CN109829650B - Model for evaluating different degradation degrees of meadow grassland and establishing method and application thereof - Google Patents

Abstract

The application discloses a model for evaluating different degradation degrees of meadow grassland and an establishing method and application thereof. The method is used for natural grasslands with different degradation degrees caused by different utilization degrees of meadow grasslands, a field ecological investigation method is adopted through field control experiments, the response mechanism of meadow grassland degradation to human activities (control grazing) is researched, a principal component analysis, canonical correlation and fuzzy mathematics are adopted to establish a matrix, degradation assessment system indication degree diagnosis methods with different utilization degrees are established, grassland vegetation and soil index systems with different degradation degrees in different utilization modes are screened, indication degrees with different degradation degrees are obtained, and a meadow grassland degradation quantitative assessment index system indication degree diagnosis method with high scientificity and operability is established. The method has practical significance for establishing a meadow grassland degradation index system evaluation, objectively judging the degradation condition of the meadow grassland and promoting the meadow animal husbandry production and environmental protection.

Description

Model for evaluating different degradation degrees of meadow grassland and establishing method and application thereof

Technical Field

The invention relates to the field of grassland ecosystem protection, in particular to a model for evaluating different degradation degrees of meadow grasslands and an establishing method and application thereof.

Background

The grassland area of China is 60 hundred million mu, which occupies 41.7 percent of the land area of China, wherein the areas of northern meadows and meadow meadows are about 5.96 hundred million mu, which is a meadow with highest productivity and most abundant diversity in northern pasturing areas, the bearing capacity of livestock is equivalent to the sum of other temperate meadows, and the ecological and production functions of the meadows have the status of lifting the weight. Compared with the meadow types in arid regions, the meadow has more diversified utilization modes, more complicated degradation process and mechanism, more than 90% of steppes in China degrade since the 80 s of the 20 th century, the productivity of the meadow in the north is reduced by 20-50%, and the proportion of high-quality pasture is reduced by more than 15%. However, due to the apparent characteristics of high productivity and high diversity, the actual degradation degree of the meadow grassland is seriously underestimated, a special degradation standard and a recovery theory are theoretically lacked, a complete ecological management and continuous utilization technical system is not formed in application, and the high-productivity dominant exertion of the meadow in north and the development of regional grass husbandry are restricted.

Therefore, as the deteriorated grassland area is enlarged and increased, the restoration and reconstruction of the deteriorated grassland are in the forefront, and the premise and the basis are to carry out scientific diagnosis on the deteriorated grassland degree. Currently, there is still a lack of comprehensive, simple and practical systematic diagnosis of the degree of deterioration of grassland systems.

Disclosure of Invention

Aiming at the defects in the prior art, the invention aims to provide a method for establishing a model for evaluating different degradation degrees of meadow grassland. The model obtained by the method has practical significance for objectively judging the degradation condition of meadow grasslands, particularly understanding the influence conditions of different degradation degrees on the grasslands, making a practical grassland sustainable development strategy and promoting the production of the grassland animal husbandry and environmental protection.

In order to achieve the above purposes, the technical scheme adopted by the invention is as follows:

the invention provides a method for establishing a model for evaluating different degradation degrees of meadow grassland, which comprises the following steps: s1, selecting variable data of meadow grasslands with different degradation degrees, wherein the variable data comprise a first vegetation characteristic group and a first soil characteristic group;

s2, respectively carrying out principal component analysis on the first vegetation feature group and the first soil feature group of each meadow with different degradation degrees, selecting principal components according to the standard that the value of a feature root is greater than 1, and obtaining a second vegetation feature group with relatively large factor load (namely, accumulated contribution rate) on the principal component with the same dimension and a second soil feature group with relatively large factor load on the principal component with the same dimension;

s3, performing typical correlation analysis on the second vegetation characteristic group and the second soil characteristic group of each meadow with different degradation degrees, and extracting a third vegetation characteristic group and a third soil characteristic group which play a main determining role;

s4, taking the data of the third vegetation characteristic group and the third soil characteristic group as a factor set, taking different degradation degrees as a processing set, carrying out fuzzy comprehensive evaluation on each meadow grassland with different degradation degrees, calculating difference coefficients between the data of the third vegetation characteristic group and the third soil characteristic group of each meadow grassland with different degradation degrees, and calculating to obtain fuzzy comprehensive evaluation coefficients d_i(i.e., indicativity), wherein i takes any one of natural numbers 1-n and represents meadow grasslands with different degradation degrees; (fuzzy comprehensive evaluation index system is the basis for comprehensive evaluation, and whether the evaluation index is properly selected can directly influence the accuracy of the comprehensive evaluation)

S5, defining different fuzzy comprehensive evaluation coefficients d_iAnd obtaining models for evaluating different degradation degrees of the meadow grassland.

In the above method, the typical correlation analysis in step S3 includes the following steps:

firstly, checking whether two groups of variables of a second vegetation characteristic group and a second soil characteristic group of each meadow with different degradation degrees are related, and carrying out the following steps under the related condition:

s31, finding out a first pair of linear combinations in the variables of the second vegetation characteristic group and the second soil characteristic group respectively, and enabling the linear combinations to be u as shown in the following formula (1)₁And v₁With the greatest correlation:

s32, and then finding out a second pair of linear combinations in the variables of the second vegetation characteristic group and the second soil characteristic group as the following formula (2), so that the linear combinations are respectively not related to the first linear combinations in the group, and the u of the linear combinations are₂And v₂With the next largest correlation:

in the formulae (1) and (2), u₁、u₂For a model of the vegetation index system, v₁、v₂Is a soil index system model; u. of₂And u₁Independently of each other, v₁And v₂Are independent of each other; but u₁And v₁Correlation u₂And v₂Correlation; x is the number of₁,x₂… denotes a vegetation index in the second vegetation feature group, y₁,y₂And … denotes a soil index in the second soil characteristic group, a₁₁,a₂₁,…，a_p1,a₁₂,a₂₂，…，a_p2And b₁₁,b₂₁,…，b_p1,b₁₂,b₂₂,…,b_p2The typical correlation coefficient is represented and obtained by a matrix under the condition of significant level correlation of the typical correlation, and the larger the absolute value of the typical correlation coefficient is, the larger the degree of reflecting information of the typical variable is;

s33, repeating S31 and S32 to obtain a typical variable v_qAnd u_qExtracting the variables pair by pair from big to small according to the correlation coefficients of the variables pair by pair until the correlation between two groups of variables is extracted in the step q, and obtaining q pairs of typical correlation variables; 1-q pairs of typical correlation variables with significance of the correlation coefficients of the typical correlation variables are reserved;

and extracting vegetation indexes and soil indexes with relatively large absolute values of typical correlation coefficients from the 1-q pairs of typical correlation variables as a third vegetation characteristic group and a third soil characteristic group which play a main decision role.

In the above method, the fuzzy comprehensive evaluation in step S4 specifically includes the following steps:

firstly, meadow grasslands with different degradation degrees are set as follows: x ═ X₁,X₂,…,X_i,…,X_n；

The factors influencing the grassland are set as follows: u is equal to U₁,U₂,…,U_j,…,U_m；

The feature matrix is U_n×m＝(U_ij)_n×m (3)；

r∈〔0,1〕；

The evaluation matrix R ═ R (R)_ij)_n×m (5)；

Secondly, calculating to obtain a difference coefficient between data of a third vegetation characteristic group and a third soil characteristic group of each meadow with different degradation degrees according to the evaluation matrix, wherein the calculation process is as follows:

the evaluation functions are taken as follows:

D₁＝1/m×(r_i1+r_i2+…+r_im) (6)；

D₂＝Max(r_i1,r_i2,…,r_im) (7)；

D₃＝Min(r_i1,r_i2,…,r_im) (8)；

respectively calculating the difference coefficient d_i1,d_i2,d_i3；

Let W₁＝(D₁，D₂，D₃)，R₁＝F(X×W₁) Namely:

finally, let d again_i＝1/3×(d_i1+d_i2+d_i3) (10)；

Calculating to obtain a fuzzy comprehensive evaluation coefficient d_iWherein d is_i∈〔0,1〕。

In the above method, preferably, in step S1, the first vegetation feature group includes the following vegetation indexes: community aboveground biomass (C1), community underground biomass (0-60 cm) (C2), community litter (C3), community coverage (C4), community height (C5), Margelef abundance index (C6), Shannon-Wiener diversity index (C7), Simpson dominance index (C8) and Pielou uniformity index (C9);

and/or the first soil characteristic group comprises the following soil indexes: soil moisture content (C10), soil bulk weight (C11), soil pH (C12), soil organic carbon (C13), soil total nitrogen content (C14), soil total phosphorus content (C15), soil total potassium content (C16), C/N (C17), quick-acting nitrogen (C18), quick-acting phosphorus (C19), quick-acting potassium (C20), soil microbial carbon (C21), and soil microbial nitrogen (C22); and the indexes in the first soil characteristic group are indexes of 0-10cm soil layers.

In the above method, preferably, the meadow grasslands of different degrees of deterioration include non-grazing areas, light grazing deteriorated areas, medium grazing deteriorated areas and extreme grazing deteriorated areas; wherein the non-grazing area is an extremely stable grassland, d _i1 is ═ 1; the coefficient of each different degradation degree evaluation is between 0 and 1, d_iThe closer to 1, the closer to a very steady state the grass is.

In the above method, in step S2, it is preferable that the second vegetation feature group of the non-grazing area includes the following vegetation indexes: community underground biomass (C2), community height (C5), Shannon-Wiener diversity index (C7), Simpson dominance index (C8) and Pielou uniformity index (C9); the second soil characteristic group comprises the following soil indexes: pH value (C12), soil organic carbon (C13), soil total nitrogen content (C14), soil total phosphorus content (C15), C/N (C17) and soil microorganism carbon (C21);

the second vegetation characteristic group of the mild grazing utilization area comprises the following vegetation indexes: community underground biomass (C2), Margelef abundance index (C6), Shannon-Wiener diversity index (C7), Simpson dominance index (C8), Pielou uniformity index (C9); the second soil characteristic group comprises the following soil indexes: soil volume weight (C11), pH value (C12), soil total phosphorus content (C15), C/N (C17), quick-acting potassium (C20) and soil microbial nitrogen (C22);

the second vegetation feature group of the moderate grazing utilization area comprises the following vegetation indexes: colony litter (C3), colony coverage (C4), Shannon-Wiener diversity index (C7), Simpson dominance index (C8), Pielou uniformity index (C9); the second soil characteristic group comprises the following soil indexes: soil volume weight (C11), pH value (C12), soil total phosphorus content (C15), C/N (C17), quick-acting nitrogen (C18) and soil microbial nitrogen (C22);

the second vegetation feature group of the extreme grazing utilization area comprises the following vegetation indexes: community underground biomass (C2), community coverage (C4), community height (C5), Shannon-Wiener diversity index (C7) and Simpson dominance index (C8); the second soil characteristic group comprises the following soil indexes: the soil water content (C10), the pH value (C12), the soil total nitrogen content (C14), the soil total phosphorus content (C15), the quick-acting phosphorus (C19) and the soil microorganism carbon (C21).

In the above method, in step S3, it is preferable that the third vegetation feature group of the non-grazing area includes the following vegetation indexes: community underground biomass (C2), community height (C5), Shannon-Wiener diversity index (C7); the third soil characteristic group comprises the following soil indexes: soil organic carbon (C13), soil total nitrogen content (C14), C/N (C17);

the third vegetation characteristic group of the mild grazing degradation area comprises the following vegetation indexes: a Margelef abundance index (C6), a Simpson dominance index (C8), a Pielou evenness index (C9); the third soil characteristic group comprises the following soil indexes: C/N (C17), quick-acting potassium (C20), soil microbial nitrogen (C22);

the third vegetation feature group of the moderate grazing degradation zone comprises the following vegetation indexes: community litter (C3), Shannon-Wiener diversity index (C7), Simpson dominance index (C8); the third soil characteristic group comprises the following soil indexes: pH value (C12), soil total phosphorus content (C15), and quick-acting nitrogen (C18);

the third vegetation feature group of the extreme grazing utilization area comprises the following vegetation indexes: community underground biomass (C2), community coverage (C4), Shannon-Wiener diversity index (C7); the third soil characteristic group comprises the following soil indexes: soil moisture content (C10), pH value (C12), and quick-acting phosphorus (C19).

In the above method, in step S5, the definition is preferably as follows:

when 1 is more than or equal to d_iWhen the degradation degree of the meadow grassland to be evaluated is more than 0.9922, judging the degradation degree of the meadow grassland to be evaluated to be an extremely stable level;

when 0.9922 is more than or equal to d_iWhen the degradation degree of the meadow grassland to be evaluated is more than 0.8025, judging the degradation degree of the meadow grassland to be evaluated to be a stable level;

when 0.8025 is more than or equal to d_iWhen the meadow degradation degree is more than 0.7914, judging the meadow degradation degree to be evaluated to be an unstable grade;

when 0.7914 is more than or equal to d_iAnd (4) being more than or equal to 0, judging the degradation degree of the meadow grassland to be evaluated to be an extremely unstable grade.

In the method, the meadow grassland plant community types are a leymus chinensis + mixed grass community, a Beggel needle grass + leymus chinensis community and a filifolius sibiricus grassland community, and the leymus chinensis + mixed grass community is preferred.

The invention also provides a model for evaluating different degradation degrees of meadow grassland, which is established by any one of the methods.

The invention protects the application of the model in evaluating different degradation degrees of meadow grassland.

The invention has the following beneficial effects:

aiming at natural grasslands with different degradation degrees caused by different utilization degrees of meadow grasslands, the method researches the response mechanism of the meadow grassland degradation to human activities (controlling grazing) by adopting a field ecological investigation method through a field control experiment, establishes an indication degree diagnosis method of the degradation evaluation system with different utilization degrees by adopting principal component analysis, typical correlation and fuzzy mathematics, screens grassland vegetation and soil index systems with different utilization modes and different degradation degrees, screens a grassland degradation index system, analyzes the difference, sensitivity and uncertainty of the degradation degrees, obtains the indication degrees of different degradation degrees, establishes a meadow grassland degradation quantitative evaluation index system indication degree diagnosis method with strong scientificity and operability, enriches the content of meadow degradation degree evaluation, provides a theoretical basis for the establishment of the meadow grassland degradation index system evaluation, and moreover, the method has practical significance for objectively judging the degradation condition of meadow grasslands by related departments, especially for understanding the influence conditions of different degradation degrees on the grasslands, making a practical grassland sustainable development strategy and promoting the production of grassland animal husbandry and environmental protection.

Drawings

FIG. 1 shows the height variation of the characterised communities of vegetation communities between different years and different grazing strengths, among which smallThe written letters represent the difference between different grazing intensities in the same year, the correlation formula between the community height y and the grazing intensity x is-18.306 x +20.877, and the correlation coefficient R²＝0.9738。

FIG. 2 is a graph showing the variation of the coverage of a characteristic community of vegetation between different years and different grazing intensities, wherein lower case letters represent the difference between different grazing intensities in the same year, the correlation formula between the community coverage y and the grazing intensity x is-32.291 x +73, and the correlation coefficient R is²＝0.9551。

FIG. 3 shows the above-ground biomass variation of a vegetation community characteristic of the vegetation community between different years and different grazing intensities, wherein lower case letters represent the difference between different grazing intensities of the same year, and the correlation formula between the above-ground biomass y of the community and the grazing intensity x is that y is 260.34e^-1.594xCoefficient of correlation R²＝0.9898，P＜0.001。

FIG. 4 is a graph showing the vegetation population characteristic litter biomass variation between different years and different grazing strengths, wherein lower case letters represent the difference between different grazing strengths in the same year, the correlation between litter biomass y and grazing strength x is-225.49 x +198.13, and the correlation coefficient R is²＝0.9672。

FIG. 5 shows the variation of the underground biomass of the vegetation community character community among different soil depths, wherein lower case letters represent the difference among different grazing strengths in the same soil depth.

Fig. 6 is a graph of the Margalef abundance index change of the vegetation population character, wherein lower case letters represent the difference between different grazing strengths.

FIG. 7 is a plot of the Shannon-wiener diversity index change characteristic of vegetation populations, where lower case letters represent the difference between different grazing intensities.

Fig. 8 is a graph of Simpson dominance index change in vegetation population characteristics, where lower case letters represent differences between different grazing intensities.

Fig. 9 is a plot of the pilou uniformity index change in vegetation population characteristics, where lower case letters represent the difference between different grazing intensities.

FIG. 10 shows the results of different years and different grazing strengthsWherein the correlation between the soil temperature y and the grazing intensity x is 23.785e^0.1003xCoefficient of correlation R²＝0.5475。

FIG. 11 shows the variation of soil moisture (soil moisture content) between different years and between different grazing strengths, where the correlation between the soil moisture content y and the grazing strength x is-1.9209 x +18.503, and the correlation coefficient R is²＝0.8426。

FIG. 12 shows the variation of soil volume weight between different years and different grazing strengths, wherein the correlation between the soil volume weight y and the grazing strength x is 0.0445x +1.0062, and the correlation coefficient R is²＝0.751。

FIG. 13 shows the variation of organic carbon content in soil between different years and between different grazing strengths, wherein the correlation between the organic carbon content of soil y and the grazing strength x is-1.8697 x +36.417, and the correlation coefficient R is²＝0.3845。

FIG. 14 shows the variation of total nitrogen content of soil between different years and different grazing strengths, wherein the correlation between total nitrogen y of soil and grazing strength x is-0.2762 x +3.1641, and the correlation coefficient R is²＝0.655。

FIG. 15 shows the variation of total phosphorus content of soil between different years and different grazing strengths, wherein the correlation between total phosphorus y and grazing strength x is-0.0228 x +0.592, and the correlation coefficient R is²＝0.7245。

FIG. 16 shows the variation of total potassium content of soil between different years and between different grazing strengths, wherein the correlation between total potassium y of soil and grazing strength x is-4.6725 x²+4.3935x +24.421, correlation coefficient R²＝0.7331。

FIG. 17 shows the change in soil pH between different years and different grazing intensities, where the correlation between soil pH y and grazing intensity x is-0.2794 x +6.7608, and the correlation coefficient R is²＝0.9506。

FIG. 18 shows the variation of soil rapid-acting nitrogen between different years and different grazing strengths, wherein the correlation between the soil rapid-acting nitrogen y and the grazing strength x is represented by y-31.259 x²+27.713x +291.36, correlation coefficient R²＝0.1513。

FIG. 19 shows the variation of the soil rapid-acting phosphorus between different years and different grazing strengths, wherein the correlation between the soil rapid-acting phosphorus y and the grazing strength x is represented by y-2.3312 x²-2.2335x +5.483, coefficient of correlation R²＝0.8565。

FIG. 20 shows the variation of soil rapid-acting potassium between different years and different grazing strengths, wherein the correlation between the soil rapid-acting potassium y and the grazing strength x is represented by y 27.812x +232.68, and the correlation coefficient R is²＝0.5408。

FIG. 21 shows the carbon change of soil microorganisms in the left graph and the nitrogen change of soil microorganisms in the right graph.

FIG. 22 shows the variation of soil C/N between different years and different grazing strengths, wherein the correlation between soil C/N y and grazing strength x is represented by y 1.1179x +10.311, and the correlation coefficient R is²＝0.7294。

In FIGS. 1 to 22, 0.00, 0.23, 0.34, 0.46, 0.69, and 0.92 represent grazing intensity G, respectively_0.00：0.00Au/hm²、G_0.23：0.23Au/hm²、G_0.34：0.34Au/hm²、G_0.46：0.46Au/hm²、G_0.69：0.69Au/hm²、G_0.92：0.92Au/hm²。

Detailed Description

Example 1 influence of varying degrees of deterioration on meadow due to grazing intensity

And (3) experimental design: taking natural grassland with a community type of a Chinese wildrye and a weed community as a grazing sample plot, and setting 6 levels of grazing intensity gradient treatment, wherein the livestock carrying rates are respectively as follows: g_0.00:0.00Au/hm²、G_0.23:0.23Au/hm²、G_0.34:0.34Au/hm²、G_0.46:0.46Au/hm²、G_0.69:0.69Au/hm²、G_0.92:0.92Au/hm²。

Through a grazing test of ten years (2009-2018), along with the duration of grazing time, different grazing intensities gradually generate obvious changes on vegetation community characteristics and soil characteristics, so that obvious series areas of slight degeneration, moderate degeneration and severe degeneration are caused. The specific data are as follows:

1. community characteristic change after continuously controlling grazing intensity for years

After ten-year grazing test, along with the duration of grazing time, the community height, community coverage, community aboveground biomass and litter biomass (namely community litter) show gradual reduction along with the increase of grazing gradient, the difference between different grazing gradients is obvious, and grazing G is not used_0.00And light grazing G_0.23The data is significantly higher than that of moderate grazing G_0.46Severe grazing G_0.69And G_0.92(FIGS. 1 to 4).

As shown in fig. 5, the community underground biomass decreased with increasing grazing intensity and soil depth. The surface 0-10cm underground biomass of different grazing strength shows no grazing and mild grazing less than moderate grazing G_0.46And severe grazing G_0.69(ii) a 0-20cm underground biomass presents a mild grazing G_0.23(mild regression) greater than moderate grazing G_0.46And severe grazing G_0.69(ii) a Other soil layers all appear to be grazing significantly higher than other grazing. 0-60cm presents grazing free G_0.00And light grazing G_0.23Higher than moderate grazing G_0.46And severe grazing G_0.92Underground biomass.

2. Community diversity change under continuous control of grazing intensity

The diversity index analysis (fig. 6) of the species in different grazing gradient herds indicates that the Margalef abundance index shows a mild grazing G after 10 years of grazing_0.34Is obviously higher than moderate grazing G_0.46And severe grazing G_0.92(P<0.05). The grazing inhibits the competitive power of dominant species, possibly causes the invasion and colonization of weak species, and increases the diversity of species in the community to a certain extent. However, if the pasturing is excessive, the edible pasture in the community can lose the regeneration capability due to excessive gnawing, and gradually disappear in the community, so that the diversity of the community is reduced, and the proper pasturing can keep the Margalef richness index of the community, which shows that the interference of the pasturing activity on the grassland depends on the pasturing frequency, intensity and the type of livestock, and the proper grazing activityThe grazing intensity can promote the development of the grassland, and the biological diversity of the grassland is improved and matched. Mild grazing shows a gradual increase trend with time on grazing, and is greater than other grazing treatments.

As shown in fig. 7-9, light grazing is beneficial to improve the diversity of colony species; both the grazing absence and the severe grazing are used for remarkably reducing the Shannon-wiener diversity index and the Simpson dominance index; the moderate grazing keeps a higher species Pielou evenness index, the whole research reveals the response mechanism of the grassland vegetation to grazing interference, and the moderate interference hypothesis is verified.

3. Soil physical property change after continuous control of grazing strength for years

As shown in fig. 10, the grassland soil temperature increased with increasing grazing intensity. As shown in fig. 11, grazing reduces the moisture storage of the soil and reduces the soil moisture content during the grazing season. The water content of the soil with different grazing strengths of 0-10cm shows that the grazing is not performed and the mild grazing is larger than the moderate grazing G_0.46And severe grazing G_0.69，G_0.92. As shown in FIG. 12, the volume weight of the soil increases linearly with the grazing intensity, and the soil layer with different grazing intensities of 0-10cm shows severe grazing G_0.69、G_0.92Significantly greater than no grazing and mild grazing greater than moderate grazing G_0.46。

4. Continuously controlling the chemical characteristic content change of soil after many years of grazing strength

With the duration of grazing time, the grazing strength has obvious influence on the organic carbon content and total nitrogen of the soil surface layer. Light grazing G_0.34And moderate grazing G_0.46High total nitrogen and organic carbon in soil, and high grazing degree G_0.69The soil carbon nitrogen content was low (fig. 13-14). As shown in fig. 15-16, the total phosphorus and potassium in the soil are increased and then decreased, the influence of grazing on the soil lags behind that of plants, and no obvious change rule is shown. As shown in fig. 17, severe grazing significantly reduced soil pH; as shown in fig. 18-20, grazing increases the amount of organic nitrogen readily decomposed by the soil as the grazing intensity increases, thereby increasing the amount of available nitrogen in the soil. The effective effect of the quick-acting phosphorus is higher than that of light grazing and severe grazing without grazing and severe grazingAnd (4) transforming. With the extreme degradation of grassland, the regeneration of pasture is greatly inhibited, the demand for potassium is obviously reduced, so that the nutrient accumulation is caused, meanwhile, as the excrement excreted by the grazing livestock contains a large amount of potassium, the high-strength grazing increases the number of the livestock per unit area, the potassium excreted to the grassland through the excrement every day is correspondingly increased, and therefore, the content of the quick-acting potassium on the surface layer of the soil is obviously increased along with the increase of the grazing strength. As shown in FIG. 22, grazing increases the soil C/N content as the strength of grazing increases.

5. Soil biological character change after continuous control of grazing intensity for years

As shown in fig. 21, as grazing time continues, the carbon content of microorganisms in non-grazing, light grazing and medium grazing soil of 0-10cm soil layer increases, the carbon content of microorganisms in heavy grazing soil also increases, and the increase ratio is larger; and the nitrogen content of soil microorganisms is reduced by severe grazing.

Example 2 establishment of a model for evaluating different degradation degrees of meadow

In order to study the interaction between phyto-communities and soil factors in meadow meadows with different grazing degradation degrees and to select an index system for diagnosing the degradation degree of meadow, the leymus and mixed grass communities in example 1 were not grazed (G0, i.e. the grazing intensity is G)_0.00) Light grazing utilization area (G1, i.e. grazing intensity G)_0.23) And a moderate grazing zone (G2, i.e. grazing intensity G)_0.46) The high grazing utilization area (G3, i.e. grazing intensity G)_0.69) The association between the plant community and the soil factors was analyzed using the "Canonical Correlation Analysis (CCA)" method.

In each degree of degradation, the plant population variable set (i.e., the first vegetation characteristic set) is represented by a variable or vegetation index: the biomass comprises community aboveground biomass (C1), community underground biomass (0-60 cm) (C2), community litter (C3), community coverage (C4), community height (C5), Margelef abundance index (C6), Shannon-Wiener diversity index (C7), Simpson dominance index (C8) and Pielou uniformity index (C9);

the soil factor variable set (i.e., the first soil characteristic set) is defined by the variables or soil indices: soil water content (C10), soil volume weight (C11), soil pH (C12), soil organic carbon (C13), soil total nitrogen content (C14), soil total phosphorus content (C15), soil total potassium content (C16), C/N (C17), quick-acting nitrogen (C18), quick-acting phosphorus (C19), quick-acting potassium (C20), soil microbial carbon (C21) and soil microbial nitrogen (C22);

the soil indexes are all (0-10 cm) soil layer indexes.

First, principal component analysis

First, Principal Component Analysis (PCA) is respectively carried out on the first vegetation characteristic group and the first soil characteristic group under different utilization degrees. Selecting main components according to the standard that the value of the characteristic root is greater than 1:

the PCA analysis results of the first vegetation characteristic group under different degradation degrees (shown in Table 1) show that except that the cumulative contribution rate on the front four-dimensional principal component reaches 83.306% at the degradation G2 stage, the cumulative contribution rates on the front three-dimensional principal component of other degradation levels reach 78.148% (G0), 78.091% (G1), 75.011% (G2) and 89.116% (G3).

The factor load of each variable on the first three/four-dimensional principal component is the maximum (second vegetation characteristic group) respectively:

grazing zone (G0): community underground biomass (C2), community height (C5), Shannon-Wiener diversity index (C7), Simpson dominance index (C8) and Pielou uniformity index (C9);

a mild grazing utilization area (G1), community underground biomass (C2), Margelef abundance index (C6), Shannon-Wiener diversity index (C7), Simpson dominance index (C8) and Pielou evenness index (C9);

intermediate grazing utilization zone (G2): colony litter (C3), colony coverage (C4), Shannon-Wiener diversity index (C7), Simpson dominance index (C8), Pielou uniformity index (C9);

the extreme grazing utilization area (G3) is community underground biomass (C2), community coverage (C4), community height (C5), Shannon-Wiener diversity index (C7) and Simpson dominance index (C8);

TABLE 1 vegetation index PCA analysis results

The results of PCA analysis of the first soil characteristic group at different degrees of utilization degradation (as shown in Table 2) show that the cumulative contributions of the first four-dimensional principal components reach 76.863% (G0), 74.802% (G1), 74.483% (G2) and 72.179% (G3), respectively. The maximum factor load (second soil characteristic group) of each variable on the first four-dimensional principal component is:

grazing zone (G0): pH value (C12), soil organic carbon (C13), soil total nitrogen content (C14), soil total phosphorus content (C15), C/N (C17) and soil microorganism carbon (C21);

a light grazing utilization area (G1), namely soil volume weight (C11), pH value (C12), soil total phosphorus content (C15), C/N (C17), quick-acting potassium (C20) and soil microbial nitrogen (C22);

intermediate grazing utilization zone (G2): soil volume weight (C11), pH value (C12), soil total phosphorus content (C15), C/N (C17), quick-acting nitrogen (C18) and soil microbial nitrogen (C22);

the extreme grazing utilization area (G3) comprises soil water content (C10), pH value (C12), soil total nitrogen content (C14), soil total phosphorus content (C15), quick-acting phosphorus (C19) and soil microorganism carbon (C21).

TABLE 2 soil index PCA analysis results

Canonical correlation analysis

The basic idea of typical correlation is that the correlation between multiple variables and multiple variables can be converted into the correlation between two variables, and the correlation between the two synthesized variables is used to reflect the overall correlation between two sets of indexes. The method comprises the following specific steps:

first, whether two groups of variables (a second vegetation characteristic group and a second soil characteristic group) are related or not is checked, typical correlation analysis is carried out under the related condition,

1) first, a first pair of linear combinations is found in each group of variables, and u is made₁And v₁With the greatest correlation:

2) then, a second pair of linear combinations is found in each set of variables, which are respectively uncorrelated with the first linear combination in the set, and u is set₂And v₂With the next largest correlation:

in the formulae (1) and (2), u₁、u₂For a model of the vegetation index system, v₁、v₂Is a soil index system model; u. of₂And u₁Independently of each other, v₁And v₂Are independent of each other; but u₁And v₁Correlation u₂And v₂Correlation; x is the number of₁,x₂… denotes a vegetation index, y₁,y₂And … denotes a soil index, a₁₁,a₂₁,…，a_p1,a₁₂,a₂₂，…，a_p2And b₁₁,b₂₁,…，b_p1,b₁₂,b₂₂,…,b_p2The typical correlation coefficient is represented and obtained by a matrix under the condition of significant level correlation of the typical correlation, and the larger the absolute value of the typical correlation coefficient is, the larger the degree of reflecting information of the typical variable is;

repeating steps 1) and 2) to obtain a representative variable v_qAnd u_qThe method comprises the steps of extracting the variables in pairs from big to small according to the correlation coefficients of the variables, and obtaining q pairs of variables, namely q pairs of typical correlation variables, until the correlation between two groups of variables is extracted in q steps.

Finally, the significance of the correlation coefficients of each pair of typical correlation variables is checked to determine that the pairs of typical variables are reserved, and if the correlation degree of a certain pair is not significant, the pair of variables is not representative and can be ignored.

A first pair of typical correlation variables was selected for this study, with significant correlations (positive for positive coefficient and negative for negative coefficient) at α <0.05 levels. The expression of the correlation between the vegetation index and the soil index under different degradation degrees is as follows:

grazing zone (G0):

u₁＝0.752C2-0.688C5+0.761C7-0.496C8-0.562C9，

v₁＝-0.165C12-1.888C13+2.041C14-0.315C15+0.634C17-0.010C21，

light grazing utilization area (G1):

u₁＝-0.428C2-1.000C6-0.398C7+1.183C8-0.686C9，

v₁＝0.293C11+0.118C12-0.306C15-0.325C17-0.506C20+0.843C22，

intermediate grazing utilization zone (G2):

u₁＝-0.991C3+0.023C4+0.49C7-0.381C8-0.254C9，

v₁＝-0.125C11+0.564C12+0.157C15+0.09C17+0.437C18+0.119C22，

extreme grazing utilization area (G3):

u₁＝-0.385C2+0.652C4+0.132C5+0.593C7-0.361C8，

v₁＝1.097C10-0.455C12-0.009C14-0.049C15-0.206C19+0.005C21。

as can be seen from the above expression (i.e., the vegetation index and the soil index having relatively large absolute values of typical correlation coefficients are extracted as the third vegetation characteristic group and the third soil characteristic group that play a major role):

plant variables in the non-grazing zone (G0) are mainly determined by community underground biomass (C2), community height (C5), Shannon-Wiener diversity index (C7); the soil variables are mainly determined by soil organic carbon (C13), soil total nitrogen content (C14) and C/N (C17).

Plant variables in the mild grazing regression zone (G1) were determined primarily by the Margelef abundance index (C6), Simpson dominance index (C8), and Pielou uniformity index (C9); the soil variables are mainly determined by C/N (C17), quick-acting potassium (C20) and soil microbial nitrogen (C22).

Plant variables in the moderate grazing regression zone (G2) are mainly determined by community litter (C3), Shannon-Wiener diversity index (C7), Simpson dominance index (C8); the soil variables are mainly determined by pH value (C12), soil total phosphorus content (C15) and quick-acting nitrogen (C18).

Plant variables in the extreme grazing degenerated areas (G3) are mainly determined by community underground biomass (C2), community coverage (C4), Shannon-Wiener diversity index (C7); soil variables are mainly determined by soil moisture content (C10), pH (C12), and fast-acting phosphorus (C19).

Three, fuzzy comprehensive evaluation

Taking the data of the third vegetation characteristic group and the third soil characteristic group as a factor set and different degradation degrees as a processing set, carrying out fuzzy comprehensive evaluation on each meadow grassland with different degradation degrees, calculating a difference coefficient between the data of the third vegetation characteristic group and the third soil characteristic group of each meadow grassland with different degradation degrees, and calculating a fuzzy comprehensive evaluation coefficient d_iWherein, i is any number of natural numbers 1-n and represents meadow grasslands with different degradation degrees;

firstly, meadow grasslands with different degradation degrees are set as follows: x ═ X₁,X₂,…,X_i,…,X_n；

The factors influencing the grassland are set as follows: u is equal to U₁,U₂,…,U_j,…,U_m；

The feature matrix is U_n×m＝(U_ij)_n×m (3)；

r∈〔0,1〕；

The evaluation matrix R ═ R (R)_ij)_n×m (5)；

Secondly, calculating to obtain a difference coefficient between data of a third vegetation characteristic group and a third soil characteristic group of each meadow with different degradation degrees according to the evaluation matrix, wherein the calculation process is as follows:

the evaluation functions are taken as follows:

D₁＝1/m×(r_i1+r_i2+…+r_im) (6)；

D₂＝Max(r_i1,r_i2,…,r_im) (7)；

D₃＝Min(r_i1,r_i2,…,r_im) (8)；

respectively calculating the difference coefficient d_i1,d_i2,d_i3；

Let W₁＝(D₁，D₂，D₃)，R₁＝F(X×W₁) Namely:

finally, let d again_i＝1/3×(d_i1+d_i2+d_i3) (10)；

Calculating to obtain a fuzzy comprehensive evaluation coefficient d_iWherein d is_iE [ 0,1 ], where the grazing zone is an extremely stable grass field, d _i1 is ═ 1; the coefficient of each different degradation degree evaluation is between 0 and 1, d_iThe closer to 1, the closer to a very steady state the grass is.

1. Fuzzy comprehensive evaluation using vegetation index as system

By taking vegetation field survey data as a factor set and different degradation degrees as a processing set, the degradation degrees of the Chinese wildrye and the miscellaneous grass communities are subjected to fuzzy comprehensive evaluation as follows:

light grazing degenerated area (G1)

The fuzzy comprehensive evaluation matrix is U_2×3＝(U_ij)_2×3

Obtaining an evaluation matrix R ═ (R)_ij)_2×3

From the evaluation matrix R, the difference coefficients of the Margelef abundance index (C6), the Simpson dominance index (C8) and the Pielou uniformity index (C9) of the leymus chinensis + rough herd community light grazing degenerated area can be obtained.

R₁＝F(X×W₁) Namely, it is

Fuzzy comprehensive evaluation coefficient (degree of indication)

d₁1.000 grazing areas

d₂1.0557 slightly grazing degenerated area

From the fuzzy comprehensive evaluation result taking the 3 vegetation indexes of the Margelef abundance index (C6), the Simpson dominance index (C8) and the Pielou uniformity index (C9) as a system, the indication degree of the slow grazing degraded area of the sheep grass and the weed community is 1.0557, namely the vegetation degradation degree of the slow grazing degraded area is 105.57 percent of the vegetation degradation degree of the current non-grazing area.

Moderate grazing degradation zone (G2)

The fuzzy comprehensive evaluation matrix is U_2×3＝(U_ij)_2×3

Obtaining an evaluation matrix R ═ (R)_ij)_2×3

From the evaluation matrix R, we can obtain the difference coefficients of the community litter (C3), Shannon-Wiener diversity index (C7) and Simpson dominance index (C8) of the medium grazing degradation area of the leymus chinensis + weed community.

R₁＝F(X×W₁) Namely, it is

Fuzzy comprehensive evaluation coefficient (degree of indication)

d₁1.000 grazing areas

d₂0.7748 moderate grazing degradation zone

From the fuzzy comprehensive evaluation result of the system of 3 vegetation indexes of community litter (C3), Shannon-Wiener diversity index (C7) and Simpson dominance index (C8), the index of the medium grazing degradation zone of the Chinese wildrye and mixed grass community is 0.7748, namely the vegetation degradation degree of the medium grazing degradation zone is 77.48 percent of the vegetation degradation degree of the current non-grazing zone.

Extreme grazing degradation zone (G3)

The fuzzy comprehensive evaluation matrix is U_2×3＝(U_ij)_2×3

Obtaining an evaluation matrix R ═ (R)_ij)_2×3

From the evaluation matrix R, we can obtain the difference coefficients of the underground biomass of the community (C2), the community coverage (C4) and the Shannon-Wiener diversity index (C7) of the extremely grazing and degenerated area of the leymus and the weed community.

R₁＝F(X×W₁) Namely, it is

Fuzzy comprehensive evaluation coefficient (degree of indication)

d₁1.000 grazing areas

d₂0.7535 utmostGrazing degenerated area

From the results of fuzzy comprehensive evaluation taking 3 vegetation indexes of community underground biomass (C2), community coverage (C4) and Shannon-Wiener diversity index (C7) as a system, the indication degree of the extremely grazing degraded area of the Chinese wildrye and the weed community is 0.7535, namely the vegetation degradation degree of the extremely grazing degraded area is 75.35 percent of the vegetation degradation degree of the current non-grazing area.

2. Fuzzy comprehensive evaluation using soil index as system

Taking soil field investigation data as a factor set and different degradation degrees as a processing set, and carrying out fuzzy comprehensive evaluation on the degradation degree of the Chinese wildrye and the miscellaneous grass communities as follows:

light grazing degenerated area (G1)

The fuzzy comprehensive evaluation matrix is U_2×3＝(U_ij)_2×3

Obtaining an evaluation matrix R ═ (R)_ij)_2×3

From the evaluation matrix R, the difference coefficients of C/N (C17), quick-acting potassium (C20) and soil microbial nitrogen (C22) of the leymus chinensis + rough grass community light grazing degenerated area can be obtained.

R₁＝F(X×W₁) Namely, it is

Fuzzy comprehensive evaluation coefficient (degree of indication)

d₁1.000 grazing areas

d₂0.9814 mild grazing degradation zone

From the results of fuzzy comprehensive evaluation taking 3 soil indexes of C/N (C17), quick-acting potassium (C20) and soil microbial nitrogen (C22) as a system, the indication degree of the leymus and mixed grass community light grazing degraded area is 0.9814, namely the vegetation degradation degree of the light grazing degraded area is 98.14 percent of the vegetation degradation degree of the current non-grazing area.

Moderate grazing degradation zone (G2)

The fuzzy comprehensive evaluation matrix is U_2×3＝(U_ij)_2×3

Obtaining an evaluation matrix R ═ (R)_ij)_2×3

From the evaluation matrix R, the difference coefficients of the soil pH value (C12), the soil total phosphorus content (C15) and the quick-acting nitrogen (C18) of the medium grazing degradation area of the leymus chinensis and the weed grass community can be obtained.

R₁＝F(X×W₁) Namely, it is

Fuzzy comprehensive evaluation coefficient (degree of indication)

d₁1.000 grazing areas

d₂1.0035 moderate grazing degradation zone

From the fuzzy comprehensive evaluation result taking 3 soil indexes of pH value (C12), soil total phosphorus content (C15) and quick-acting nitrogen (C18) as a system, the indication degree of the middle grazing degraded area of the leymus chinensis and mixed grass community is 1.0035, namely the vegetation degradation degree of the middle grazing degraded area is 100.35 percent of the current non-grazing area.

Extreme grazing degradation zone (G3)

The fuzzy comprehensive evaluation matrix is U_2×3＝(U_ij)_2×3

Obtaining an evaluation matrix R ═ (R)_ij)_2×3

From the evaluation matrix R, the difference coefficients of the soil moisture content (C10), the pH value (C12) and the available phosphorus (C19) of the guinea grass + mixed grass community extreme grazing degradation area can be obtained.

R₁＝F(X×W₁) Namely, it is

Fuzzy comprehensive evaluation coefficient (degree of indication)

d₁1.000 grazing areas

d₂0.9376 degradation area of extreme grazing

From the fuzzy comprehensive evaluation result of the system of 3 soil indexes of soil moisture content (C10), pH value (C12) and quick-acting phosphorus (C19), the indication degree of the sheep grass and the weed grass community in the extremely grazing degraded area is 0.9376, namely the vegetation degradation degree of the extremely grazing degraded area is 93.76 percent of the vegetation degradation degree of the current non-grazing area.

3. Fuzzy comprehensive evaluation using vegetation and soil indexes as system

The vegetation index and the soil index obtained by the analysis are factor sets, different degradation degrees are processing sets, and the degradation degree of the Chinese wildrye and mixed grass community is subjected to fuzzy comprehensive evaluation as follows:

light grazing degenerated area (G1)

The fuzzy comprehensive evaluation matrix is U_2×6＝(U_ij)_2×6

Obtaining an evaluation matrix R ═ (R)_ij)_2×6

From the evaluation matrix R, the difference coefficients of the Margelef abundance index (C6), the Simpson dominance index (C8), the Pielou uniformity index (C9), the C/N (C17), the quick-acting potassium (C20) and the soil microbial nitrogen (C22) of the leymus plus all-grass colony light grazing degenerated area can be obtained.

R₁＝F(X×W₁) Namely, it is

Fuzzy comprehensive evaluation coefficient (degree of indication)

d₁1.000 grazing areas

d₂0.9922 slightly grazing degenerated area

From the results of fuzzy comprehensive evaluation taking 6 indexes of Margelef abundance index (C6), Simpson dominance index (C8), Pielou uniformity index (C9), C/N (C17), quick-acting potassium (C20) and soil microbial nitrogen (C22) as a system, the index of the leymus and mixed grass community mild grazing degradation area is 0.9922, namely the degradation degree of vegetation and soil indexes in the mild grazing degradation area is 99.22% of that in the current non-grazing area.

Moderate grazing degradation zone (G2)

The fuzzy comprehensive evaluation matrix is U_2×6＝(U_ij)_2×6

Obtaining an evaluation matrix R ═ (R)_ij)_2×6

From the evaluation matrix R, the difference coefficients of the litter (C3) of the middle grazing degenerated area group of the Chinese wildrye and the weed community, the Shannon-Wiener diversity index (C7), the Simpson dominance index (C8), the pH value (C12), the soil total phosphorus content (C15) and the quick-acting nitrogen (C18) can be obtained.

R₁＝F(X×W₁) Namely, it is

Fuzzy comprehensive evaluation coefficient (degree of indication)

d₁1.000 grazing areas

d₂0.8025 moderate grazing degradation zone

From the fuzzy comprehensive evaluation result of a system with 6 indexes of community litter (C3), Shannon-Wiener diversity index (C7), Simpson dominance index (C8), pH value (C12), soil total phosphorus content (C15) and quick-acting nitrogen (C18), the index degree of the medium grazing degradation area of the leymus and the weed community is 0.8025, namely the degradation degree of vegetation and soil indexes in the medium grazing degradation area is 80.25 percent of the current non-grazing area.

Extreme grazing degradation zone (G3)

The fuzzy comprehensive evaluation matrix is U_2×6＝(U_ij)_2×6

Obtaining an evaluation matrix R ═ (R)_ij)_2×6

From the evaluation matrix R, the difference coefficients of biomass (C2), community coverage (C4), Shannon-Wiener diversity index (C7), soil water content (C10), pH value (C12) and available phosphorus (C19) of the leymus and miscellaneous grass community under the ground of the extreme grazing degenerated community can be obtained.

R₁＝F(X×W₁) Namely, it is

Fuzzy comprehensive evaluation coefficient (degree of indication)

d₁1.000 grazing areas

d₂0.7914 degradation area of extreme grazing

From the results of fuzzy comprehensive evaluation taking 6 indexes of community underground biomass (C2), community coverage (C4), Shannon-Wiener diversity index (C7), soil water content (C10), pH value (C12) and quick-acting phosphorus (C19) as a system, the index of the extremely grazing and degenerated area of the leymus and the weed community is 0.7914, namely the index of the vegetation and soil indexes in the extremely grazing and degenerated area is 79.14 percent of the index of the vegetation and soil in the existing non-grazing area.

And (4) conclusion:

according to the fuzzy comprehensive evaluation result taking the vegetation index as a system, the indication degree of the leymus and mixed grass community mild grazing degraded area is 1.0557, namely the vegetation degradation degree of the mild grazing degraded area is 105.57% of the vegetation degradation degree of the current non-grazing area. The indication degree of the moderate grazing degradation area is 0.7748, namely the vegetation degradation degree of the moderate grazing degradation area is 77.48 percent of the current non-grazing area. The indication degree of the extremely grazing degraded area is 0.7535, namely the vegetation degradation degree of the extremely grazing degraded area is 75.35 percent of the vegetation degradation degree of the current non-grazing area.

According to the fuzzy comprehensive evaluation result taking the soil index as a system, the indication degree of the leymus chinensis and mixed grass community mild grazing degraded area is 0.9814, namely the vegetation degradation degree of the mild grazing degraded area is 98.14% of the vegetation degradation degree of the current non-grazing area. The indication degree of the moderate grazing degradation zone is 1.0035, namely the vegetation degradation degree of the moderate grazing degradation zone is 100.35 percent of the vegetation degradation degree of the current non-grazing zone. The indication degree of the extremely grazing degraded area is 0.9376, namely the vegetation degradation degree of the extremely grazing degraded area is 93.76 percent of the current non-grazing area.

The fuzzy comprehensive evaluation result of an index system combining the vegetation index and the soil index shows that the index degree of the leymus chinensis + mixed grass community mild grazing degraded area is 0.9922, namely the degradation degree of the vegetation and soil index in the mild grazing degraded area is 99.22 percent of the degradation degree of the vegetation and soil index in the current non-grazing area. The degree of degradation of vegetation and soil indexes in the moderate grazing degraded area is 0.8025, namely the degree of degradation of vegetation and soil indexes in the moderate grazing degraded area is 80.25 percent of the degree of the current non-grazing area. The degree of degradation of the extremely grazing degraded area is 0.7914, namely the degree of degradation of vegetation and soil indexes of the extremely grazing degraded area is 79.14 percent of the degree of degradation of the current non-grazing area.

According to the indication degree obtained by combining the plant community and the soil into an index system fuzzy comprehensive evaluation, the non-grazing utilization is an extremely stable grade, the light grazing utilization area is a stable grade, the medium grazing utilization degradation area is an unstable grade, the extreme grazing utilization degradation area is an extremely unstable grade, the indication degree threshold values of the degradation of different grazing utilization degrees of the leymus chinensis and the weed community are 1-0.9922, 0.9922-0.8025, 0.8025-0.7914 and 0,7914-0 in sequence, namely: if the meadow grassland to be evaluated has the index d_i，

When 1 is more than or equal to d_iWhen the degradation degree of the meadow grassland to be evaluated is more than 0.9922, judging the degradation degree of the meadow grassland to be evaluated to be an extremely stable level;

when 0.9922 is more than or equal to d_iWhen the degradation degree of the meadow grassland to be evaluated is more than 0.8025, judging the degradation degree of the meadow grassland to be evaluated to be a stable level;

when 0.8025 is more than or equal to d_iWhen the meadow degradation degree is more than 0.7914, judging the meadow degradation degree to be evaluated to be an unstable grade;

when 0.7914 is more than or equal to d_iAnd (4) being more than or equal to 0, judging the degradation degree of the meadow grassland to be evaluated to be an extremely unstable grade.

Example 3 method test

Taking the data which is not used for establishing the model of the embodiment 2 in the embodiment 1 as the data of the meadow grassland to be evaluated, and calculating according to the method of the embodiment 2 to obtain the indication degree d_iAnd judging the degradation level of the meadow grassland to be evaluated according to the threshold value of the embodiment 2, wherein the result completely conforms to the actual result. The method has good accuracy.

Those not described in detail in this specification are within the skill of the art. The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims (15)

1. A method for establishing a model for evaluating different degradation degrees of meadow grassland comprises the following steps:

s1, selecting variable data of meadow grasslands with different degradation degrees, wherein the variable data comprise a first vegetation characteristic group and a first soil characteristic group, and the meadow grasslands with different degradation degrees are obtained by taking natural steppes as grazing sample plots and performing grazing intensity gradient treatment to cause obvious regions of a series of slight degradation, moderate degradation and severe degradation;

s2, respectively carrying out principal component analysis on the first vegetation characteristic group and the first soil characteristic group of each meadow with different degradation degrees, selecting principal components according to the standard that the value of the characteristic root is more than 1, and obtaining a second vegetation characteristic group with relatively large factor load on the principal component with the same dimensionality and a second soil characteristic group with relatively large factor load on the principal component with the same dimensionality;

s3, performing typical correlation analysis on the second vegetation characteristic group and the second soil characteristic group of each meadow with different degradation degrees, and extracting a third vegetation characteristic group and a third soil characteristic group which play a main determining role;

s4, taking the data of the third vegetation characteristic group and the third soil characteristic group as a factor set, taking different degradation degrees as a processing set, carrying out fuzzy comprehensive evaluation on each meadow grassland with different degradation degrees, calculating difference coefficients between the data of the third vegetation characteristic group and the third soil characteristic group of each meadow grassland with different degradation degrees, and calculating to obtain fuzzy comprehensive evaluation coefficients d_iWherein, i takes any number from 1 to n in nature and represents meadow grasslands with different degradation degrees;

s5, defining different fuzzy comprehensive evaluation coefficients d_iThe numerical range of (A) and the different degradation degrees of the meadow grassland corresponding to the numerical range of (A) are evaluatedEstimating models of different degradation degrees of meadow grassland;

the fuzzy comprehensive evaluation in the step S4 includes the following steps:

firstly, meadow grasslands with different degradation degrees are set as follows: x ═ X₁,X₂,…,X_i,…,X_n；

The factors influencing the grassland are set as follows: u is equal to U₁,U₂,…,U_j,…,U_m；

The feature matrix is U_n×m＝(U_ij)_n×m (3)；

r∈〔0,1〕；

The evaluation matrix R ═ R (R)_ij)_n×m (5)；

Secondly, calculating to obtain a difference coefficient between data of a third vegetation characteristic group and a third soil characteristic group of each meadow with different degradation degrees according to the evaluation matrix, wherein the calculation process is as follows:

the evaluation functions are taken as follows:

D₁＝1/m×(r_i1+r_i2+…+r_im) (6)；

D₂＝Max(r_i1,r_i2,…,r_im) (7)；

D₃＝Min(r_i1,r_i2,…,r_im) (8)；

respectively calculating the difference coefficient d_i1,d_i2,d_i3；

Let W₁＝(D₁，D₂，D₃)，R₁＝F(X×W₁) Namely:

finally, let d again_i＝1/3×(d_i1+d_i2+d_i3) (10)；

Calculating to obtain a fuzzy comprehensive evaluation coefficient d_iWherein d is_i∈〔0,1〕。

2. The method of claim 1, wherein: the typical correlation analysis in step S3 includes the following steps:

firstly, checking whether two groups of variables of a second vegetation characteristic group and a second soil characteristic group of each meadow with different degradation degrees are related, and carrying out the following steps under the related condition:

s31, finding out a first pair of linear combinations in the variables of the second vegetation characteristic group and the second soil characteristic group respectively, and enabling the linear combinations to be u as shown in the following formula (1)₁And v₁With the greatest correlation:

s32, and then finding out a second pair of linear combinations in the variables of the second vegetation characteristic group and the second soil characteristic group as the following formula (2), so that the linear combinations are respectively not related to the first linear combinations in the group, and the u of the linear combinations are₂And v₂With the next largest correlation:

in the formulae (1) and (2), u₁、u₂For a model of the vegetation index system, v₁、v₂Is a soil index system model; u. of₂And u₁Independently of each other, v₁And v₂Are independent of each other; but u₁And v₁Correlation u₂And v₂Correlation; x is the number of₁,x₂… denotes a vegetation index in the second vegetation feature group, y₁,y₂And … denotes a soil index in the second soil characteristic group, a₁₁,a₂₁,…，a_p1,a₁₂,a₂₂，…，a_p2And b₁₁,b₂₁,…，b_p1,b₁₂,b₂₂,…,b_p2The typical correlation coefficient is represented and obtained by a matrix under the condition of significant level correlation of the typical correlation, and the larger the absolute value of the typical correlation coefficient is, the larger the degree of reflecting information of the typical variable is;

s33, repeating S31 and S32 to obtain a typical variable v_qAnd u_qExtracting the variables pair by pair from big to small according to the correlation coefficients of the variables pair by pair until the correlation between two groups of variables is extracted in the step q, and obtaining q pairs of typical correlation variables; 1-q pairs of typical correlation variables with significance of the correlation coefficients of the typical correlation variables are reserved;

and extracting vegetation indexes and soil indexes with relatively large absolute values of typical correlation coefficients from the 1-q pairs of typical correlation variables as a third vegetation characteristic group and a third soil characteristic group which play a main decision role.

3. The method of claim 1 or 2, wherein: in step S1, the first vegetation feature group includes the following vegetation indexes: community aboveground biomass (C1), community underground biomass (0-60 cm) (C2), community litter (C3), community coverage (C4), community height (C5), Margelef abundance index (C6), Shannon-Wiener diversity index (C7), Simpson dominance index (C8) and Pielou uniformity index (C9);

and/or the first soil characteristic group comprises the following soil indexes: soil moisture content (C10), soil volume weight (C11), soil pH (C12), soil organic carbon (C13), soil total nitrogen content (C14), soil total phosphorus content (C15), soil total potassium content (C16), C/N (C17), quick-acting nitrogen (C18), quick-acting phosphorus (C19), quick-acting potassium (C20), soil microbial carbon (C21) and soil microbial nitrogen (C22), and indexes in the first soil characteristic group are indexes of 0-10cm soil layers.

4. The method of claim 3, wherein: the meadow grasslands with different degradation degrees comprise non-grazing areas, light grazing degradation areas, medium grazing degradation areas and extreme grazing degradation areas;

wherein the non-grazing area is an extremely stable grassland, d_i1 is ═ 1; the coefficient of each different degradation degree evaluation is between 0 and 1, d_iThe closer to 1, the closer to a very steady state the grass is.

5. The method of claim 1, wherein: the meadow grasslands with different degradation degrees comprise non-grazing areas, light grazing degradation areas, medium grazing degradation areas and extreme grazing degradation areas;

wherein the non-grazing area is an extremely stable grassland, d_i1 is ═ 1; the coefficient of each different degradation degree evaluation is between 0 and 1, d_iThe closer to 1, the closer to a very steady state the grass is.

6. The method of claim 2, wherein: the meadow grasslands with different degradation degrees comprise non-grazing areas, light grazing degradation areas, medium grazing degradation areas and extreme grazing degradation areas;

wherein the non-grazing area is an extremely stable grassland, d_i1 is ═ 1; the coefficient of each different degradation degree evaluation is between 0 and 1, d_iThe closer to 1, the closer to a very steady state the grass is.

7. The method of any of claims 4-6, wherein: in step S2, the second vegetation feature group in the non-grazing area includes the following vegetation indexes: community underground biomass (C2), community height (C5), Shannon-Wiener diversity index (C7), Simpson dominance index (C8) and Pielou uniformity index (C9); the second soil characteristic group comprises the following soil indexes: pH value (C12), soil organic carbon (C13), soil total nitrogen content (C14), soil total phosphorus content (C15), C/N (C17) and soil microorganism carbon (C21);

the second vegetation characteristic group of the mild grazing utilization area comprises the following vegetation indexes: community underground biomass (C2), Margelef abundance index (C6), Shannon-Wiener diversity index (C7), Simpson dominance index (C8), Pielou uniformity index (C9); the second soil characteristic group comprises the following soil indexes: soil volume weight (C11), pH value (C12), soil total phosphorus content (C15), C/N (C17), quick-acting potassium (C20) and soil microbial nitrogen (C22);

the second vegetation feature group of the moderate grazing utilization area comprises the following vegetation indexes: colony litter (C3), colony coverage (C4), Shannon-Wiener diversity index (C7), Simpson dominance index (C8), Pielou uniformity index (C9); the second soil characteristic group comprises the following soil indexes: soil volume weight (C11), pH value (C12), soil total phosphorus content (C15), C/N (C17), quick-acting nitrogen (C18) and soil microbial nitrogen (C22);

the second vegetation feature group of the extreme grazing utilization area comprises the following vegetation indexes: community underground biomass (C2), community coverage (C4), community height (C5), Shannon-Wiener diversity index (C7) and Simpson dominance index (C8); the second soil characteristic group comprises the following soil indexes: the soil water content (C10), the pH value (C12), the soil total nitrogen content (C14), the soil total phosphorus content (C15), the quick-acting phosphorus (C19) and the soil microorganism carbon (C21).

8. The method of any of claims 4-6, wherein: in the step S3, in the step S,

the third vegetation characteristic group of the non-grazing area comprises the following vegetation indexes: community underground biomass (C2), community height (C5), Shannon-Wiener diversity index (C7); the third soil characteristic group comprises the following soil indexes: soil organic carbon (C13), soil total nitrogen content (C14), C/N (C17);

the third vegetation characteristic group of the mild grazing degradation area comprises the following vegetation indexes: a Margelef abundance index (C6), a Simpson dominance index (C8), a Pielou evenness index (C9); the third soil characteristic group comprises the following soil indexes: C/N (C17), quick-acting potassium (C20), soil microbial nitrogen (C22);

the third vegetation feature group of the moderate grazing degradation zone comprises the following vegetation indexes: community litter (C3), Shannon-Wiener diversity index (C7), Simpson dominance index (C8); the third soil characteristic group comprises the following soil indexes: pH value (C12), soil total phosphorus content (C15), and quick-acting nitrogen (C18);

the third vegetation feature group of the extreme grazing utilization area comprises the following vegetation indexes: community underground biomass (C2), community coverage (C4), Shannon-Wiener diversity index (C7); the third soil characteristic group comprises the following soil indexes: soil moisture content (C10), pH value (C12), and quick-acting phosphorus (C19).

9. The method of claim 7, wherein: in the step S3, in the step S,

the third vegetation characteristic group of the non-grazing area comprises the following vegetation indexes: community underground biomass (C2), community height (C5), Shannon-Wiener diversity index (C7); the third soil characteristic group comprises the following soil indexes: soil organic carbon (C13), soil total nitrogen content (C14), C/N (C17);

the third vegetation characteristic group of the mild grazing degradation area comprises the following vegetation indexes: a Margelef abundance index (C6), a Simpson dominance index (C8), a Pielou evenness index (C9); the third soil characteristic group comprises the following soil indexes: C/N (C17), quick-acting potassium (C20), soil microbial nitrogen (C22);

the third vegetation feature group of the moderate grazing degradation zone comprises the following vegetation indexes: community litter (C3), Shannon-Wiener diversity index (C7), Simpson dominance index (C8); the third soil characteristic group comprises the following soil indexes: pH value (C12), soil total phosphorus content (C15), and quick-acting nitrogen (C18);

the third vegetation feature group of the extreme grazing utilization area comprises the following vegetation indexes: community underground biomass (C2), community coverage (C4), Shannon-Wiener diversity index (C7); the third soil characteristic group comprises the following soil indexes: soil moisture content (C10), pH value (C12), and quick-acting phosphorus (C19).

10. The method of claim 7, wherein: in step S5, the following is specifically defined:

when 1 is more than or equal to d_iWhen the degradation degree of the meadow grassland to be evaluated is more than 0.9922, judging the degradation degree of the meadow grassland to be evaluated to be an extremely stable level;

when 0.9922 is more than or equal to d_iWhen the degradation degree of the meadow grassland to be evaluated is more than 0.8025, judging the degradation degree of the meadow grassland to be evaluated to be a stable level;

when 0.8025 is more than or equal to d_iWhen the meadow degradation degree is more than 0.7914, judging the meadow degradation degree to be evaluated to be an unstable grade;

when 0.7914 is more than or equal to d_iAnd (4) being more than or equal to 0, judging the degradation degree of the meadow grassland to be evaluated to be an extremely unstable grade.

11. The method of claim 8, wherein: in step S5, the following is specifically defined:

when 1 is more than or equal to d_iWhen the degradation degree of the meadow grassland to be evaluated is more than 0.9922, judging the degradation degree of the meadow grassland to be evaluated to be an extremely stable level;

when 0.9922 is more than or equal to d_iWhen the degradation degree of the meadow grassland to be evaluated is more than 0.8025, judging the degradation degree of the meadow grassland to be evaluated to be a stable level;

when 0.8025 is more than or equal to d_iWhen the meadow degradation degree is more than 0.7914, judging the meadow degradation degree to be evaluated to be an unstable grade;

when 0.7914 is more than or equal to d_iAnd (4) being more than or equal to 0, judging the degradation degree of the meadow grassland to be evaluated to be an extremely unstable grade.

12. The method of claim 3, wherein: in step S5, the following is specifically defined:

when 1 is more than or equal to d_iWhen the degradation degree of the meadow grassland to be evaluated is more than 0.9922, judging the degradation degree of the meadow grassland to be evaluated to be an extremely stable level;

when 0.9922 is more than or equal to d_iWhen the degradation degree of the meadow grassland to be evaluated is more than 0.8025, judging the degradation degree of the meadow grassland to be evaluated to be a stable level;

when 0.8025 is more than or equal to d_iWhen the meadow degradation degree is more than 0.7914, judging the meadow degradation degree to be evaluated to be an unstable grade;

when 0.7914 is more than or equal to d_iAnd (4) being more than or equal to 0, judging the degradation degree of the meadow grassland to be evaluated to be an extremely unstable grade.

13. The method of any one of claims 1, 2, 4-6, and 9, wherein: in step S5, the following is specifically defined:

when 1 is more than or equal to d_iWhen the degradation degree of the meadow grassland to be evaluated is more than 0.9922, judging the degradation degree of the meadow grassland to be evaluated to be an extremely stable level;

when 0.9922 is more than or equal to d_i> 0.8025, judgmentThe degradation degree of the meadow grassland to be evaluated is a stable grade;

when 0.8025 is more than or equal to d_iWhen the meadow degradation degree is more than 0.7914, judging the meadow degradation degree to be evaluated to be an unstable grade;

when 0.7914 is more than or equal to d_iAnd (4) being more than or equal to 0, judging the degradation degree of the meadow grassland to be evaluated to be an extremely unstable grade.

14. A model device for evaluating the various degrees of deterioration of meadow, set up by the method of any one of claims 1 to 13.

15. Use of the model device of claim 14 for evaluating the degree of different degradation of meadow grasses.

Images