 238000010521 absorption reaction Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000000090 biomarker Substances 0.000 description 1
- 230000001186 cumulative effect Effects 0.000 description 1
- 238000000354 decomposition reaction Methods 0.000 description 1
- 230000006866 deterioration Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 239000003344 environmental pollutant Substances 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 235000015097 nutrients Nutrition 0.000 description 1
- 238000001556 precipitation Methods 0.000 description 1
- 238000004321 preservation Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000001303 quality assessment method Methods 0.000 description 1
- 238000004445 quantitative analysis Methods 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 244000000000 soil microbiome Species 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06395—Quality analysis or management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- Entrepreneurship & Innovation (AREA)
- General Physics & Mathematics (AREA)
- Marketing (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Investigation Of Foundation Soil And Reinforcement Of Foundation Soil By Compacting Or Drainage (AREA)
Abstract
The invention discloses a method for evaluating the soil quality of larch, which comprises the following steps: determining a research area, acquiring all soil full data set indexes in the research area, and constructing a minimum data set through the full data set indexes; carrying out standardized processing on the minimum data set, and calculating the score of each soil index; based on each soil index score, a soil quality index SQI is obtained, and the soil quality is evaluated based on the soil quality index SQI. The invention adopts the minimum data set, can greatly shorten the evaluation time and cost, has high efficiency, and is suitable for evaluating the soil quality under the condition of limited resources.
Description
Technical Field
The invention relates to the technical field of soil evaluation, in particular to a method for evaluating the quality of larch soil.
Background
Soil quality assessment is a method for quantitatively analyzing soil quality conditions based on a plurality of physical, chemical and biological indicators reflecting soil characteristics. The unfavorable trend of soil fertility change can be known in time by evaluating the quality of the forest stand soil, and necessary guidance can be provided for developing effective soil fertility maintenance measures.
Soil is a basic component of a forest ecosystem, and is used as a main carrier for plant growth, and plays an important role in regulating various ecological processes, such as absorption and decomposition of nutrients, acquisition of moisture and establishment of rhizosphere habitat, so that benign growth of plants is ensured, and improvement of soil quality is promoted. Soil quality encompasses three aspects: soil improves biological productivity (soil fertility quality), soil eliminates environmental pollutants and bacteria (soil environmental quality), and soil affects animal and plant health and human health (soil health quality). The quality of soil is continuously changed due to the influence of factors such as climate, environment, soil forming condition and the like, so that quantitative analysis of the soil is difficult to directly perform. The comprehensive representation of soil quality as soil attribute reflected by various indexes is to follow the principles of economy, representativeness, feasibility, sensitivity, independence and the like in the selection of indexes, and the selected indexes are required to be considered on the basis of representing the soil characteristics of a research area, so that the economic condition and the difficulty in acquiring the indexes are considered, and the reference is provided for subsequent researches.
The artificial forest of the pinus sylvestris and the larch is mostly single tree species pure forest, and is regulated by strict regulations of a natural protection area, and the forest stand in the protection area has not developed large-scale forest management activities. At present, the problems of soil condition deterioration, productivity reduction, biodiversity reduction, ecological service function reduction and the like of part of woodlands already occur. In order to effectively develop the quality improvement technology of the artificial forest, the current situation of soil fertility and the health condition of an ecological system of a forest stand in a protected area are necessary to be known, and the current outstanding problem of the artificial forest in the area is explored. Therefore, the invention provides a soil quality evaluation method to solve the problems in the prior art.
Disclosure of Invention
In order to solve the technical problems, the invention provides a larch soil quality evaluation method, which evaluates Lin Fenjin row soil quality, timely monitors unfavorable trend of soil change and adopts effective measures to improve soil condition.
In order to achieve the above object, the invention provides a method for evaluating the soil quality of larch, comprising the following steps:
determining a research area, acquiring all soil full data set indexes in the research area, and constructing a minimum data set through the full data set indexes;
carrying out standardized processing on the minimum data set, and calculating the score of each soil index;
and acquiring a soil quality index SQI based on the soil index scores, and evaluating the soil quality based on the soil quality index SQI.
Preferably, determining the investigation region comprises:
determining the research area based on a hierarchical sampling method by considering the density, the age and the differentiation degree of the forest stand, and recording the tree species names, the breast diameters, the tree heights, the crown widths and the relative coordinates of the arbor in the research area; wherein the investigation region comprises a test plot and a control plot.
Preferably, acquiring all soil full dataset indicators within the investigation region comprises:
and randomly selecting a plurality of soil sampling points in all the sample areas, adopting a ring cutter method to obtain surface soil of 0-20 cm, passing the soil samples in the same area through a target mesh screen, equally mixing, then taking part of the soil samples, uniformly dividing the mixed soil samples into a first soil sample and a second soil sample, keeping the first soil sample in an environment of-80 ℃ for high-throughput sequencing of soil microorganisms, drying the second soil sample at room temperature, and then carrying out soil chemical property analysis to obtain the soil full data set index.
Preferably, constructing the minimum data set comprises:
based on the soil full dataset indexes, extracting main components with characteristic values more than 1 by adopting a main component analysis method, dividing soil indexes with factor load absolute values more than or equal to 0.5 in the same main component into a group, and if the factor load absolute values of the soil indexes in different main components are all more than or equal to 0.5, carrying out correlation analysis among the indexes and dividing the soil indexes into a group with low correlation with other indexes;
calculating the Norm value of each group of soil indexes, and screening out soil indexes within the range of more than 90% of the highest Norm value in each group as high-factor load indexes;
if only one high-factor load index is in the group, the soil index is directly marked into the minimum data set; if a plurality of high-factor load indexes exist in the group, carrying out correlation analysis among the indexes, if the correlation coefficient is more than 0.7, reserving the soil index with the highest correlation coefficient and the largest Norm value to enter the minimum data set, otherwise, dividing the indexes into the minimum data set.
Preferably, determining whether the soil index can enter the minimum data set by calculating the Norm value of each group of soil indexes;
the method for calculating the Norm value is as follows:
wherein N is ik And U ik Norm value and factor load value, lambda, of the ith index in the kth principal component, respectively k Is the eigenvalue of the kth principal component.
Preferably, calculating the score of each soil index includes:
and (3) carrying out standardized treatment on each soil index by adopting a nonlinear scoring method, and converting the soil index into a score between 0 and 1.
Preferably, the method for calculating the score between 0 and 1 is as follows:
wherein S is NL A is the maximum score value of each index normalized, x and x 0 The actual value and the average value of each index are respectively shown, and b is the slope of the equation.
Preferably, the method for obtaining the soil quality index SQI comprises the following steps:
wherein n is the number of indexes participating in soil quality evaluation, and w i Is the ratio of the common factor variance of the ith index to the total common factor variance in the principal component analysis, S i Each index score.
Compared with the prior art, the invention has the following advantages and technical effects:
(1) The method adopts the minimum data set, can greatly shorten the evaluation time and cost, and has high efficiency, so the method is particularly suitable for evaluating the soil quality under the condition of limited resources;
(2) According to the invention, the soil quality index is constructed through various indexes, a plurality of soil quality factors are comprehensively considered, the soil quality is more accurately estimated, and higher precision is realized;
(3) The method is simple and easy to implement and high in operability, so that the method can be widely applied to soil with different forest types and different evaluation scenes;
(4) The evaluation result of the method can provide important decision basis for the management of larch, cultivation of large-diameter materials and the like, has practicability, and is beneficial to improving the multifunctional benefit of larch.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application, illustrate and explain the application and are not to be construed as limiting the application. In the drawings:
FIG. 1 is a flowchart of a soil quality evaluation method according to an embodiment of the present invention.
Detailed Description
It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is illustrated in the flowcharts, in some cases the steps illustrated or described may be performed in an order other than that illustrated herein.
The invention provides a larch soil quality evaluation method, as shown in figure 1, comprising the following steps:
determining a research area, acquiring all soil full data set indexes in the research area, and constructing a minimum data set through the full data set indexes;
carrying out standardized processing on the minimum data set, and calculating the score of each soil index;
and acquiring a soil quality index SQI based on the soil index scores, and evaluating the soil quality based on the soil quality index SQI.
In this embodiment, a mechanical forest farm of a sain dam is taken as an example, and a technical scheme of the application is described. The land is located in a transition zone from an inner Mongolia plateau to a North China plain, the topography is low in south and high in north, and the topography is mainly hills and plain. The winter is long throughout the year, the spring and the autumn are short, the temperature difference between day and night is large, and the method belongs to temperate continental monsoon climate. The annual average temperature in the area is-1.3 ℃, the annual average precipitation is about 460mm, and the disaster weather such as frost, strong wind, drought and the like is more. The soil types are mainly mountain brown loam, aeolian sandy soil and gray forest soil. The soil types are mainly mountain brown loam, aeolian sandy soil and gray forest soil. The main arbor species include larch, pinus sylvestris, white birch, etc.; the shrubs include northeast rhamnus, herba Lespedezae Cuneatae, she Xiaochai, and flos Rhododendri mollis; herbs include herba Violae, radix Scutellariae, flos Brassicae Junceae, rhizoma et radix Valerianae and radix Sangusorbae.
Determining a research area, acquiring all soil full dataset indexes in the research area, and constructing a minimum dataset through the full dataset indexes, wherein the method comprises the following steps of:
on the basis of field investigation of the protection area, the density, age and differentiation degree of the forest stand are considered at the same time, and the selected pattern is required to reflect the basic characteristics of the forest stand of the investigation area. Based on a layered random sampling method, 24 standard plots of 30m×30m are respectively arranged on larch and pinus sylvestris, and 18 experimental plots and 6 control plots are respectively included. Among them, 1 pinus sylvestris control plot was discarded due to serious damage, and a total of 47 were standardized.
The activities of various places under artificial interference are small, and the altitudes and the gradients are basically consistent. And (5) recording the tree species names, breast diameters, tree heights, crown widths and relative coordinates of the trees in all the plots. According to the diagonal principle, herbal sample formulas are distributed at 4 corners (1 m multiplied by lm) and central positions (1 m multiplied by 1 m) of the sample area, and the types, the plant numbers, the coverage and the heights of the herbs are recorded. Laying small sample squares at 4 corners (5 m multiplied by 5 m) and central positions (5 m multiplied by 5 m) of the sample, investigating shrubs, updated seedlings and saplings in the sample squares, and recording types, plant numbers, heights, base diameters, crown widths and the like. In each plot, 5 soil sampling points are randomly selected, surface soil of 0-20 cm is taken by a ring cutter method, the soil samples with the same shape are filtered through a 20-mesh sieve, and about 1kg of soil samples are taken after equal amount mixing and are brought back to a laboratory. The soil sample is divided into two parts, the soil sample is kept in an environment of-80 ℃ for high-throughput sequencing of soil microorganisms, and the soil sample II is dried at room temperature and then subjected to soil chemical property analysis. The soil sample I has strict preservation conditions and is used for measuring bacteria and fungi; and the soil sample II is used for measuring chemical indexes such as conventional organic carbon, total nitrogen, total phosphorus, total potassium and the like. The soil indexes comprise: organic carbon, total nitrogen, total phosphorus, total potassium, pH, available phosphorus, quick-acting nitrogen, quick-acting potassium, soil bacteria richness, soil fungi richness and the like.
Performing standardization processing on the minimum data set, and calculating the score of each soil index, wherein the method comprises the following steps:
and taking all soil indexes participating in analysis as full data set indexes according to the actual conditions of a research area. Firstly, extracting main components with a plurality of characteristic values of >1 by adopting a main component analysis method, dividing soil indexes with factor load absolute values more than or equal to 0.5 in the same main component into a group, and if the factor load absolute values of the soil indexes in different main components are all more than or equal to 0.5, carrying out correlation analysis among the indexes and dividing the soil indexes into a group with lower correlation with other indexes. And secondly, calculating the Nor value of each group of soil indexes, wherein the Nor value represents the capability of each index to interpret the overall soil quality information, and the information redundancy among the indexes can be reduced according to the Nor value. Screening out soil indexes within the range of more than 90% of the highest Norm value in each group, taking the soil indexes as high-factor load indexes, and if only one high-factor load index exists in the group, directly dividing the soil indexes into a minimum data set; if a plurality of high-factor load indexes exist in the group, carrying out correlation analysis among the indexes, if the correlation coefficient is more than 0.7, reserving the soil index with the highest correlation coefficient and the largest Norm value into a minimum data set, otherwise, dividing the indexes into the minimum data set.
The calculation formula is as follows:
wherein N is ik And U ik Norm value and factor load value, lambda, of the ith index in the kth principal component, respectively k Is the eigenvalue of the kth principal component.
The soil index is standardized by adopting a nonlinear scoring method, so that the soil index is converted into a score between 0 and 1:
S NL a is the maximum score value of each index normalized, x and x 0 The actual value and the average value of each index are respectively, b is the slope of an equation, and the value is divided into two cases: the "larger and better" soil index (b= -2.5), and the "smaller and better" soil index (b=2.5).
By scoring each index (S i ) And weight (w) i ) And (5) carrying out weighted summation to obtain a Soil Quality Index (SQI), wherein the calculation formula is as follows:
wherein n is the number of indexes participating in soil quality evaluation, and w i Is given as weight S i Each index score.
As shown in Table 1, 2 main components are extracted according to the principle that the characteristic value is more than or equal to 1, the contribution rates are 66.557% and 14.958%, and the cumulative contribution rate reaches 81.516%, which indicates that the extracted main components can explain more than 80% of the information of all soil indexes.
TABLE 1
Soil indexes with the absolute value of factor load of more than or equal to 0.5 in each main component are divided into a group. The soil indexes of the first group comprise total nitrogen, total phosphorus, organic carbon, quick-acting nitrogen, quick-acting potassium and pH, and the soil indexes of the second group comprise total potassium and effective phosphorus. Next, the index within 90% of the highest Norm value for each group is screened out as a pre-selected index into the MDS. The first group of norms ranges from 2.036 to 2.262, the preselected indexes comprise total nitrogen, total phosphorus, organic carbon and quick-acting nitrogen, according to correlation analysis, the correlation coefficient among the 4 indexes is larger than 0.7 and has extremely obvious correlation (P is smaller than 0.01), so that quick-acting nitrogen with the largest correlation coefficient and the highest norms is selected to enter MDS; the second group has a Norm value in the range of 1.232 to 1.369 and the preselected target includes only available phosphorus, directly into the MDS. Thus, the soil quality evaluation MDS of the present application consists of 2 indicators of soil quick-acting nitrogen and available phosphorus. And calculating the weights of all indexes in the TDS and the MDS according to the variance of the common factors obtained by principal component analysis, wherein the weights of the quick-acting nitrogen and the effective phosphorus in the MDS are 0.5 (table 1). The indexes are converted into scores between 0 and 1 through a nonlinear scoring equation, the larger the numerical value is, the better the soil quality is represented, and according to the positive and negative of factor load values of all the indexes on main components, soil organic matters, total nitrogen, total phosphorus, quick-acting nitrogen, effective phosphorus and quick-acting potassium are determined to be 'larger and better' indexes, and the total potassium and pH value of the soil are determined to be 'smaller and better' indexes.
TABLE 2
The foregoing is merely a preferred embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions easily conceivable by those skilled in the art within the technical scope of the present application should be covered in the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (8)
1. The method for evaluating the soil quality of larch is characterized by comprising the following steps:
determining a research area, acquiring all soil full data set indexes in the research area, and constructing a minimum data set through the full data set indexes;
carrying out standardized processing on the minimum data set, and calculating the score of each soil index;
and acquiring a soil quality index SQI based on the soil index scores, and evaluating the soil quality based on the soil quality index SQI.
2. The method for evaluating the soil quality of larch according to claim 1, wherein determining the research area comprises:
determining the research area based on a hierarchical sampling method by considering the density, the age and the differentiation degree of the forest stand, and recording the tree species names, the breast diameters, the tree heights, the crown widths and the relative coordinates of the arbor in the research area; wherein the investigation region comprises a test plot and a control plot.
3. The method for evaluating the soil quality of larch according to claim 2, wherein obtaining all soil full dataset indexes in the research area comprises:
and randomly selecting a plurality of soil sampling points in all the sample areas, adopting a ring cutter method to obtain surface soil of 0-20 cm, passing the soil samples in the same area through a target mesh screen, equally mixing, then taking part of the soil samples, uniformly dividing the mixed soil samples into a first soil sample and a second soil sample, keeping the first soil sample in an environment of-80 ℃ for high-throughput sequencing of soil microorganisms, drying the second soil sample at room temperature, and then carrying out soil chemical property analysis to obtain the soil full data set index.
4. The method for evaluating the soil quality of larch according to claim 1, wherein constructing a minimum data set comprises:
based on the soil full dataset indexes, extracting main components with characteristic values more than 1 by adopting a main component analysis method, dividing soil indexes with factor load absolute values more than or equal to 0.5 in the same main component into a group, and if the factor load absolute values of the soil indexes in different main components are all more than or equal to 0.5, carrying out correlation analysis among the indexes and dividing the soil indexes into a group with low correlation with other indexes;
calculating the Norm value of each group of soil indexes, and screening out soil indexes within the range of more than 90% of the highest Norm value in each group as high-factor load indexes;
if only one high-factor load index is in the group, the soil index is directly marked into the minimum data set; if a plurality of high-factor load indexes exist in the group, carrying out correlation analysis among the indexes, if the correlation coefficient is more than 0.7, reserving the soil index with the highest correlation coefficient and the largest Norm value to enter the minimum data set, otherwise, dividing the indexes into the minimum data set.
5. The method for evaluating the soil quality of larch according to claim 4, wherein whether the soil index can enter a minimum data set is judged by calculating the Norm value of each set of soil indexes;
the method for calculating the Norm value is as follows:
wherein N is ik And U ik Norm value and factor load value, lambda, of the ith index in the kth principal component, respectively k Is the eigenvalue of the kth principal component.
6. The method for evaluating the soil quality of larch according to claim 1, wherein calculating the score of each soil index comprises:
and (3) carrying out standardized treatment on each soil index by adopting a nonlinear scoring method, and converting the soil index into a score between 0 and 1.
7. The method for evaluating the soil quality of larch according to claim 6, wherein the method for calculating the score between 0 and 1 is as follows:
wherein S is NL A is the maximum score value of each index normalized, x and x 0 The actual value and the average value of each index are respectively shown, and b is the slope of the equation.
8. The method for evaluating the soil quality of larch according to claim 1, wherein the method for obtaining the soil quality index SQI is as follows:
wherein n is the number of indexes participating in soil quality evaluation, and w i Is the ratio of the common factor variance of the ith index to the total common factor variance in the principal component analysis, S i Each index score.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310351513.6A CN116341984A (en) | 2023-04-04 | 2023-04-04 | Method for evaluating soil quality of larch |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310351513.6A CN116341984A (en) | 2023-04-04 | 2023-04-04 | Method for evaluating soil quality of larch |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116341984A true CN116341984A (en) | 2023-06-27 |
Family
ID=86892963
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310351513.6A Pending CN116341984A (en) | 2023-04-04 | 2023-04-04 | Method for evaluating soil quality of larch |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116341984A (en) |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113344409A (en) * | 2021-06-22 | 2021-09-03 | 山东农业大学 | Evaluation method and system for facility continuous cropping soil quality |
-
2023
- 2023-04-04 CN CN202310351513.6A patent/CN116341984A/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113344409A (en) * | 2021-06-22 | 2021-09-03 | 山东农业大学 | Evaluation method and system for facility continuous cropping soil quality |
Non-Patent Citations (1)
| Title |
|---|
| 杨振奇等: "基于最小数据集的砒砂岩区人工林地土壤质量评价指标体系构建", 基于最小数据集的砒砂岩区人工林地土壤质量评价指标体系构建, vol. 50, no. 5, pages 1072 - 1078 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Sevik et al. | The effect of climate on leaf micromorphological characteristics in some broad-leaved species | |
| Turkyilmaz et al. | Variation of heavy metal accumulation in certain landscaping plants due to traffic density | |
| Bässler et al. | The BIOKLIM Project: Biodiversity Research between Climate Change and Wilding in a temperate montane forest–The conceptual framework | |
| McPartland et al. | Cannabis is indigenous to Europe and cultivation began during the Copper or Bronze age: a probabilistic synthesis of fossil pollen studies | |
| Gole et al. | Floristic composition and environmental factors characterizing coffee forests in southwest Ethiopia | |
| Bu et al. | The study of biological soil crusts: hotspots and prospects | |
| CN108388566B (en) | Spatial simulation method for abundance distribution of plant species | |
| Royles et al. | Moss stable isotopes (carbon-13, oxygen-18) and testate amoebae reflect environmental inputs and microclimate along a latitudinal gradient on the Antarctic Peninsula | |
| Pasho et al. | Factors driving growth responses to drought in Mediterranean forests | |
| Cordova et al. | The potential of Poaceae, Cyperaceae, and Restionaceae phytoliths to reflect past environmental conditions in South Africa | |
| Zhang et al. | Effect of desert shrubs on fine-scale spatial patterns of understory vegetation in a dry-land | |
| Liang et al. | The alpine dwarf shrub Cassiope fastigiata in the Himalayas: does it reflect site-specific climatic signals in its annual growth rings? | |
| Glausen et al. | Successional trends and processes on a glacial foreland in Southern Iceland studied by repeated species counts | |
| Barthelemy et al. | Floristic diversity of Piliostigma asssociations in relation to latitudinal gradient, soil and climate variables in Burkina Faso, West Africa | |
| van Vugt et al. | Pollen, macrofossils and sedaDNA reveal climate and land use impacts on Holocene mountain vegetation of the Lepontine Alps, Italy | |
| Bunting | Pollen in wetlands: using simulations of pollen dispersal and deposition to better interpret the pollen signal: A PolLandCal contribution | |
| Sprenkle-Hyppolite et al. | Landscape factors and restoration practices associated with initial reforestation success in Haiti | |
| Zhang et al. | Effect of sex ratio, habitat factors and neighborhood competition on stem growth in the dioecious tree Fraxinus mandshurica | |
| Kaewthongrach et al. | Impact of severe drought during the strong 2015/2016 El Nino on the phenology and survival of secondary dry dipterocarp species in Western Thailand | |
| Li et al. | Geographic patterns and environmental correlates of taxonomic, phylogenetic and functional β-diversity of wetland plants in the Qinghai-Tibet Plateau | |
| CN116341984A (en) | Method for evaluating soil quality of larch | |
| PINTO¹ et al. | Mapping of environmental degradation in regions and states of Brazil | |
| Chen et al. | Ecological land classification: A quantitative classification and ordination of forest communities adjacent to a rapidly expanding urban area in southeast coastal China | |
| Vachnadze et al. | Carbon Stock Sequestered in the phytocenosis of oak forests in Georgia | |
| Khan et al. | How environmental variables can determine the Chir pine (Pinus roxburghii sarg.) distribution in swat Hindukush range of Pakistan: Current and future prospective of the species |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |