CN109829650A - Assess Meadow difference degree of degeneration model and its method for building up and application - Google Patents
Assess Meadow difference degree of degeneration model and its method for building up and application Download PDFInfo
- Publication number
- CN109829650A CN109829650A CN201910101139.8A CN201910101139A CN109829650A CN 109829650 A CN109829650 A CN 109829650A CN 201910101139 A CN201910101139 A CN 201910101139A CN 109829650 A CN109829650 A CN 109829650A
- Authority
- CN
- China
- Prior art keywords
- soil
- group
- degree
- index
- grazing
- 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.)
- Granted
Links
Landscapes
- Cultivation Of Plants (AREA)
Abstract
This application discloses a kind of assessment Meadow difference degree of degeneration model and its method for building up and applications.The application is directed to the natural meadow of difference degree of degeneration caused by Meadow difference producing level, it is controlled and is tested by field, using field Ecological Investigation method, study the Response Mechanism that Meadow is degenerated to mankind's activity (control is herded), using principal component analysis, canonical correlation and fuzzy mathematics establish matrix, establish different producing level degradation assessment system instruction degree diagnostic methods, screen different application practice difference degree of degeneration grassland vegetation and soil root system system, obtain the instruction degree of different degree of degenerations, the scientific and stronger Meadow of operability is established to degenerate and quantify evaluation index system instruction degree diagnostic method.The objective judgement of the degraded condition of foundation, Meadow that the application assesses Meadow solum pattern and the production of propulsion grassland agriculture and environmental protection have the meaning of reality.
Description
Technical field
The present invention relates to Grassland ecosystems protect field, and in particular to assessment Meadow difference degree of degeneration model and
Its method for building up and application.
Background technique
6,000,000,000 mu of China grassland area, accounts for the 41.7% of national territorial area, wherein northern grassy marshland and Meadow area are about
5.96 hundred million mu, be north China pastures productivity highest, the most abundant grassland of diversity, and domestic animal bearing capacity is equivalent to other downlands
The summation of type, ecology and production function all have very important status.Relative to arid biogeographic zone grassland types, Meadow
Land use systems are more polynary, and degenerative process and mechanism are more complicated, and since the 1980s, 90% or more China grassland is moved back
Change, the productivity of northern Meadow declines 20-50%, 15% or more high quality forage ratio decline.But since height produces
Power, the appearance features of highly diverse, the practical degree of degeneration of Meadow are seriously underestimated, and special degeneration mark is theoretically lacked
Quasi- and Renew theory, does not form complete ecological management and sustainable utilization technology system using upper, and it is high to constrain northern grassy marshland
Productivity advantage plays and region grass animal husbandry development.
Therefore, with the expansion and aggravation of grassland degeneration area, the recovery and reconstruction to degeneration meadow are extremely urgent, before
It mentions and basis is then the diagnosis for carrying out science to Grassland degradation degree.Currently, still lacking comprehensive and simple and practical meadow system
Degree of degeneration system diagnosis method.
Summary of the invention
In view of the deficiencies in the prior art, the purpose of the present invention is to provide a kind of foundation assessment Meadow is different
The method of the model of degree of degeneration.The model that this method obtains especially understands the degraded condition of objective judgement Meadow
Different degree of degenerations influence situation to meadow, and grassland agriculture production and environmental protection is promoted to have the meaning of reality.
To achieve the above objectives, the technical solution adopted by the present invention is that:
The present invention provides a kind of method of model for establishing assessment Meadow difference degree of degeneration, includes the following steps:
The variable data of S1, the different degree of degeneration Meadows of selection, the variable data include the first vegetation characteristics group and the first soil
Earth feature group；
S2, the first vegetation characteristics group of each different degree of degeneration Meadows and the first soil characteristic group are carried out respectively
Principal component analysis, the value according to characteristic root are greater than 1 and choose principal component for standard, obtain the factor loading in identical dimensional principal component
Relatively large second vegetation characteristics group of amount (i.e. accumulation contribution rate) and factor load is relatively in identical dimensional principal component
The second big soil characteristic group；
S3, typical case is carried out to the second vegetation characteristics group of each different degree of degeneration Meadows and the second soil characteristic group
The third vegetation characteristics group and third soil characteristic group of main decisive action have been extracted in correlation analysis；
S4, using the data of third vegetation characteristics group and third soil characteristic group as set of factors, different degree of degenerations be processing
Collection carries out fuzzy overall evaluation to the Meadow of each different degree of degenerations, each different degree of degeneration grassy marshlands is calculated
Difference property coefficient between meadow third vegetation characteristics group and the data of third soil characteristic group, and fuzzy synthesis is calculated and comments
Valence coefficient di(i.e. instruction degree), wherein i takes any number in 1-n of natural number, represents different degree of degeneration Meadows；(mould
Paste System of Comprehensive Evaluation is to carry out the basis of overall merit, and whether the selection of evaluation index is suitable for that will directly affect synthesis
The accuracy of evaluation)
S5, different fuzzy overall evaluation coefficient ds are definediNumberical range and its corresponding Meadow difference degeneration journey
Degree obtains the model of assessment Meadow difference degree of degeneration.
In the above-mentioned methods, canonical correlation analysis includes the following steps: in step S3
Firstly, examining two groups of the second vegetation characteristics group and the second soil characteristic group of each different degree of degeneration Meadows
Whether variable is related, and following steps are carried out in relevant situation:
S31, first pair of linear combination is found out such as in the variable of the second vegetation characteristics group and the second soil characteristic group respectively
Following formula (1), makes its u1And v1With maximum correlation:
S32, second pair of linear combination is then found out in the variable of the second vegetation characteristics group and the second soil characteristic group again
Such as following formula (2), it is uncorrelated to the first linear combination in this group respectively to make it, and makes its u2And v2With secondary big correlation:
In formula (1) and (2), u1、u2For vegetation Index System Model, v1、v2For soil root system system model； u2And u1Phase
It is mutually independent, v1With v2Independently of each other；But u1And v1Correlation, u2And v2It is related；x1,x2... indicate the vegetation in the second vegetation characteristics group
Index, y1,y2... indicate the soil root system in the second soil characteristic group, a11,a21..., ap1,a12,a22..., ap2And b11,
b21..., bp1,b12,b22,…,bp2It indicates canonical correlation coefficient, is to do matrix institute in the relevant situation of the canonical correlation level of signifiance
, the absolute value of canonical correlation coefficient is bigger, illustrates that canonical variable reflection extent of information is bigger；
S33, repeat S31 and S32, the canonical variable v of acquisitionqAnd uqIt is descending according to their related coefficient
By to extraction, until the correlation proceeded between q two groups of variables of step, which is extracted, to be finished, available q is to canonical correlation
Variable；Retain each pair of canonical correlation variant correlation coefficient and has 1-q of conspicuousness to canonical correlation variable；
From 1-q to the relatively large vegetation index of extraction canonical correlation coefficient absolute value and soil in canonical correlation variable
Index is as the third vegetation characteristics group and third soil characteristic group for playing main decisive action.
In the above-mentioned methods, the fuzzy overall evaluation in step S4 specifically comprises the following steps:
Firstly, setting different degree of degeneration Meadows are as follows: X=X1,X2,…,Xi,…,Xn；
If items influence the set of factors on meadow are as follows: U=U1,U2,…,Uj,…,Um；
Eigenmatrix is Un×m=(Uij)n×m(3)；
r∈〔0,1〕；
Evaluations matrix R=(rij)n×m(5)；
Secondly, each different degree of degeneration Meadow third vegetation characteristics groups and third is calculated according to evaluations matrix
Difference property coefficient between the data of soil characteristic group, calculating process are as follows:
Evaluation function is taken to be respectively as follows:
D1=1/m × (ri1+ri2+…+rim) (6)；
D2=Max (ri1,ri2,…,rim) (7)；
D3=Min (ri1,ri2,…,rim) (8)；
Calculate separately to obtain otherness coefficient di1,di2,di3；
Enable U1=(D1, D2, D3), R1=F (X × U1) i.e.:
Finally, enabling d againi=1/3 × (di1+di2+di3) (10)；
Fuzzy overall evaluation coefficient d is calculatedi, wherein di∈〔0,1〕。
In the above-mentioned methods, it is preferable that include following vegetation index: locality in the first vegetation characteristics group in step S1
Upper biomass (C1), group's underground biomass (0~60cm) (C2), group's Litter-fall (C3), cover degree of communities (C4), group's height
(C5), Margelef diversity index (C6), Shannon-Wiener diversity indices (C7), Simpson dominance index
(C8) and Pielou evenness index (C9)；
It and/or include following soil root system: soil moisture content (C10), the soil weight in the first soil characteristic group
(C11), soil pH (C12), soil organic matter (C13), total nitrogen content of soil (C14), Soil total nitrogen (C15), soil are complete
Potassium content (C16), C/N (C17), available nitrogen (C18), rapid available phosphorus (C19), available potassium (C20), Soil Microbial Biomass Carbon (C21) and
Soil microbial nitrogen (C22)；, the index in the first soil characteristic group is the index of 0-10cm soil layer.
In the above-mentioned methods, it is preferable that the difference degree of degeneration Meadow includes not grazing district, slight grazing degradated
Area, moderate grazing degradated area and extreme over-grazing degenerate region；Wherein, grazing district is not extremely stable meadow, di=1；Each difference is moved back
Change the coefficient of degree evaluation between 0~1, diCloser to 1, meadow is closer to extremely stable state.
In the above-mentioned methods, in step S2, it is preferable that do not include that following vegetation refers in the second vegetation characteristics group of grazing district
Mark: group's underground biomass (C2), group's height (C5), Shannon-Wiener diversity indices (C7), Simpson dominance
Index (C8), Pielou evenness index (C9)；It include following soil root system: pH value (C12), soil in second soil characteristic group
Organic carbon (C13), total nitrogen content of soil (C14), Soil total nitrogen (C15), C/N (C17), Soil Microbial Biomass Carbon (C21)；
Include following vegetation index in the second vegetation characteristics group in slight grazing utilization area: group's underground biomass (C2),
Margelef diversity index (C6), Shannon-Wiener diversity indices (C7), Simpson dominance index (C8),
Pielou evenness index (C9)；It include following soil root system: the soil weight (C11), pH value in second soil characteristic group
(C12), Soil total nitrogen (C15), C/N (C17), available potassium (C20), Soil microbial nitrogen (C22)；
It include following vegetation index: group's Litter-fall (C3), group in the second vegetation characteristics group in moderate grazing utilization area
Cover degree (C4), Shannon-Wiener diversity indices (C7), Simpson dominance index (C8), Pielou evenness index
(C9)；It include following soil root system: the soil weight (C11), pH value (C12), Soil total nitrogen in second soil characteristic group
(C15), C/N (C17), available nitrogen (C18), Soil microbial nitrogen (C22)；
It includes following vegetation index in the second vegetation characteristics group in area that extreme over-grazing, which utilizes: group's underground biomass (C2),
Cover degree of communities (C4), group's height (C5), Shannon-Wiener diversity indices (C7), Simpson dominance index
(C8)；It include following soil root system: soil moisture content (C10), pH value (C12), total nitrogen content of soil in second soil characteristic group
(C14), Soil total nitrogen (C15), rapid available phosphorus (C19), Soil Microbial Biomass Carbon (C21).
In the above-mentioned methods, in step S3, it is preferable that do not include that following vegetation refers in the third vegetation characteristics group of grazing district
Mark: group's underground biomass (C2), group's height (C5), Shannon-Wiener diversity indices (C7)；Third soil characteristic
It include following soil root system: soil organic matter (C13), total nitrogen content of soil (C14), C/N (C17) in group；
It include following vegetation index: Margelef diversity index in the third vegetation characteristics group in slight grazing degradated area
(C6), Simpson dominance index (C8), Pielou evenness index (C9)；It include following soil in third soil characteristic group
Index: C/N (C17), available potassium (C20), Soil microbial nitrogen (C22)；
Include following vegetation index in the third vegetation characteristics group in moderate grazing degradated area: group's Litter-fall (C3),
Shannon-Wiener diversity indices (C7), Simpson dominance index (C8)；It include following soil in third soil characteristic group
Earth index: pH value (C12), Soil total nitrogen (C15), available nitrogen (C18)；
It includes following vegetation index in the third vegetation characteristics group in area that extreme over-grazing, which utilizes: group's underground biomass (C2),
Cover degree of communities (C4), Shannon-Wiener diversity indices (C7)；It include following soil root system in third soil characteristic group: soil
Earth water content (C10), pH value (C12), rapid available phosphorus (C19).
In the above-mentioned methods, in step S5, it is preferable that the content of the definition is as follows:
As 1 >=di> 0.9922 judges the degree of degeneration of Meadow to be assessed for extremely stable grade；
As 0.9922 >=di> 0.8025 judges the degree of degeneration of Meadow to be assessed to stablize grade；
As 0.8025 >=di> 0.7914 judges the degree of degeneration of Meadow to be assessed for unstable grade；
As 0.7914 >=di>=0, judge the degree of degeneration of Meadow to be assessed for extremely unstable grade.
In the above-mentioned methods, the Meadow type of plant communities is sheep's hay+miscellany grass group, Stipa baicalensis+sheep
Careless group, Filifolium sibiricum psilium, preferably sheep's hay+miscellany grass group.
The present invention also provides a kind of models for assessing Meadow difference degree of degeneration, are built by any of the above-described the method
It is vertical to obtain.
The present invention protects application of the model in assessment Meadow difference degree of degeneration.
The present invention has the beneficial effect that:
The application is directed to the natural meadow of difference degree of degeneration caused by Meadow difference producing level, is controlled by field
System experiment studies the Response Mechanism that Meadow is degenerated to mankind's activity (control is herded) using field Ecological Investigation method,
Matrix is established using principal component analysis, canonical correlation and fuzzy mathematics, establishes different producing level degradation assessment system instruction degree
Diagnostic method screens different application practice difference degree of degeneration grassland vegetation and soil root system system, carries out grassland degeneration index
The otherness of System For Screening and degree of degeneration, the analysis of uncertainty and sensibility obtain the instruction degree of different degree of degenerations, build
The vertical scientific and stronger Meadow of operability degenerates and quantifies evaluation index system instruction degree diagnostic method, enriches grassland degeneration
The content of scale evaluation, the foundation for the assessment of Meadow solum pattern are provided fundamental basis, and to relevant department visitor
The degraded condition for judging Meadow is seen, especially understanding different degree of degenerations influences situation to meadow, promotes grassland agriculture
Production and environmental protection have the meaning of reality.
Detailed description of the invention
Vegetational type syndrome of the Fig. 1 between different year between Different grazing strength falls height change, wherein small letter
Mother represents the difference between same time Different grazing strength, and the correlativity formula between group height y and grazing intensity x is y
=-18.306x+20.877, coefficient R2=0.9738.
Vegetational type feature cover degree of communities variation of the Fig. 2 between different year between Different grazing strength, wherein small letter
Mother represents the difference between same time Different grazing strength, and the correlativity formula between cover degree of communities y and grazing intensity x is y
=-32.291x+73, coefficient R2=0.9551.
Fig. 3 biomass variety in vegetational type's feature locality between different year between Different grazing strength, wherein
Lowercase represents the difference between same time Different grazing strength, between group ground biomass y and grazing intensity x
Correlativity formula is y=260.34e-1.594x, coefficient R2=0.9898, P < 0.001.
Vegetational type feature litter biomass variety of the Fig. 4 between different year between Different grazing strength, it is medium and small
The mother that writes represents the difference between same time Different grazing strength, the related pass between litter biomass y and grazing intensity x
Be formula be y=-225.49x+198.13, coefficient R2=0.9672.
Fig. 5 biomass variety under the locality of vegetation COMMUNITY CHARACTERISTICS between different soil depth, wherein lowercase represents same
Difference in one soil depth between Different grazing strength.
Fig. 6 is the variation of vegetational type's feature Margalef diversity index, and wherein lowercase represents Different grazing strength
Between difference.
Fig. 7 is the variation of vegetational type's feature Shannon-wiener diversity indices, and wherein lowercase represents difference and puts
Herd the difference between intensity.
Fig. 8 be vegetational type's feature Simpson dominance index variation, wherein lowercase represent Different grazing strength it
Between difference.
Fig. 9 be vegetational type's feature Pielou evenness index variation, wherein lowercase represent Different grazing strength it
Between difference.
Soil moisture variation of the Figure 10 between different year between Different grazing strength wherein soil moisture y and is herded strong
The correlativity formula spent between x is y=23.785e0.1003x, coefficient R2=0.5475.
Soil moisture (soil moisture content) variation of the Figure 11 between different year between Different grazing strength, wherein soil contains
Correlativity formula between water y and grazing intensity x is y=-1.9209x+18.503, coefficient R2=0.8426.
Soil weight variation of the Figure 12 between different year between Different grazing strength wherein soil weight y and is herded strong
The correlativity formula spent between x is y=0.0445x+1.0062, coefficient R2=0.751.
Soil organic carbon of the Figure 13 between different year between Different grazing strength changes, wherein soil organic matter y
Correlativity formula between grazing intensity x is y=-1.8697x+36.417, coefficient R2=0.3845.
Figure 14 between different year between Different grazing strength total nitrogen content of soil variation, wherein total soil nitrogen y with put
Herding the correlativity formula between intensity x is y=-0.2762x+3.1641, coefficient R2=0.655.
Figure 15 between different year between Different grazing strength Soil total nitrogen variation, wherein total Phosphorus In Soil y with put
Herding the correlativity formula between intensity x is y=-0.0228x+0.592, coefficient R2=0.7245.
Figure 16 between different year between Different grazing strength total potassium content of soil variation, wherein the full potassium y of soil with put
Herding the correlativity formula between intensity x is y=-4.6725x2+ 4.3935x+24.421, coefficient R2=0.7331.
Soil pH of the Figure 17 between different year between Different grazing strength changes, wherein soil pH y and grazing intensity x
Between correlativity formula be y=-0.2794x+6.7608, coefficient R2=0.9506.
Figure 18 between different year between Different grazing strength Soil Available nitrogen variation, wherein Soil Available nitrogen y with put
Herding the correlativity formula between intensity x is y=-31.259x2+ 27.713x+291.36, coefficient R2=0.1513.
Figure 19 between different year between Different grazing strength soil quick-effective phosphor variation, wherein soil quick-effective phosphor y with put
Herding the correlativity formula between intensity x is y=2.3312x2- 2.2335x+5.483, coefficient R2=0.8565.
Figure 20 between different year between Different grazing strength soil available nitrogen variation, wherein soil available nitrogen y with put
Herding the correlativity formula between intensity x is y=27.812x+232.68, coefficient R2=0.5408.
Figure 21 left figure Soil Microbial Biomass Carbon between different year changes, and right figure Soil microbial nitrogen between different year becomes
Change.
Soil C/N variation of the Figure 22 between different year between Different grazing strength wherein soil C/N y and is herded strong
The correlativity formula spent between x is y=1.1179x+10.311, coefficient R2=0.7294.
In Fig. 1-22,0.00,0.23,0.34,0.46,0.69,0.92 respectively represents grazing intensity G0.00: 0.00Au/
hm2、G0.23: 0.23Au/hm2、G0.34: 0.34Au/hm2、G0.46: 0.46Au/hm2、G0.69: 0.69Au/hm2、G0.92:
0.92Au/hm2。
Specific embodiment
Embodiment 1, grazing intensity cause influence of the different degree of degenerations to Meadow
Experimental design: being sheep's hay+miscellany grass group natural grasslands as with herding sample, setting 6 water altogether using coenotype
Flat grazing intensity gradient processing, stocking rate are respectively as follows: G0.00:0.00Au/hm2、G0.23:0.23Au /hm2、G0.34:0.34Au/
hm2、G0.46:0.46Au/hm2、G0.69:0.69Au/hm2、G0.92:0.92Au/hm2。
By the Crazing test of 10 years (2009-2018), with the continuity of grazing time, Different grazing strength is to vegetation group
It falls feature and soil characteristic and gradually generates apparent variation, cause apparent slight degeneration, gently degraded and heavy-degraded series
Region.Specific data are as follows:
1, COMMUNITY CHARACTERISTICS changes after continuous control grazing intensity many years
By 10 years Crazing tests, with the continuity of grazing time, group's height, cover degree of communities, group's ground biomass
It is showed as the increase for herding gradient gradually decreases, between different pasture gradient with litter biomass (i.e. group's Litter-fall)
Otherness is significant, does not herd G0.00G is herded with slight0.23Data be significantly higher than moderate and herd G0.46, severe herd G0.69With
G0.92(Fig. 1-4).
As shown in figure 5, group's underground biomass increases with grazing intensity and soil depth and is reduced.Different grazing strength table
Layer 0-10cm underground biomass, which shows not herd and slightly herd, herds G less than moderate0.46G is herded with severe0.69；0-20cm
Underground biomass, which shows, slightly herds G0.23(slight to degenerate) is greater than moderate and herds G0.46G is herded with severe0.69；Other soil layers
It is rendered as not herding and is significantly higher than other and herds.0-60cm, which shows, does not herd G0.00G is herded with slight0.23It is put higher than moderate
Herd G0.46G is herded with severe0.92Underground biomass.
2, community diversity changes under continuous control grazing intensity
Different pasture gradient Community Species Diversity index analysis (Fig. 6) shows Margalef diversity index by 10
Year herds, and shows and slightly herds G0.34It is significantly higher than moderate and herds G0.46G is herded with severe0.92(P<0.05).Explanation is herded
The competitiveness for inhibiting sociales may cause the invasion and colonization of weak tendency species, and the diversity of species occurs one in group
Determine the increase of degree.But if overgrazed, the edibility herbage in group is made to gnaw excessively and lose power of regeneration, by
Gradually disappear in group, so that community diversity declines, so conservative grazing is able to maintain group's Margalef diversity index,
This shows that grazing depends on herding frequency, intensity and the type of livestock to the interference on grassland, and suitable grazing intensity can promote
Into the development on grassland, so that its bio-diversity is increased and match.With the continuity of grazing time, presentation is slightly herded gradually
Increase trend, and it is greater than other grazing treatments.
As shown in Fig. 7-9, slightly herds and be conducive to improve Community Species Diversity；It does not herd and herds significant drop with severe
Low Shannon-wiener diversity indices and Simpson dominance index；Moderate, which is herded, keeps higher species Pielou
Evenness index, entire research disclose grassland vegetation to the response mechanism for herding interference, verify intermediate disturbance hypothesis.
3, soil physical property changes after continuous control grazing intensity many years
As shown in Figure 10, grassland soil temperature increases with grazing intensity and is increased.As shown in figure 11, it in grazing season, puts
The moisture storage for reducing soil is herded, soil moisture content is reduced.Different grazing strength 0-10cm soil moisture content shows
It does not herd and slightly herds and herd G greater than moderate0.46G is herded with severe0.69, G0.92.As shown in figure 12, the soil weight is with herding
Intensity linearly increases, and Different grazing strength 0-10cm soil layer shows severe and herds G0.69、G0.92It does not herd noticeably greater than and gently
Degree, which is herded, herds G greater than moderate0.46。
4, soil chemistry characteristic changes of contents after continuous control grazing intensity many years
With the continuity of grazing time, full grazing intensity has apparent influence to upper soll layer organic carbon content and nitrogen.Slightly
Herd G0.34G is herded with moderate0.46Total soil nitrogen and organic carbon are higher, and severe herds G0.69Lower (the Figure 13-of SOIL CARBON AND NITROGEN content
14).As shown in Figure 15-16, total Phosphorus In Soil, full potassium first increases reduces afterwards, herds and lags behind plant to Influence To Soil, is not in
Reveal fixed apparent changing rule.As shown in figure 17, severe, which is herded, significantly reduces soil pH value；As shown in Figure 18-20,
With the increase of grazing intensity, herds and increase the labile organic nitrogen of soil, to increase Soil available nitrogen content.It is quick-acting
Phosphorus is not herded or not to be higher than with severe and slightly be herded, and does not herd the validation that sharp phosphorus is herded with severe.As the extreme on meadow is moved back
Change, the regeneration of herbage receives great inhibition, and the demand to potassium significantly reduces, and causes nutrients accumulation, simultaneously as herding house
It raises and contains a large amount of potassiums in the excrement of excretion, high intensity, which is herded, increases unit area domestic animal head number, is drained daily by excrement
Potassium amount to meadow is also increase accordingly, and therefore, with the increase of grazing intensity, upper soll layer quick-acting potassium content is dramatically increased.Such as
Shown in Figure 22, with the increase of grazing intensity, herds and increase soil C/N content.
5, Biological character of soil changes after continuous control grazing intensity many years
As shown in figure 21, with the continuity of grazing time, 0-10cm soil layer is rendered as not herding, slightly herds and put with moderate
The increase of edaphon carbon content is herded, severe herds microbial biomass C content and increase is also presented, and increases large percentage；Severe
Herding reduces edaphon nitrogen content.
Embodiment 2, the model for establishing assessment Meadow difference degree of degeneration
In order to study Meadow influencing each other between phytobiocoenose and edaphic factor under different pasture degree of degeneration
Meadow degree of degeneration index system is diagnosed with selection, (G0 is not put for grazing district to sheep's hay in embodiment 1+miscellany grass group
Herding intensity is G0.00), (G1, i.e. grazing intensity are G in slight grazing utilization area0.23), (G2 is herded strong in moderate grazing utilization area
Degree is G0.46), extreme over-grazing using area (G3, i.e. grazing intensity be G0.69) apply " canonical correlation analysis (CCA) " method, analysis
Correlation between phytobiocoenose and edaphic factor.
In each degree of degeneration, phytobiocoenose set of variables (i.e. the first vegetation characteristics group) is by variable or vegetation index: group
Fall ground biomass (C1), group's underground biomass (0~60cm) (C2), group's Litter-fall (C3), cover degree of communities (C4), group
Drop height degree (C5), Margelef diversity index (C6), Shannon-Wiener diversity indices (C7), Simpson dominance
Index (C8) and Pielou evenness index (C9) are constituted；
Edaphic factor set of variables (i.e. the first soil characteristic group) is by variable or soil root system: soil moisture content (C10), soil
Earth bulk density (C11), soil pH (C12), soil organic matter (C13), total nitrogen content of soil (C14), Soil total nitrogen (C15),
Total potassium content of soil (C16), C/N (C17), available nitrogen (C18), rapid available phosphorus (C19), available potassium (C20), Soil Microbial Biomass Carbon
(C21) it is constituted with Soil microbial nitrogen (C22)；
The index of all (0-10cm) soil layers of soil root system.
One, principal component analysis
Firstly, carrying out principal component analysis respectively to the first vegetation characteristics group under different producing levels and the first soil characteristic group
(PCA).Value according to characteristic root is greater than 1 and chooses principal component for standard:
The first vegetation characteristics group PCA analyzes result (as shown in table 1) and shows except in degeneration G2 rank under different degree of degenerations
Contribution rate is accumulated in Duan Qian four-dimension principal component and respectively reaches 83.306%, other degradation levels add up in preceding three-dimensional principal component
Contribution rate respectively reaches 78.148% (G0), 78.091% (G1), 75.011% (G2), 89.116% (G3).
Each variable first three/four-dimensional principal component on factor load the maximum (the second vegetation characteristics group) be respectively:
Not grazing district (G0): group's underground biomass (C2), group's height (C5), Shannon-Wiener diversity refer to
Number (C7), Simpson dominance index (C8), Pielou evenness index (C9)；
Slight grazing utilization area (G1): group's underground biomass (C2), Margelef diversity index (C6),
Shannon-Wiener diversity indices (C7), Simpson dominance index (C8), Pielou evenness index (C9)；
Moderate grazing utilization area (G2): group's Litter-fall (C3), cover degree of communities (C4), Shannon-Wiener diversity
Index (C7), Simpson dominance index (C8), Pielou evenness index (C9)；
Extreme over-grazing utilize area (G3): group's underground biomass (C2), cover degree of communities (C4), group's height (C5),
Shannon-Wiener diversity indices (C7), Simpson dominance index (C8)；
1. vegetation index PCA of table analyzes result
Show preceding four-dimensional master using the first soil characteristic group PCA analysis result (as shown in table 2) under degree of degeneration different
Contribution rate is accumulated on ingredient respectively reaches 76.863% (G0), 74.802% (G1), 74.483% (G2), 72.179% (G3).
The factor load the maximum (the second soil characteristic group) of each variable in preceding four-dimensional principal component is respectively:
Not grazing district (G0): pH value (C12), soil organic matter (C13), total nitrogen content of soil (C14), Soil total nitrogen
(C15), C/N (C17), Soil Microbial Biomass Carbon (C21)；
Slight grazing utilization area (G1): the soil weight (C11), pH value (C12), Soil total nitrogen (C15), C/N
(C17), available potassium (C20), Soil microbial nitrogen (C22)；
Moderate grazing utilization area (G2): the soil weight (C11), pH value (C12), Soil total nitrogen (C15), C/N
(C17), available nitrogen (C18), Soil microbial nitrogen (C22)；
Extreme over-grazing utilizes area (G3): soil moisture content (C10), pH value (C12), total nitrogen content of soil (C14), soil
Content of tatal phosphorus (C15), rapid available phosphorus (C19), Soil Microbial Biomass Carbon (C21).
2. soil root system PCA of table analyzes result
Two, canonical correlation analysis
The basic thought of canonical correlation is the correlation between multiple variables and multiple variables can be converted to two changes
Correlation between amount reflects the overall relevancy between two groups of indexs using the correlativity between the two generalized variables.
Specific step is as follows:
Firstly, examining two groups of variables (the second vegetation characteristics group to the second soil characteristic group) whether related, in relevant feelings
Canonical correlation analysis is carried out under condition,
1) it finds out first pair of linear combination in every group of variable respectively first, makes its u1And v1With maximum correlation:
2) then second pair of linear combination is found out in every group of variable again, make its respectively with the first linear combination in this group
It is uncorrelated, and make its u2And v2With secondary big correlation:
In formula (1) and (2), u1、u2For vegetation Index System Model, v1、v2For soil root system system model； u2And u1Phase
It is mutually independent, v1With v2Independently of each other；But u1And v1Correlation, u2And v2It is related；x1,x2... indicate vegetation index, y1,y2... it indicates
Soil root system, a11,a21..., ap1,a12,a22..., ap2And b11,b21..., bp1,b12,b22,…,bp2Indicate canonical correlation system
Number is done in the relevant situation of the canonical correlation level of signifiance obtained by matrix, and the absolute value of canonical correlation coefficient is bigger, illustrates typical case
Variable reflects that extent of information is bigger；
Repeat step 1) and 2), the canonical variable v of acquisitionqAnd uqBe according to their related coefficient it is descending by
To extraction, until the correlation proceeded between q two groups of variables of step, which is extracted, to be finished, available q is to variable, i.e., q pairs
Canonical correlation variable.
Finally, by examining the conspicuousness of each pair of canonical correlation variant correlation coefficient to determine to retain several pairs of canonical variables, such as
The degree of correlation of fruit a pair is not significant, then this does not just have representativeness to variable, so that it may ignore.
This research has chosen first pair of canonical correlation variable, and under the level of α < 0.05, there are conspicuousness correlativities (to be
Number, which is positive, to be indicated to be positively correlated, and coefficient, which is negative, indicates negatively correlated).Phase under different degree of degenerations between vegetation index and soil root system
Mutual relation expression formula is as follows:
Not grazing district (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 slightly herds benefit
With 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, moderate herd benefit
With area (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 over-grazing benefit
With 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.
By above-mentioned expression formula it can be concluded that (the i.e. relatively large vegetation index of extraction canonical correlation coefficient absolute value and soil
Index is as the third vegetation characteristics group and third soil characteristic group for playing main decisive action):
Grazing district (G0) plant variable is not mainly by group's underground biomass (C2), group's height (C5), Shannon-
Wiener diversity indices (C7) determines；Soil variable is mainly by soil organic matter (C13), total nitrogen content of soil (C14), C/N
(C17) it determines.
Slight grazing degradated area (G1) plant variable is mainly by Margelef diversity index (C6), Simpson dominance
Index (C8), Pielou evenness index (C9) determine；Soil variable is mainly by C/N (C17), available potassium (C20), the micro- life of soil
Object nitrogen (C22) determines.
Moderate grazing degradated area (G2) plant variable is mainly by group's Litter-fall (C3), Shannon-Wiener diversity
Index (C7), Simpson dominance index (C8) determine；Soil variable mainly by pH value (C12), Soil total nitrogen (C15),
Available nitrogen (C18) determines.
Extreme over-grazing degenerate region (G3) plant variable mainly by group's underground biomass (C2), cover degree of communities (C4),
Shannon-Wiener diversity indices (C7) determines；Soil variable is mainly by soil moisture content (C10), pH value (C12), speed
Phosphorus (C19) is imitated to determine.
Three, fuzzy overall evaluation
Using the data of third vegetation characteristics group and third soil characteristic group as set of factors, different degree of degenerations are processing collection,
Fuzzy overall evaluation is carried out to the Meadow of each different degree of degenerations, each different degree of degeneration Meadows are calculated
Simultaneously fuzzy overall evaluation system is calculated in difference property coefficient between third vegetation characteristics group and the data of third soil characteristic group
Number di, wherein i takes any number of 1-n of natural number, represents different degree of degeneration Meadows；
Firstly, setting different degree of degeneration Meadows are as follows: X=X1,X2,…,Xi,…,Xn；
If items influence the set of factors on meadow are as follows: U=U1,U2,…,Uj,…,Um；
Eigenmatrix is Un×m=(Uij)n×m(3)；
r∈〔0,1〕；
Evaluations matrix R=(rij)n×m(5)；
Secondly, each different degree of degeneration Meadow third vegetation characteristics groups and third is calculated according to evaluations matrix
Difference property coefficient between the data of soil characteristic group, calculating process are as follows:
Evaluation function is taken to be respectively as follows:
D1=1/m × (ri1+ri2+…+rim) (6)；
D2=Max (ri1,ri2,…,rim) (7)；
D3=Min (ri1,ri2,…,rim) (8)；
Calculate separately to obtain otherness coefficient di1,di2,di3；
Enable U1=(D1, D2, D3), R1=F (X × U1) i.e.:
Finally, enabling d againi=1/3 × (di1+di2+di3) (10)；
Fuzzy overall evaluation coefficient d is calculatedi, wherein di∈ (0,1), wherein grazing district is not extremely stable meadow, di
=1；The coefficient of each difference assessment of degradation degree is between 0~1, diCloser to 1, meadow is closer to extremely stable state.
1, using vegetation index as the fuzzy overall evaluation of system
Using vegetation field investigation data as set of factors, different degree of degenerations are processing collection, to sheep's hay+miscellany grass Community Degradation
It is as follows that degree carries out fuzzy overall evaluation:
Slight grazing degradated area (G1)
Fuzzy overall evaluation matrix is U2×3=(Uij)2×3
Obtain evaluations matrix R=(rij)2×3
The Margelef richness in our available sheep's hays+miscellany grass group is slight grazing degradated area from evaluations matrix R
Index (C6), Simpson dominance index (C8), Pielou evenness index (C9) difference property coefficient.
R1=F (X × U1) i.e.
Fuzzy overall evaluation coefficient (instruction degree)
d11.000 not grazing district
d21.0557 slight grazing degradated area
From Margelef diversity index (C6), Simpson dominance index (C8), Pielou evenness index (C9) 3
A vegetation index is that the result of the fuzzy overall evaluation of system is seen, sheep's hay+miscellany grass group is slight, and instruction degree in grazing degradated area is
1.0557, i.e., slight grazing degradated area vegetation degeneration degree is the 105.57% of current not grazing district degree.
Moderate grazing degradated area (G2)
Fuzzy overall evaluation matrix is U2×3=(Uij)2×3
Obtain evaluations matrix R=(rij)2×3
From evaluations matrix R our available sheep's hay+moderate grazing degradated area, miscellany grass group group's Litter-falls (C3),
The difference property coefficient of Shannon-Wiener diversity indices (C7), Simpson dominance index (C8).
R1=F (X × U1) i.e.
Fuzzy overall evaluation coefficient (instruction degree)
d11.000 not grazing district
d20.7748 moderate grazing degradated area
From group's Litter-fall (C3), Shannon-Wiener diversity indices (C7), Simpson dominance index (C8) 3
A vegetation index is that the result of the fuzzy overall evaluation of system sees that sheep's hay+miscellany grass group moderate grazing degradated area's instruction degree is
0.7748, i.e. moderate grazing degradated area vegetation degeneration degree is the 77.48% of current not grazing district degree.
Extreme over-grazing degenerate region (G3)
Fuzzy overall evaluation matrix is U2×3=(Uij)2×3
Obtain evaluations matrix R=(rij)2×3
Our available sheep's hay+miscellany grass group's extreme over-grazing degenerate region group's underground biomasses from evaluations matrix R
(C2), the difference property coefficient of cover degree of communities (C4), Shannon-Wiener diversity indices (C7).
R1=F (X × U1) i.e.
Fuzzy overall evaluation coefficient (instruction degree)
d11.000 not grazing district
d20.7535 extreme over-grazing degenerate region
From group's underground biomass (C2), cover degree of communities (C4), Shannon-Wiener diversity indices (C7) 3 plants
See that sheep's hay+miscellany grass group's extreme over-grazing degenerate region instruction degree is by the result for the fuzzy overall evaluation that index is system
0.7535, i.e. extreme over-grazing degenerate region vegetation degeneration degree is the 75.35% of current not grazing district degree.
2, using soil root system as the fuzzy overall evaluation of system
Using soil field investigation data as set of factors, different degree of degenerations are processing collection, to sheep's hay+miscellany grass Community Degradation
It is as follows that degree carries out fuzzy overall evaluation:
Slight grazing degradated area (G1)
Fuzzy overall evaluation matrix is U2×3=(Uij)2×3
Obtain evaluations matrix R=(rij)2×3
The C/N (C17) in our available sheep's hays+miscellany grass group is slight grazing degradated area, quick-acting from evaluations matrix R
The difference property coefficient of potassium (C20), Soil microbial nitrogen (C22).
R1=F (X × U1) i.e.
Fuzzy overall evaluation coefficient (instruction degree)
d11.000 not grazing district
d20.9814 slight grazing degradated area
It is commented from C/N (C17), available potassium (C20), the fuzzy synthesis that 3 soil root systems of Soil microbial nitrogen (C22) are system
The result of valence sees, sheep's hay+miscellany grass group is slight, and instruction degree in grazing degradated area is 0.9814, i.e., slight grazing degradated area vegetation
Degree of degeneration is the 98.14% of current not grazing district degree.
Moderate grazing degradated area (G2)
Fuzzy overall evaluation matrix is U2×3=(Uij)2×3
Obtain evaluations matrix R=(rij)2×3
From evaluations matrix R our available sheep's hay+moderate grazing degradated area, miscellany grass group soil pH value (C12),
The difference property coefficient of Soil total nitrogen (C15), available nitrogen (C18).
R1=F (X × U1) i.e.
Fuzzy overall evaluation coefficient (instruction degree)
d11.000 not grazing district
d21.0035 moderate grazing degradated area
From pH value (C12), Soil total nitrogen (C15), the fuzzy synthesis that 3 soil root systems of available nitrogen (C18) are system
The result of evaluation sees that sheep's hay+miscellany grass group moderate grazing degradated area's instruction degree is
1.0035, i.e. moderate grazing degradated area vegetation degeneration degree is the 100.35% of current not grazing district degree.
Extreme over-grazing degenerate region (G3)
Fuzzy overall evaluation matrix is U2×3=(Uij)2×3
Obtain evaluations matrix R=(rij)2×3
Our available sheep's hay+miscellany grass group's extreme over-grazing degenerate region soil moisture contents from evaluations matrix R
(C10), pH value (C12), rapid available phosphorus (C19) difference property coefficient.
R1=F (X × U1) i.e.
Fuzzy overall evaluation coefficient (instruction degree)
d11.000 not grazing district
d20.9376 extreme over-grazing degenerate region
It is commented from soil moisture content (C10), pH value (C12), the fuzzy synthesis that 3 soil root systems of rapid available phosphorus (C19) are system
The result of valence sees that sheep's hay+miscellany grass group's extreme over-grazing degenerate region instruction degree is 0.9376, i.e. extreme over-grazing degenerate region vegetation moves back
Change degree is the 93.76% of current not grazing district degree.
3, using vegetation and soil root system as the fuzzy overall evaluation of system
The vegetation index and soil root system obtained by above-mentioned analysis is set of factors, and different degree of degenerations are processing collection, right
It is as follows that sheep's hay+miscellany grass group degree of degeneration carries out fuzzy overall evaluation:
Slight grazing degradated area (G1)
Fuzzy overall evaluation matrix is U2×6=(Uij)2×6
Obtain evaluations matrix R=(rij)2×6
The Margelef richness in our available sheep's hays+miscellany grass group is slight grazing degradated area from evaluations matrix R
Index (C6), Simpson dominance index (C8), Pielou evenness index (C9), C/N (C17), available potassium (C20), soil
The difference property coefficient of earth microorganism nitrogen (C22).
R1=F (X × U1) i.e.
Fuzzy overall evaluation coefficient (instruction degree)
d11.000 not grazing district
d20.9922 slight grazing degradated area
From Margelef diversity index (C6), Simpson dominance index (C8), Pielou evenness index (C9),
C/N (C17), available potassium (C20), the result of fuzzy overall evaluation that 6 indexs of Soil microbial nitrogen (C22) are system see, sheep
Grass+miscellany grass group is slight, and instruction degree in grazing degradated area is 0.9922, i.e., slight grazing degradated area vegetation and soil root system are degenerated
Degree is the 99.22% of current not grazing district degree.
Moderate grazing degradated area (G2)
Fuzzy overall evaluation matrix is U2×6=(Uij)2×6
Obtain evaluations matrix R=(rij)2×6
From evaluations matrix R our available sheep's hay+miscellany grass group moderate grazing degradated area group's Litter-falls (C3),
Shannon-Wiener diversity indices (C7), Simpson dominance index (C8), pH value (C12), Soil total nitrogen
(C15), the difference property coefficient of available nitrogen (C18).
R1=F (X × U1) i.e.
Fuzzy overall evaluation coefficient (instruction degree)
d11.000 not grazing district
d20.8025 moderate grazing degradated area
From group's Litter-fall (C3), Shannon-Wiener diversity indices (C7), Simpson dominance index (C8),
PH value (C12), Soil total nitrogen (C15), the result of fuzzy overall evaluation that 6 indexs of available nitrogen (C18) are system see, sheep
Grass+miscellany grass group moderate grazing degradated area's instruction degree is 0.8025, i.e. moderate grazing degradated area vegetation and soil root system degenerates
Degree is the 80.25% of current not grazing district degree.
Extreme over-grazing degenerate region (G3)
Fuzzy overall evaluation matrix is U2×6=(Uij)2×6
Obtain evaluations matrix R=(rij)2×6
Our available sheep's hay+miscellany grass group's extreme over-grazing degenerate region group underground biomasses from evaluations matrix R
(C2), cover degree of communities (C4), Shannon-Wiener diversity indices (C7), soil moisture content (C10), pH value (C12), quick-acting
The difference property coefficient of phosphorus (C19).
R1=F (X × U1) i.e.
Fuzzy overall evaluation coefficient (instruction degree)
d11.000 not grazing district
d20.7914 extreme over-grazing degenerate region
Contain from group's underground biomass (C2), cover degree of communities (C4), Shannon-Wiener diversity indices (C7), soil
Water (C10), pH value (C12), the result of fuzzy overall evaluation that 6 indexs of rapid available phosphorus (C19) are system see, sheep's hay+miscellany
Careless group's extreme over-grazing degenerate region instruction degree is 0.7914, i.e. extreme over-grazing degenerate region vegetation and soil root system degree of degeneration is to work as
The 79.14% of preceding not grazing district degree.
Conclusion:
It is seen by the fuzzy overall evaluation result of system of vegetation index, sheep's hay+miscellany grass group is slight, and grazing degradated area refers to
Indication is 1.0557, i.e., slight grazing degradated area vegetation degeneration degree is the 105.57% of current not grazing district degree.Moderate is put
Herding degenerate region instruction degree is 0.7748, i.e., moderate grazing degradated area vegetation degeneration degree is current not grazing district degree
77.48%.Instruction degree in extreme over-grazing degenerate region is 0.7535, i.e. extreme over-grazing degenerate region vegetation degeneration degree is not herd currently
The 75.35% of area's degree.
It is seen by the fuzzy overall evaluation result of system of soil root system, sheep's hay+miscellany grass group is slight, and grazing degradated area refers to
Indication is 0.9814, i.e., slight grazing degradated area vegetation degeneration degree is the 98.14% of current not grazing district degree.Moderate is put
Herding degenerate region instruction degree is 1.0035, i.e., moderate grazing degradated area vegetation degeneration degree is current not grazing district degree
100.35%.Instruction degree in extreme over-grazing degenerate region is 0.9376, i.e. extreme over-grazing degenerate region vegetation degeneration degree is not put currently
The 93.76% of pastoral area degree.
The fuzzy overall evaluation of the index system combined with soil root system with vegetation index the result shows that, sheep's hay+miscellaneous
Lei Cao group is slight, and instruction degree in grazing degradated area is 0.9922, i.e., slight grazing degradated area vegetation and soil root system degree of degeneration are
The 99.22% of current not grazing district degree.Moderate grazing degradated area's instruction degree is 0.8025, i.e. moderate grazing degradated area vegetation
With soil root system degree of degeneration is current not grazing district degree 80.25%.Instruction degree in extreme over-grazing degenerate region is 0.7914,
I.e. extreme over-grazing degenerate region vegetation and soil root system degree of degeneration are the 79.14% of current not grazing district degree.
It is integrated as the instruction degree that index system fuzzy overall evaluation is obtained according to phytobiocoenose and soil, will not be herded
It is utilized as extremely stable grade, slight grazing utilization area is to stablize grade, and moderate grazing utilization degenerate region is as unstable grade, pole
Spend the instruction degree that grazing utilization degenerate region is degenerated as extremely unstable grade, sheep's hay+miscellany grass group different pasture producing level
Threshold value is followed successively by 1-0.9922,0.9922-0.8025,0.8025-0.7914, and 0,7914-0, it may be assumed that if Meadow to be assessed
Instruction degree be di,
As 1 >=di> 0.9922 judges the degree of degeneration of Meadow to be assessed for extremely stable grade；
As 0.9922 >=di> 0.8025 judges the degree of degeneration of Meadow to be assessed to stablize grade；
As 0.8025 >=di> 0.7914 judges the degree of degeneration of Meadow to be assessed for unstable grade；
As 0.7914 >=di>=0, judge the degree of degeneration of Meadow to be assessed for extremely unstable grade.
Embodiment 3, method are examined
To be not used for the data that the data of 2 model foundation of embodiment are Meadow to be assessed in embodiment 1, according to implementation
The method of example 2 carries out that instruction degree d is calculatedi, according to the degradation level of the threshold decision of embodiment 2 Meadow to be assessed,
As a result it is consistent completely with practical.Prove that the application method accuracy is fine.
The content being not described in detail in this specification belongs to the prior art well known to professional and technical personnel in the field.More than
Described is only embodiments herein, is not intended to limit this application.To those skilled in the art, the application can
To there is various modifications and variations.All any modification, equivalent replacement, improvement and so within the spirit and principles of the present application,
It should be included within the scope of the claims of this application.
Claims (10)
1. a kind of method for the model for establishing assessment Meadow difference degree of degeneration, includes the following steps:
The variable data of S1, the different degree of degeneration Meadows of selection, the variable data include the first vegetation characteristics group and the
One soil characteristic group；
S2, to the first vegetation characteristics group of each different degree of degeneration Meadows and the first soil characteristic group carry out respectively it is main at
Analysis, the value according to characteristic root are greater than 1 for standard selection principal component, obtain the factor load phase in identical dimensional principal component
The relatively large second soil characteristic group of factor load to biggish second vegetation characteristics group and in identical dimensional principal component；
S3, canonical correlation is carried out to the second vegetation characteristics group of each different degree of degeneration Meadows and the second soil characteristic group
Analysis, has extracted the third vegetation characteristics group and third soil characteristic group of main decisive action；
S4, using the data of third vegetation characteristics group and third soil characteristic group as set of factors, different degree of degenerations be processing collection, it is right
The Meadow of each difference degree of degeneration carries out fuzzy overall evaluation, and each different degree of degeneration Meadow the is calculated
Difference property coefficient between three vegetation characteristics groups and the data of third soil characteristic group, and fuzzy overall evaluation coefficient is calculated
di, wherein i takes any number in 1-n of natural number, represents different degree of degeneration Meadows；
S5, different fuzzy overall evaluation coefficient ds are definediNumberical range and its corresponding Meadow difference degree of degeneration, obtain
Assess the model of Meadow difference degree of degeneration.
2. the method as described in claim 1, it is characterised in that: canonical correlation analysis includes the following steps: in step S3
Firstly, examining two groups of variables of the second vegetation characteristics group and the second soil characteristic group of each different degree of degeneration Meadows
It is whether related, following steps are carried out in relevant situation:
S31, first pair of linear combination such as following formula is found out in the variable of the second vegetation characteristics group and the second soil characteristic group respectively
(1), make its u1And v1With maximum correlation:
S32, then to find out second pair of linear combination in the variable of the second vegetation characteristics group and the second soil characteristic group again as follows
It is uncorrelated to the first linear combination in this group respectively to make it, and makes its u for formula (2)2And v2With secondary big correlation:
In formula (1) and (2), u1、u2For vegetation Index System Model, v1、v2For soil root system system model；u2And u1Mutually solely
It is vertical, v1With v2Independently of each other；But u1And v1Correlation, u2And v2It is related；x1,x2... indicate that the vegetation in the second vegetation characteristics group refers to
Mark, y1,y2... indicate the soil root system in the second soil characteristic group, a11,a21..., ap1,a12,a22..., ap2And b11,
b21..., bp1,b12,b22,…,bp2It indicates canonical correlation coefficient, is to do matrix institute in the relevant situation of the canonical correlation level of signifiance
, the absolute value of canonical correlation coefficient is bigger, illustrates that canonical variable reflection extent of information is bigger；
S33, repeat S31 and S32, the canonical variable v of acquisitionqAnd uqIt is descending by right according to their related coefficient
It extracts, until the correlation proceeded between q two groups of variables of step, which is extracted, to be finished, available q is to canonical correlation variable；
Retain each pair of canonical correlation variant correlation coefficient and has 1-q of conspicuousness to canonical correlation variable；
From 1-q to the relatively large vegetation index of extraction canonical correlation coefficient absolute value and soil root system in canonical correlation variable
As the third vegetation characteristics group and third soil characteristic group for playing main decisive action.
3. method according to claim 1 or 2, it is characterised in that: the fuzzy overall evaluation in step S4 includes the following steps:
Firstly, setting different degree of degeneration Meadows are as follows: X=X1,X2,…,Xi,…,Xn；
If items influence the set of factors on meadow are as follows: U=U1,U2,…,Uj,…,Um；
Eigenmatrix is Un×m=(Uij)n×m(3)；
Evaluations matrix R=(rij)n×m(5)；
Secondly, each different degree of degeneration Meadow third vegetation characteristics groups and third soil is calculated according to evaluations matrix
Difference property coefficient between the data of feature group, calculating process are as follows:
Evaluation function is taken to be respectively as follows:
D1=1/m × (ri1+ri2+…+rim) (6)；
D2=Max (ri1,ri2,…,rim) (7)；
D3=Min (ri1,ri2,…,rim) (8)；
Calculate separately to obtain otherness coefficient di1,di2,di3；
Enable U1=(D1, D2, D3), R1=F (X × U1) i.e.:
Finally, enabling d againi=1/3 × (di1+di2+di3) (10)；
Fuzzy overall evaluation coefficient d is calculatedi, wherein di∈〔0,1〕。
4. the method as described in any in claim 1-3, it is characterised in that: in step S1, include in the first vegetation characteristics group
Following vegetation index: group's ground biomass (C1), group's underground biomass (0~60cm) (C2), group's Litter-fall (C3), group
Fall cover degree (C4), group's height (C5), Margelef diversity index (C6), Shannon-Wiener diversity indices (C7),
Simpson dominance index (C8) and Pielou evenness index (C9)；
And/or in the first soil characteristic group include following soil root system: soil moisture content (C10), the soil weight (C11),
Soil pH (C12), soil organic matter (C13), total nitrogen content of soil (C14), Soil total nitrogen (C15), total potassium content of soil
(C16), C/N (C17), available nitrogen (C18), rapid available phosphorus (C19), available potassium (C20), Soil Microbial Biomass Carbon (C21) and soil are micro-
Biological nitrogen (C22)；, the index in the first soil characteristic group is the index of 0-10cm soil layer.
5. the method as described in any in claim 1-4, it is characterised in that: it is described difference degree of degeneration Meadows include
Not grazing district, slight grazing degradated area, moderate grazing degradated area and extreme over-grazing degenerate region；
Wherein, grazing district is not extremely stable meadow, di=1；The coefficient of each difference assessment of degradation degree is between 0~1, diMore
Close to 1, meadow is closer to extremely stable state.
6. method as claimed in claim 5, it is characterised in that: in step S2, do not wrapped in the second vegetation characteristics group of grazing district
Include following vegetation index: group's underground biomass (C2), group's height (C5), Shannon-Wiener diversity indices (C7),
Simpson dominance index (C8), Pielou evenness index (C9)；Include following soil root system in second soil characteristic group:
PH value (C12), soil organic matter (C13), total nitrogen content of soil (C14), Soil total nitrogen (C15), C/N (C17), soil are micro-
Biological carbon (C21)；
Include following vegetation index in the second vegetation characteristics group in slight grazing utilization area: group's underground biomass (C2),
Margelef diversity index (C6), Shannon-Wiener diversity indices (C7), Simpson dominance index (C8),
Pielou evenness index (C9)；It include following soil root system: the soil weight (C11), pH value in second soil characteristic group
(C12), Soil total nitrogen (C15), C/N (C17), available potassium (C20), Soil microbial nitrogen (C22)；
It include following vegetation index: group's Litter-fall (C3), cover degree of communities in the second vegetation characteristics group in moderate grazing utilization area
(C4), Shannon-Wiener diversity indices (C7), Simpson dominance index (C8), Pielou evenness index (C9)；
It include following soil root system: the soil weight (C11), pH value (C12), Soil total nitrogen (C15), C/ in second soil characteristic group
N (C17), available nitrogen (C18), Soil microbial nitrogen (C22)；
It includes following vegetation index: group's underground biomass (C2), group in the second vegetation characteristics group in area that extreme over-grazing, which utilizes,
Cover degree (C4), group's height (C5), Shannon-Wiener diversity indices (C7), Simpson dominance index (C8)；Second
It include following soil root system: soil moisture content (C10), pH value (C12), total nitrogen content of soil (C14), soil in soil characteristic group
Content of tatal phosphorus (C15), rapid available phosphorus (C19), Soil Microbial Biomass Carbon (C21).
7. such as method described in claim 5 or 6, it is characterised in that: in step S3,
It does not include following vegetation index: group's underground biomass (C2), group's height in the third vegetation characteristics group of grazing district
(C5), Shannon-Wiener diversity indices (C7)；It include following soil root system: soil organic matter in third soil characteristic group
(C13), total nitrogen content of soil (C14), C/N (C17)；
Include following vegetation index in the third vegetation characteristics group in slight grazing degradated area: Margelef diversity index (C6),
Simpson dominance index (C8), Pielou evenness index (C9)；Include following soil root system in third soil characteristic group:
C/N (C17), available potassium (C20), Soil microbial nitrogen (C22)；
It include following vegetation index: group's Litter-fall (C3), Shannon- in the third vegetation characteristics group in moderate grazing degradated area
Wiener diversity indices (C7), Simpson dominance index (C8)；Include following soil root system in third soil characteristic group:
PH value (C12), Soil total nitrogen (C15), available nitrogen (C18)；
It includes following vegetation index: group's underground biomass (C2), group in the third vegetation characteristics group in area that extreme over-grazing, which utilizes,
Cover degree (C4), Shannon-Wiener diversity indices (C7)；Include following soil root system in third soil characteristic group: soil contains
Water (C10), pH value (C12), rapid available phosphorus (C19).
8. the method as described in any in claim 1-7, it is characterised in that: in step S5, be defined as follows:
As 1 >=di> 0.9922 judges the degree of degeneration of Meadow to be assessed for extremely stable grade；
As 0.9922 >=di> 0.8025 judges the degree of degeneration of Meadow to be assessed to stablize grade；
As 0.8025 >=di> 0.7914 judges the degree of degeneration of Meadow to be assessed for unstable grade；
As 0.7914 >=di>=0, judge the degree of degeneration of Meadow to be assessed for extremely unstable grade.
9. a kind of model for assessing Meadow difference degree of degeneration, is established by the method any in claim 1-8
It arrives.
10. application of the model described in claim 9 in assessment Meadow difference degree of degeneration.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910101139.8A CN109829650B (en) | 2019-01-31 | 2019-01-31 | Model for evaluating different degradation degrees of meadow grassland and establishing method and application thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910101139.8A CN109829650B (en) | 2019-01-31 | 2019-01-31 | Model for evaluating different degradation degrees of meadow grassland and establishing method and application thereof |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109829650A true CN109829650A (en) | 2019-05-31 |
| CN109829650B CN109829650B (en) | 2021-10-08 |
Family
ID=66863235
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910101139.8A Active CN109829650B (en) | 2019-01-31 | 2019-01-31 | Model for evaluating different degradation degrees of meadow grassland and establishing method and application thereof |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109829650B (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110245867A (en) * | 2019-06-18 | 2019-09-17 | 青海大学 | A kind of grassland degeneration stage division based on bp neural network |
| CN111626580A (en) * | 2020-05-20 | 2020-09-04 | 兰州大学 | Method for evaluating accumulative influence of highways on grassland ecological sensitive areas |
| CN112016837A (en) * | 2020-08-31 | 2020-12-01 | 中国气象科学研究院 | Weathered zone grassland growing season terminal withered and fallen object quantification method based on meteorological data |
| CN112051363A (en) * | 2020-09-02 | 2020-12-08 | 西南民族大学 | Method for judging degradation degree of alpine meadow based on root-soil ratio |
| CN112213468A (en) * | 2020-10-27 | 2021-01-12 | 浙江省农业科学院 | Method for evaluating safety utilization technical effect of contaminated soil |
| CN112561398A (en) * | 2020-12-28 | 2021-03-26 | 河北地质大学 | Method and system for quantifying degradation degree of ecological geological environment of transition zone of agriculture, animal husbandry and forestry |
| CN115294460A (en) * | 2022-10-08 | 2022-11-04 | 杭州领见数字农业科技有限公司 | Method for determining degradation degree of phyllostachys praecox forest, medium and electronic device |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103065043A (en) * | 2012-12-19 | 2013-04-24 | 北京师范大学 | Alpine grassland soil health evaluation method based on physical, biological and chemical composite indicators |
| US20180059286A1 (en) * | 2014-09-13 | 2018-03-01 | International Business Machines Corporation | Aggregation and Analytics for Application-Specific Optimization Based on Multiple Data Sources |
| CN108170926A (en) * | 2017-12-12 | 2018-06-15 | 伊犁师范学院 | A kind of information data acquisition of river valley grassland degeneration situation and analysis method |
-
2019
- 2019-01-31 CN CN201910101139.8A patent/CN109829650B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103065043A (en) * | 2012-12-19 | 2013-04-24 | 北京师范大学 | Alpine grassland soil health evaluation method based on physical, biological and chemical composite indicators |
| US20180059286A1 (en) * | 2014-09-13 | 2018-03-01 | International Business Machines Corporation | Aggregation and Analytics for Application-Specific Optimization Based on Multiple Data Sources |
| CN108170926A (en) * | 2017-12-12 | 2018-06-15 | 伊犁师范学院 | A kind of information data acquisition of river valley grassland degeneration situation and analysis method |
Non-Patent Citations (1)
| Title |
|---|
| 闫瑞瑞等: ""不同放牧梯度下草甸草原土壤微生物和酶活性研究"", 《生态环境学报》 * |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110245867A (en) * | 2019-06-18 | 2019-09-17 | 青海大学 | A kind of grassland degeneration stage division based on bp neural network |
| CN111626580A (en) * | 2020-05-20 | 2020-09-04 | 兰州大学 | Method for evaluating accumulative influence of highways on grassland ecological sensitive areas |
| CN112016837A (en) * | 2020-08-31 | 2020-12-01 | 中国气象科学研究院 | Weathered zone grassland growing season terminal withered and fallen object quantification method based on meteorological data |
| CN112051363A (en) * | 2020-09-02 | 2020-12-08 | 西南民族大学 | Method for judging degradation degree of alpine meadow based on root-soil ratio |
| CN112213468A (en) * | 2020-10-27 | 2021-01-12 | 浙江省农业科学院 | Method for evaluating safety utilization technical effect of contaminated soil |
| CN112213468B (en) * | 2020-10-27 | 2023-08-11 | 浙江省农业科学院 | Method for evaluating technical effect of safety utilization of polluted soil |
| CN112561398A (en) * | 2020-12-28 | 2021-03-26 | 河北地质大学 | Method and system for quantifying degradation degree of ecological geological environment of transition zone of agriculture, animal husbandry and forestry |
| CN115294460A (en) * | 2022-10-08 | 2022-11-04 | 杭州领见数字农业科技有限公司 | Method for determining degradation degree of phyllostachys praecox forest, medium and electronic device |
| CN115294460B (en) * | 2022-10-08 | 2023-01-17 | 杭州领见数字农业科技有限公司 | Method for determining degradation degree of phyllostachys praecox forest, medium and electronic device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109829650B (en) | 2021-10-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109829650A (en) | Assess Meadow difference degree of degeneration model and its method for building up and application | |
| Zhu et al. | The large mean body size of mammalian herbivores explains the productivity paradox during the Last Glacial Maximum | |
| Reuman et al. | Allometry of body size and abundance in 166 food webs | |
| Wu et al. | Testing biodiversity-ecosystem functioning relationship in the world’s largest grassland: overview of the IMGRE project | |
| CN102855351B (en) | Crop straw resource spatialization method based on statistical data and remotely-sensed data | |
| Van der Wal et al. | Are spring staging brent geese evicted by vegetation succession? | |
| Weisskopf et al. | The interplay of millets and rice in Neolithic central China: Integrating phytoliths into the archaeobotany of Baligang | |
| Ren et al. | Grazing effects on herbage nutritive values depend on precipitation and growing season in Inner Mongolian grassland | |
| CN110232502A (en) | Valuation Method and device based on grassland resources quality | |
| CN111598431A (en) | Watershed water ecological environment bearing capacity assessment method with function difference | |
| CN116883215A (en) | Lake wetland ecological hydrologic regulation and control method considering dynamic requirements of waiting bird habitat | |
| Qi et al. | The complex biodiversity-ecosystem function relationships for the Qinghai-Tibetan grassland community | |
| Wan et al. | Moderate grazing increases the structural complexity of soil micro-food webs by promoting root quantity and quality in a Tibetan alpine meadow | |
| Ding et al. | Identifying and optimizing ecological security patterns from the perspective of the water-energy-food nexus | |
| CN117271968A (en) | Accounting method and system for carbon sequestration amount of soil | |
| CN105184234B (en) | A kind of Wheat Straw burns the measuring method and device of pollutant discharge amount | |
| CN115439022A (en) | Risk assessment method and system for environmental pollution after manure returning to field in farm | |
| Trentacoste et al. | Systems change: Investigating climatic and environmental impacts on livestock production in lowland Italy between the Bronze Age and Late Antiquity (c. 1700 BC–AD 700) | |
| Goodall | Studying the effects of environmental factors on ecosystems | |
| Gräfnings et al. | Restored intertidal eelgrass (Z. marina) supports benthic communities taxonomically and functionally similar to natural seagrasses in the Wadden Sea | |
| CN109272184B (en) | Wetland quality judgment method | |
| Johnson et al. | Exploring the Waterenergy-food Nexus in Rwanda's Akagara Basin | |
| Flannery et al. | Analyzing patterns of growth | |
| Wathern et al. | Recent upland land use change and agricultural policy in Clwyd, North Wales | |
| Xiang et al. | Heterogeneity analysis of food supply-demand patterns and land carbon loss under different dietary structures in China |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |