CN101546421A - Provincial region comparable cultivated land quality evaluation method based on GIS - Google Patents

Abstract

A provincial and regional comparable farmland quality evaluation method based on a GIS mainly takes the GIS as a technical platform, and achieves the comparability of provincial and regional farmland quality evaluation results by adopting a mode of county-level investigation basic data, local and city-level unified modeling calculation and four-level quality inspection establishment through a provincial parameter macroscopic control and unified standard index system. The method specifically comprises the steps of dividing an index control area by adopting a GIS space superposition technology and a system clustering method, determining evaluation factors by adopting a principal component analysis method, determining weight by adopting a fuzzy hierarchy method, standardizing factor indexes by adopting a piecewise function method, calculating indexes such as the natural quality of the cultivated land, utilizing the indexes such as the indexes and the economy index by adopting a modular design, constructing a cultivated land quality factor combination analogy system, and dividing the cultivated land quality by combining a frequency method and an equidistant method. The method can provide scientific basis for the differential and accurate management of cultivated land in provincial and even national regions, and has great significance for promoting the reasonable utilization and protection of cultivated land resources and ensuring the national food safety.

Description

A kind of province-wide comparison tillage quality evaluation method based on GIS

Technical field:

The invention belongs to land valuation and management domain, a kind of specifically province-wide comparison tillage quality evaluation method based on GIS.

Technical background:

The farmland quality evaluation is under specific purposes (mainly be plantation crops), to natural, social economy's two aspect attributes of ploughing carry out comprehensively, the process of quantitative assessment and divided rank.

1. foreign study

The history in existing more than 2,000 year of external farmland quality evaluation study.In the document historical data of ancient Greek, Egypt, India, Rome ancient country, all relevant for the record of arable land grade classification.Ancient Roman's famous scholar and agronomist watt sieve proposes to carry out the classification of soil by " the value size of farmland is arranged " in its works " opinion agricultural ".Along with chemistry and geological development, the various countries scholar analyzes soil property and the deciding grade and level that grades since 18 century.And what the farmland quality appraisement system was made the earliest comprehensively and systematically discussed is the works of " expert evidence in soil, Ni Rigele state " of storehouse, Russian road in 1886 Aleksandr Chayev, and its method system is still used in the USSR (Union of Soviet Socialist Republics) various countries at present.The former Russian scholar has been carried out the applied research of view potentiality (or land quality) evaluation and evaluation aspect at the requirement of agricultural production.Storie (1933) has considered the influence of farmland qualities such as soil profile development degree, soil surface quality, the variable proterties of soil at SIR that California, USA proposes (5011 know the military ing of deX chi) index.The Germany Ministry of Finance (1934) proposes " farmland evaluation regulations ", divides farmland quality according to the influence of soil character pair output.HalCrow is divided into different grade according to the crop estimated output with Montana soil with Stucky (1949), and calculates the foundation of the average quality rank of per 16 hectares of soil as tax revenue.Federal science of research institution and industrial organization (CSIRO) that Australia is maximum, resource exploration with estimate as main one of research task, set up one from nineteen forty-six, at study in Australia land resource comprehensive survey, set up a cover classification and evaluation system, and formed one and overlap comparatively investigation, evaluation and the drafting method of standard.American scholar KlingebieA.A. (1985) is divided into eight potentiality levels according to the land consolidation degree with the soil, and each potentiality level is applicable to farming, woods, herds development in various degree.

After nineteen sixties, external farmland quality evaluation has entered visibly different developing stage, begins to be regionl development and land use planning service.Along with the application of technological means such as resource exploration and remote sensing in resource exploration widely, soil research beginning is checked by the soil and is moved towards land valuation.1976 FAO (Food and Agriculture Organization of the United Nation) (FAO) issued " land valuation outline " (Framework of landevaluation) (Beroteran, 1997), this outline is from the suitability angle in soil, be divided into guiding principle, class, subclass, unit level Four, the suitability degree in main reflection soil and the restrictive factor in soil and improvement control measures (FAO, 1976), provide fertility evaluation and soil to utilize mode, become the milestone on the Evaluation for Soil Resources history than more comprehensive land characteristics and coupling thereof.Soil conservation office of United States Department of Agriculture had proposed in 1981, and " land valuation and site evaluation " system is mainly used in the land valuation of agricultural purposes, and important farmland and agrological yield-power are evaluated.This system has also reflected economic factor except the physics and chemical characteristic considering to plough; Both consider land control measure, considered the tax revenue and rules of ploughing again, had comprehensive preferably, accuracy and legality.In addition, Canadian biological Land Commission, Dutch earth science research institute, research place of Australian land resource and Eastern Europe, South Asia, some countries of Latin America have all extensively launched the work of farmland quality evaluation study.After the sixties, especially along with the development of technology such as GIS technology, infotech, optimization computation, external fertility quantitative simulation application of model is more extensive, and this stage is estimated and begun to step into sxemiquantitative, basal ration period.

2. domestic research

China's farmland quality evaluation study has a long history, and is to study land classification in the world, carry out land valuation country the earliest." a pipe ground person piece of writing " before more than 2000 year is according to underground water table, natural vegetation, soil property and yield-power difference, the soil is divided into third-class 18 classes, every class is divided into five kinds again, totally nine ten kinds, be in the world the earliest land classification and the scientific paper of land valuation.It is third-class that " Yu's tribute " book then is divided into upper, middle and lower according to the difference of soil natural fertility with the soil, nine divisions of China in remote antiquity, and every grade is divided into three grades of upper, middle and lower again, and totally nine grades, and press soil grade regulation feudal land tax standard (king in groups, 1982)." all gift ground lawsuit on foot " then emphatically based on soil fertility, classifies by color and quality to nine divisions of China in remote antiquity soil, with the method for local product " (king in groups, 1982) develops production.The Northern Wei Dynasty Important Arts for the People's Welfare one book think " it is good thin that physical features has, and the pool, mountain has different suitable, when following the mandate of heaven, the amount land productivity, then firmly less and success is many " (Miu Qiyu, 1982).These all are the works that relevant the earliest in the world farmland quality is estimated.And each dynasty in the Chinese history, all adopt various mode to manage the soil, carry out land classification and evaluation, as the foundation of formulating tax revenue.

China starts from twice overall survey of soil behind the fifties than the farmland quality evaluation study of system, carries out the overall survey of soil for the first time in 1958.Then carried out the whole nation overall survey of soil for the second time that lasts 16 years again in 1979.In the overall survey of soil second time, finish the overall survey of soil in 2444 counties, 312 state farms and 44 forestry districts, gather and write " Chinese soil " and monographs such as " Chinese soil species will ", provide full and accurate basic data for carrying out the farmland quality evaluation.To middle nineteen seventies, is the incubation period that China's land resource subject forms from middle fifties.The land valuation of carrying out in conjunction with the soil geography in the natural resources integrated survey of Chinese Academy of Sciences tissue and land resources survey, by hydrothermal condition the whole nation is divided into district and secondary area, complexity by the measure of reclamation of wasteland is divided into five grades or third-class again under secondary area, waits following grouping.Representative achievement has: " Xinjiang Evaluation for Soil Resources and estimation ", " the suitable agricultural land resource in area, the Inner Mongol " (Cheng Hong, 1962).The land valuation in this period belongs to regional individual event evaluation study mostly, combines closely with production and construction, has stronger specific aim, but because the experience color is denseer, the theory summary deficiency.Middle nineteen seventies began to the eighties mid-term, was the maturity stage of China's land valuation research.The land valuation research in this period progressively transfers comprehensive and comprehensive land valuation to by the individual event evaluation, provincialism research combines with national research, remote sensing technology has obtained using more widely in land valuation investigation and drawing, and has progressively formed and have China's characteristic land valuation theory and method system.The Chinese Academy of Sciences, State Planning Commission's natural resources are combined and have been formulated land resource classification and land valuation project and the index that tallies with the national condition under the hosting of examining meeting, worked out " Chinese 1:100 ten thousand land resource maps ", published 63 width of cloth maps and the Chinese soil resource database that cover the whole nation.Soil research work has in the past systematically been summed up in these researchs comprehensively, the series of theories and the method problem of land resource and compilation thereof have been illustrated, obtain the data of various places, all kinds of land qualities, effectively promoted the in-depth of China's land valuation theory and the development of method.The principal feature of this land resource research in period is: (1) moves towards nationwide research from provincialism; (2) move towards national comprehensive resources research from the individual event resource; (3) the more important thing is from experience and rise to theory and systematic research, thereby begun to take shape land resource and research system thereof with China's characteristic; (4) remote sensing technology obtains using more widely in the land resource drawing.The eighties mid-term, the soil quality evaluation regional study is from the whole nation or economize progressively the carrying out the transition to of (district) on a large scale, small area, pay attention to that more task is put into practice in land valuation research and regional land use planning etc. and combine.Progressively turn to nature, economic comprehensive evaluation from laying particular stress on estimating naturally of soil in the past, more attention is concentrated on urban land and the evaluation of tourism land used, thereby improved the practicality of land valuation achievement.Simultaneously, new and high technologies such as metering method and remote sensing, Geographic Information System obtain using in land valuation increasingly extensively, have strengthened quantification, robotization and the performance prediction of land valuation.As: the comprehensive regulation of the construction of the northwest-north-northeast China networks of shelterbelts, loess plateau soil erosion, (Shen Yuan village, Fengtai District, Beijing, 1987), Sichuan Xichang City (Yang Zisheng, 1989), the farmland quality evaluation of Qingfeng County, Henan (Wang Guoqiang, 1990), counties and cities such as (Lin Deguang, 1990), suburbs, Kaifeng.Most of provinces and cities have carried out arable land soil fertility assessment indicator system and have set up work whole nation agricultural technology extension service centre of 2002-2003 Ministry of Agriculture in the whole nation, have set up national arable land grade gradation data storehouse and management information system.

In sum: China to farmland quality evaluation integral status is at present: 1. the discussion that farmland quality is estimated is paid attention to inadequately, and is unclear to purpose, the meaning understanding of its research; 2. in some areas the farmland quality grade has been done some pilot researchs, but how to have carried out in conjunction with the artificial chartography of estimating with expertise on evaluation method, the method means are comparatively backward; 3. the farmland quality evaluation, particularly farming land under relevant GIS supports grades to define the level to study does not still have more perfect propagable practical experience and technical system so far; 4. big zone, the comparable evaluation of dissimilar regional farmland qualities still there is not clear and definite method and technology.

The present invention adopts the stacked technology in GIS space and adopts hierarchical clustering method to divide the control indexes district, principal component analysis (PCA) is determined factor of evaluation, fuzzy stratification is determined weight, the piecewise function method is with the factor index standardization, adopt modular designs to calculate exponential sum economic dispatch indexes such as index, utilization such as arable land nature quality, make up farmland quality factors combine analogy system, frequency method and equidistant method combine and divide the farmland quality grade.Can reflect zone multi-level comparable, macroscopical zonality differentiation rule and farmland quality steady in a long-term, again accurately non-regional differentiation rule in the reflecting regional landscape scale scope, embody farmland quality and economic results in society that the short-term human activity influences.The reasonable utilization and protection, the land development arrangement that can be province territory and even national cultivated land resource provide strong support and reference; to the unified management of big zone or dissimilar regional cultivated land resources clear and definite support technology, thereby for formulate the cultivated land resource exploitation with the planning utilization policy, guarantee that grain security provides technical guarantee.

Summary of the invention:

The present invention utilizes on level and the land efficiency basis in fusion light and temperature potential productivity, soil production potential, soil, adopt modular design to use progressively the revision method and set up the farmland quality appraisement system, with arable land nature quality is core, by the natural talent and lasting efficient utilization of coordinating cultivated land resource, both reflected comparable, macroscopical zonality differentiation rule and farmland quality steady in a long-term, again accurately non-regional differentiation rule in the reflecting regional landscape scale scope, embody farmland quality and economic results in society that the short-term human activity influences; Make up the comparable cultivated land resource quality comprehensive quantitative evaluation technical parameter system of different scale; Set up the factors combine analogy system of cultivated land resource quality assessment.

One, the foundation of the comparable farmland quality assessment technique parameter system in zone

The division in 1 factor of evaluation control indexes district

Based on the stacked technology in GIS space, by stacked integration to climatic map and topography and geomorphology figure, form composite diagram, thereby determine farmland quality control indexes one-level district, on one-level subregion basis,, be base unit with the villages (towns) according to secondary district index, adopt hierarchical clustering method respectively Nei Ge township, one-level district to be sorted out, realize the Preliminary division in farmland quality control indexes secondary district by the calculating in following each step.

(1) adopt the raw data standardization of standard deviation transformation approach to every index, its formula is:

$Z_{\mathrm{ij}}=\frac{X_{\mathrm{ij}}-{\stackrel{\‾}{X}}_j}{{\mathrm{\σ}}_j}$ (formula 1)

Wherein: ${\mathrm{\σ}}_j=\sqrt{\frac{1}{n-1}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}(X_{\mathrm{ij}}-X_j)},$ In the formula, X _IjBe the actual value of every index, X _jBe X _IjAverage; σ _jBe X _IjStandard deviation.

(2) the employing Euclidean distance is calculated the clustering distance between every index, and its formula is:

$d_{\mathrm{ij}}=\sqrt{\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{(X_{\mathrm{iK}}-X_{\mathrm{jK}})}^2}$ (formula 2)

(3) adopt the sum of squares of deviations method to carry out cluster analysis, promptly establishing Gp and Gq and class is Gr, and then the distance of Gr and another kind of Gk is:

$D_{\mathrm{Kr}}^2=\left(\frac{n_i+n_p}{n_i+n_r}\right)D_{\mathrm{Kp}}^2+\left(\frac{n_i+n_q}{n_i+n_r}\right)D_{\mathrm{Kq}}^2+\left(\frac{n_i}{n_i+n_r}\right)D_{\mathrm{pq}}^2$ (formula 3)

(4) the control indexes zoning is divided product test

According to cluster analysis result, if comprise the secondary district of varying number in each one-level district, net result need return all the secondary districts in the one-level district declares check.Its mathematical method and step are summarized as follows:

1. calculate the average of respectively distinguishing discriminant criterion respectively.

2. by computer solving, obtain the undetermined coefficient λ of decision function, and the equation of listing discriminant function in

3. calculate the discriminant critical value R in two districts _Ij, computing formula is:

$R_{\mathrm{ij}}={\mathrm{\λ}}_a\left[\frac{\mathrm{\Σ}{A}_i+\mathrm{\Σ}{A}_j}{n_i+n_j}\right]+{\mathrm{\λ}}_b\left[\frac{\mathrm{\Σ}{B}_i+\mathrm{\Σ}{B}_j}{n_i+n_j}\right]+\·\·\·\·\·\·+{\mathrm{\λ}}_n\left[\frac{\mathrm{\Σ}{N}_i+\mathrm{\Σ}{N}_j}{n_i+n_j}\right]$ (formula 4)

In the formula, R _IjBe the discriminant critical value in i district and j district, ∑ A _i, ∑ B _i..., ∑ N _iIt is each index sum of i district; ∑ A _j, ∑ B _j..., ∑ N _jIt is each index sum of j district; n _i,, n _jBe respectively the i district, j district representative sample is counted.

4. calculate and wait to declare the discriminant value of sampling point, and belong to differentiation.Each desired value substitution discriminant function of sampling point will be waited to declare, this sampling point can be tried to achieve

Be not worth R.Belong to according to following condition then

Not: when R at R _Ij→ R _iOne side, i district due to; When R at R _Ij→ R _jOne side, j district due to.

For not participating in calculating and wrong

County (city), in conjunction with topography and geomorphology, ecologic environment feature, determine its ownership again by analogy method.

2 farmland quality evaluations participate in evaluation and electing factor and Weight Determination

The factor that influences farmland quality has a lot, and the factor that different control indexes district influences farmland quality also is not quite similar, and this has increased the difficulty of definite assessment indicator system greatly.For this reason, the technology of hierarchical control index is proposed, by the division in control indexes district, control the selection of the factor that influences farmland quality, in each control indexes district, press not same-action yardstick structure factor of evaluation index system, make the selection of quality farmland quality factor of evaluation that the basis of abbreviateing arranged.And The present invention be directed to the following farmland quality evaluation that ensures that cultivated land resource safety and grain security strategy are carried out, be not the real soil fertility evaluation of just carrying out at the current grain yield of influence, therefore selecting is to influence metastable nature of farmland quality and social and economic condition.

(1) farmland quality evaluation the determining of factor that participate in evaluation and electing

The utilization principal component analysis (PCA) is studied the influence factor of arable land sample, for foundation is sought in the farmland quality evaluation.By SPSS14.0 software, realize each step of principal component analysis, the selected characteristic value is greater than 1.5, and the accumulation contribution rate replaces original factor greater than 75% major component.

(2) farmland quality the determining of factor weight that participate in evaluation and electing

For concrete zone, the factor of restriction farmland quality is different, and same factor is also inequality to local farmland quality influence degree, and this brings difficulty for the comparability of the evaluation of farmland quality, so be necessary to carry out the calculating of influence factor weight at each concrete zone.The present invention adopts fuzzy stratification to carry out restriction factor and chooses with weight and determine.

Utilize thick

The correlation theory of collection and fuzzy set is at first carried out fuzzy clustering according to used characteristic attribute, finds optimal classification, regards it the classification of certain decision attribute as, can obtain the k collection of equal value by certain hypothesis decision attribute.According to same method, delete single attribute C successively _iCarry out fuzzy clustering again, thereby obtain conditional attribute C-{c _iThe equivalence collection; Degree of dependence γ (C-{c between design conditions attribute and the decision attribute then _i, D), try to achieve attribute c _iSignificance level; Utilize the method for normalization significance level to find the solution the weight of each factor at last. concrete steps are as follows:

Step1: object set is X={x ₁, x ₂..., x _n, the conditional attribute value is (x _I1, x _I2..., x _Im), obtain raw data matrix, classify by the general step of fuzzy cluster analysis.

$A=\left[\begin{array}{cccc}{x}_{11}& x_{12}& \·\·\·& x_{1m}\\ x_{21}& x_{22}& \·\·\·& x_{2m}\\ \·& \·& \·& \·\\ \·& \·& \·& \·\\ \·& \·& \·& \·\\ x_{n1}& x_{n2}& \·\·\·& x_{\mathrm{nm}}\end{array}\right]$

Step2: determine best confidence level threshold value λ by F-statistic method, find out optimal classification

Y＝{Y ₁，Y ₂，...，Y _s}，

Y _iRepresent a collection of equal value. this classification is used as the set of the equivalence collection of certain decision attribute.

Step3: deletion conditional attribute c _i(i=1,2 ..., m) after, the raw data matrix after obtaining deleting is classified by the method for fuzzy cluster analysis to this matrix, utilizes the F-statistic to determine best confidence level, finds out

Remove conditional attribute c _iAfter optimal classification, deleted c successively _iCategory set:

E={E1, E2 ..., Em} (formula 5)

Wherein: for different i values, k also can be different; $\mathrm{Ei}=\{Y_1^{\left(i\right)},Y_2^{\left(i\right)},\·\·\·,Y_k^{\left(i\right)}\}$ The classification collection of equal value that obtains behind i conditional attribute of expression deletion;

L collection of equal value of resultant classification behind i conditional attribute of expression deletion.

Step4: utilize thick

The collection relative theory is found the solution the significance level of each attribute. and find the solution the union of each following approximate collection that collects of equal value of decision attribute respectively, formula is:

${\mathrm{POS}}_{C-\left\{c_i\right\}}\left(D\right)=\{C-\left\{c_i\right\}-\left(D\right)=\∪\{\{C-\left\{c_i\right\}\}-Y_l\}$ (formula 6)

1≤l≤s wherein, and by conditional attribute C-c _iThe equivalence collection of decision classification is $\mathrm{Ei}=\{Y_1^{\left(i\right)},Y_2^{\left(i\right)},\·\·\·,Y_k^{\left(i\right)}\},$ Calculate the degree of dependence of two property sets by rough set definition 3

γ (C-{ci}, D)=| POS _C-{ci}(D) |/| U| (formula 7)

Define 4 solving condition attribute c by the rough set correlation theory again _iSignificance level SGF (c _i, C, D).

Step5: the significance level according to each conditional attribute, assign weight with normalization processing method, the weight allocation formula is

$W_i=\mathrm{SGF}(c_i,C,D)/\underset{k=1}{\overset{m}{\mathrm{\Σ}}}\mathrm{SGF}(c_k,C,D)$ (formula 8)

(3) the farmland quality factor index score value that participates in evaluation and electing is determined

The factor that participates in evaluation and electing is to other influence degrees such as farmland qualities, and the increase and decrease with factor ATTRIBUTE INDEX data is not Linear change is non-linear sometimes.So these ATTRIBUTE INDEX data need be carried out the conversion of ATTRIBUTE INDEX data one factor quality score.The essence of this conversion is that the dimensionless of the different desired values of the factor of carrying out is handled, and the characterization value that promptly obtains a new factor quality is the factor index quality score one by one.With the computational data that the factor index quality score is estimated as farmland quality, can be more See, reasonably reflect the size of factor that participate in evaluation and electing to arable land grade influence.

Participate in evaluation and electing concerning on the basis of each ATTRIBUTE INDEX value of factor and arable land nature quality analyzing arable land nature quality, by subordinate function and indirect assignment method, employing centesimal system relative value method is calculated each natural quality factor index score value that participates in evaluation and electing.

(4) standard cropping system

The present invention at requirement and the victimization state and different multiple cropping modes requirement to hydro-thermal of crop to ecologic environment, carries out determining of subregion standard cropping system to control indexes secondary district by the ecological suitability analysis of chief crop respectively.

(5) calculate crop phototemperature potential productivity index α

Light and temperature potential productivity is the basis of arable land nature grade estimation, and it is based on local solar radiation, comes by correction calculation photosynthetic, temperature, and concrete calculation procedure is as follows:

1. the calculating of built-up radiation

Calculate each month built-up radiation Q in growth season _i(cal/cm ². month):

Q _i=59Q _Ai* D _i* (a+b*S _i) (formula 9)

In the formula:

I=1,2 ..., each month in n crop growth season;

Q _AiEach monthly average aeropause day radiant quantity

D _iEach month actual fate;

A, b each department radiation regression coefficient

S _iThe monthly average percentage of sunshine.

Growth season built-up radiation Q:

$Q=\underset{i=1}{\overset{n}{\mathrm{\Σ}}}\left[Q_i\right]$ (formula 10)

I=1,2 ..., each month in n crop growth season

2. the calculating of photosynthetic production potential

Moonlight symphysis product speed is Y in plant growth season _Pi:

Y _Pi=(E*Q _i)/[h (1-C _A)] (formula 11)

(formula 12)

In the formula:

I=1,2 ..., n is each month in crop growth season;

CA crop ash content is taken as 0.08

The required heat of the every formation 1 gram dry of h equals the dry heating power;

The theoretical efficiency of light energy utilization of E;

The ξ photosynthetically active radiation accounts for the ratio of built-up radiation, is taken as 0.49

The blade face reflectivity in α plant growth season on average is taken as 0.08

The β crop groups is penetrated rate to the leakage of solar radiation, average out to 0.06

The saturated limitation rate of γ light generally is not construed as limiting under field conditions (factors), is taken as 0

The non-photosynthetic organ of ρ crop is taken as 0.1 to the invalid absorption of solar radiation

ω crop breather loss rate is taken as 0.3

Quantum conversion is taken as 0.224

Photosynthetic production potential Y of plant growth season _p:

$Y_P=C_H*\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{Y}_{\mathrm{Pi}}$ (formula 13)

C _HThe crop harvest index correction coefficient, the economic yield of expression results accounts for the ratio of crop total biomass.

3. the calculating of light and temperature potential productivity

The light and temperature potential productivity of each month calculates general formula:

Y _PTi=Y _Pi* f (T _i) (formula 14)

Wherein:

Y _PtiThe light and temperature potential productivity of each month in growth season;

F (T _i) each month the temperature effect function, because of crop species different; Paddy rice, corn are the warm crop of happiness, and wheat is the cool crop of happiness.

Like cool crop

(formula 15)

Like warm crop

(formula 16)

In the formula: each monthly mean temperature in t plant growth season.

Light and temperature potential productivity in plant growth season:

$Y_{\mathrm{PT}}=C_H*\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{Y}_{\mathrm{PTi}}$ (formula 17)

YPT is the crop light and temperature potential productivity;

CH is the crop harvest index correction coefficient.

(6) standard grain output and rate ratio factor beta

Standard grain output is to be as the criterion with the benchmark crop yield, the output that other specify the output of crop to obtain with the conversion of rate ratio coefficient.

Rate ratio coefficient (β) is benchmark crop actual output and local various appointment crop unit area actual output ratios.Theoretically, crop yield is exactly that productive capacity for the different plot that make zones of different, growing different crops can compare than the effect of coefficient, thereby judges the height of two places productive capacity.In fact be exactly for the conversion between the Different Crop output provides a conversion factor, and then make between the Different Crop output and have comparability.

Ratio by locality various crop light and temperature potential productivity index and benchmark crop light and temperature potential productivity index is the rate ratio coefficient.With control indexes one-level district is unit, by the calculating of each chief crop light and temperature potential productivity, is calculated as follows the rate ratio coefficient of respectively specifying crop.Produce potentiality on rate ratio coefficient=benchmark crop light and temperature potential productivity/appointment crop light temperature

Two, adopt modular designs to carry out different levels farmland quality comprehensive evaluation

Other evaluations such as 1 arable land nature quality

Arable land nature quality refers to plough and can continuously supply and satisfy the needs of crop growth to illumination, temperature, moisture and nutrient, and the oeverall quality that supports plant growth base and other environmental baselines and performance.Forming principle according to crop yield, is the mxm. of arable land production potential with the light and temperature potential productivity of ploughing, and selects to form indexs such as closely-related soil and orographic factor with arable land nature quality, forms arable land nature quality assessment submodule.The following formula of the basis of indexes such as arable land nature calculates:

R _i=∑ R _Ij(formula 18)

Wherein, R _IjComputing formula is: R _Ij=α _j* C _Lij* β _j/ 100

In the formula: R _Ij---i unit j kind is specified the indexes such as natural quality of crop;

R _i---indexes such as the arable land nature quality of i evaluation unit;

∑---continuous adding operation symbol;

α _j---the light and temperature potential productivity of j kind crop;

C _Lij---plant the arable land nature quality score that the j kind is specified crop in i the evaluation unit;

β _j---the j kind is specified the rate ratio coefficient of crop.

C wherein _LijComputing formula is:

$C_{\mathrm{Lij}}=\underset{k=1}{\overset{m}{\mathrm{\Σ}}}{W}_k*f_{\mathrm{ijk}}/100$ (i=1,2 ... p; J=1,2 ... n; K=1,2 ... m) (formula 19)

In the formula: W _kThe weight of-the k factor of evaluation;

C _Lij-evaluation unit is specified crop nature quality branch, is dimensionless number;

I-evaluation unit numbering; J-appointment crop numbering;

K-factor of evaluation numbering; The number of p-evaluation unit;

The number of n-appointment crop; The number of m-factor of evaluation;

f _IjkThe quality of k factor of evaluation of j kind crop in-the i unit

Other evaluations such as 2 cultivated land utilization quality

Although local climate and arable land natural ecological condition have similarity to a certain extent in the specific region, but because agro-farming history, the planting habit of regional cultivated land resource are different with economical activities of mankind intensity, and causing the cultivated land resource quality to have regional differentiation, the society that the development and utilization of arable land production potential is subjected on average utilizes the restriction of level.Therefore, the mass of foundation that reply is ploughed carries out the correction that the long-term soil of society utilizes situation, and promptly cultivated land resource utilizes quality assessment.Its evaluation is to revise reflecting regional agro-farming level, utilize the influence to the performance of cultivated land resource quality such as intensity and attitude towards labour by making up with the cultivated land utilization coefficient of zone leveling actual output and potential theoretical yield ratio.The difference of the cultivated land resource quality that causes owing to the difference of utilizing level on main identical land quality of embodiment and the potentiality, reflecting ploughs on average utilizes the cultivated land resource that forms under the level conditions to utilize the quality assessment submodule social for a long time thereby set up.

(1) calculating of cultivated land utilization coefficient

1. according to standard cropping system and rate ratio coefficient, calculate the standard grain actual output of sampling point:

Y=∑ Y _j* β _j(formula 20)

In the formula: the standard grain actual output of Y-sampling point;

Yj-j kind is specified the actual output of crop;

β j-j kind is specified the rate ratio coefficient of crop.

2. specify the maximum standard grain per unit area yield of crop maximum production, rate ratio coefficient calculations sampling point according to the first class index control zone:

Ymax=∑ Yj, max β j (formula 21)

In the formula: the maximum standard grain of Ymax-sampling point per unit area yield;

Yj, max-sampling point j kind is specified the maximum production of crop;

β j-sampling point j kind is specified the rate ratio coefficient of crop.

3. calculate the comprehensive cultivated land utilization coefficient of sampling point

K _Lij=Y _Ij/ Y _Jmax(formula 22)

In the formula: K _LijThe comprehensive soil usage factor of-sampling point;

Y _IjThe standard grain actual output of-sampling point;

Y _{J, max}Maximum standard grain per unit area yield in the-index district.

(2) calculating of index such as cultivated land utilization quality

Indexes such as cultivated land utilization are calculated as follows:

Y _i=R _i* K _L(formula 23)

In the formula: Y _i-the i index such as evaluation unit soil utilization;

The indexes such as natural quality of Ri-i evaluation unit;

K _LEquivalent district, place ,-unit comprehensive soil usage factor.

Other evaluations such as 3 arable land economic benefit quality

Have identical production potential and the social cultivated land resource that on average utilizes level, what embodied only is that regional cultivated land resource has identical development and utilization level.Yet because the social and economic condition difference can cause the input-output level difference, cause the land revenue difference that obtains on the same soil, this just means in identical society and on average utilizes its operating income of cultivated land resource under the level conditions to have otherness.Therefore by payment based on land shares lapse rate theory, arable land economic coefficient correction by setting up the zone leveling inputoutput situation of ploughing the reflection specific region and optimum average input output ratio is owing to the different differences that cause the cultivated land resource quality of economic benefit, and it is that the turnout of crop is modified to the cultivated land resource economic benefit quality that comprises the soil input-output efficiency that cultivated land resource is utilized quality level.Set up the cultivated land resource economic quality according to soil society average input output level between zones of different and estimate submodule.

(1) calculating of arable land economic benefit coefficient

1. calculate " output-cost " index

With the input-output data that each sampling point investigation obtains, be calculated as follows " output-cost " index of sampling point:

α _j=Y _j/ C _j(formula 24)

In the formula: α _j-sampling point output-cost index;

Y _j-sampling point j kind crop actual output;

C _j-sampling point j kind is specified the crop real cost.

2. be calculated as follows the comprehensive arable land economic benefit coefficient of sampling point

Kc=α/A (formula 25)

In the formula: the comprehensive soil economic coefficient of Kc-sampling point;

Comprehensive output-the cost index of α-sampling point;

A-index district maximum production-cost index.

(2) calculating of index such as arable land economic benefit quality

Indexes such as cultivated land utilization through arable land economic coefficient correction after, promptly get the economic index of ploughing, its definition is:

G _i=Y _i* K _c(formula 26)

In the formula: G _iThe indexes such as arable land of-the i evaluation unit;

The indexes such as cultivated land utilization of Yi-i evaluation unit;

K _c-comprehensive soil economic coefficient.

Three, farmland quality factors combine analogy system construction

In the farmland quality evaluation procedure,, express not because the adopting factor number is many See and be difficult to use the GIS technology and analyze, therefore, at inner link between farmland quality and the factor and characteristics, by to influencing each factor analysis of cultivated land resource quality, each factor of evaluation is carried out classification, and the different stage of each factor of evaluation form with digital code made up one by one, to each combination comprehensive conclude and the basis of the continuous branch of class on, use the factors combine type See and express farmland quality, thereby set up the factors combine analogy system of reflection farmland quality, and, simplify the expression way of different scale cultivated land resource quality with this bridge as connection territory, county, control indexes district and province's each level of territory.

The farmland quality leveling factors that participates in evaluation and electing is adopted in the design of factors combine, and each index rank of the factor of participating in evaluation and electing is represented with digital code, and combination promptly forms each factors combine type of ploughing one by one again.Every type arable land has unique factors combine, and each factors combine is represented the arable land of unique type, and arable land factors combine code determines that with reference to the result of the factor index criteria for classifying different indexs is given different digital codes.For example: irritate

Fraction for fully satisfy, table soil property ground is configured as entire body earth, organic matter for light earth, the soil body 2.0 evaluation unit, its factors combine is 1111.

The farmland quality grade has reflected the height difference of a certain regional arable land nature quality, factors combine is a main natural cause and can influence the factor of farmland quality and the assembly of state in the cultivated land utilization system, by factors combine quantity in each grade of statistics farmland quality, thereby visit

Other analogy relation such as factors combine and farmland quality.

Embodiment:

The present invention economizes X by said method and carries out the farmland quality evaluation.

The foundation of 1 farmland quality assessment technique parameter system

(1) division in X province factor of evaluation control indexes district

1. the division in one-level district

Based on the stacked technology in GIS space, by X being economized the stacked integration of climatic map and topography and geomorphology figure, form composite diagram, thereby determine X province farmland quality control indexes one-level district, weather, landforms that X province factor of evaluation one-level district adopts the mark control zone are divided index feature such as following table:

Table 1 control indexes district one-level subregion essential characteristic

2. the division in secondary district

On one-level subregion basis,, be base unit with the villages (towns) according to secondary district index in the last table, adopt hierarchical clustering method respectively Nei Ge township, eight one-level districts to be sorted out, by cluster process, reach back declare check and adjust after, determine that finally X economizes farmland quality control indexes region two-stage subregion.

(2) participate in evaluation and electing factor and Weight Determination of farmland quality evaluation

1. farmland quality evaluation the determining of factor that participate in evaluation and electing

The utilization principal component analysis (PCA) is studied 14 factors that X economizes 49053 samples of ploughing, for foundation is sought in the farmland quality evaluation.By SPSS14.0 software, realize each step of principal component analysis, the selected characteristic value is greater than 1.5, and the accumulation contribution rate replaces original factor greater than preceding 3 major components of 75%, obtains table 3 and table 4.

Table 3 influences eigenwert and the contribution rate that X economizes preceding 3 major components of farmland quality

Table 4 influences the factor loading matrix that X economizes preceding 3 major components of farmland quality

By table 4 data as can be seen, eigenwert has 3 greater than 1.5 major component, and first three major component eigenwert

Long-pending contribution rate reaches 76.82%, has surpassed 75%.According to interrelated data, eigenwert is greater than 1, major component

Significant, and

Long-pending contribution rate is chosen major component, optional preceding 3 major components greater than 75% principle.The factor loading matrix of corresponding preceding 3 major components that obtain of 76.82%. that preceding 3 quantity of information that major component comprised account for the overall information amount has shown in the table 3 and has estimated 3 interior main overall targets of full detail scope that X economizes selected 14 the evaluation farmland qualities that factor constituted of farmland quality.

The factor loading amount of each major component can reflect the size of each factor to this overall target relativity.Therefore the factor loading amount size according to each factor can select the principal element that influences X province farmland quality, comprising: effective soil layer thickness, table soil property ground, soil body configuration, terrain slope, filling

Fraction, draining situation, the soil organism, degree of salinity, totally eight factors.

2. farmland quality the determining of factor weight that participate in evaluation and electing

Use said method the index in 23 secondary districts is carried out the calculating of weight, the result is:

Each control indexes secondary district farmland quality of table 5 X province participate in evaluation and electing factor and weighted value

(3) X province standard cropping system is determined the result

Economize the ecological suitability analysis of chief crops such as wheat, corn, paddy rice by X, at requirement and the victimization state and different multiple cropping modes requirement to hydro-thermal of crop, respectively control indexes secondary district is carried out determining of subregion standard cropping system to ecologic environment.

(4) crop phototemperature potential productivity index α calculates

Calculate the light and temperature potential productivity that can obtain each county of the whole province (city, district, field) by above-mentioned formula by said method.

(4) result of calculation of rate ratio coefficient

Economizing the benchmark crop based on X, is the rate ratio coefficient by the ratio of locality various crop light and temperature potential productivity index and benchmark crop light and temperature potential productivity index.With X province eight control indexes one-levels district is unit, by the calculating of each chief crop light and temperature potential productivity, is calculated as follows the rate ratio coefficient of respectively specifying crop.Produce potentiality on rate ratio coefficient=benchmark crop light and temperature potential productivity/appointment crop light temperature

Table 7 X province specifies crop phototemperature potential productivity and rate ratio coefficient in each control indexes district

2 adopt modular designs to carry out different levels farmland quality comprehensive evaluation

According to preceding method, calculate each evaluation unit farmland quality index Ri, Yi and Gi by the villages (towns), and index Ri, Yi and the Gi frequencies such as each level quality of drawing evaluation unit by control indexes district, the whole province respectively

Side figure.According to exponential-frequency such as each level quality

Obvious flex point on the side figure, to each control indexes district, each level performance figure of the whole province's cultivated land resource compare and with on the spot the checking, final determine cultivated land resource nature quality, utilize quality, economic quality grade to be divided into the boundary line with 200,200,100 respectively to divide that standard sees the following form:

The table 8 X province arable land nature quality grade criteria for classifying

Table 9 X economizes the cultivated land utilization quality grade criteria for classifying

The table 10 X province arable land economic quality grade criteria for classifying

The 3 farmland quality regularity of distribution and landscape patterns

According to each level farmland quality criteria for classifying X is economized each level quality of ploughing and carry out the grade division, and draw X and economize each level quality grade figure of cultivated land resource, see Fig. 3, arable land nature quality grade figure, Fig. 4, cultivated land utilization quality grade figure, Fig. 5, arable land economic benefit quality grade figure.

X province arable land nature quality etc. is divided 15 grades altogether, and on the distributed number, two to six wait each homalographic large percentage, account for 64.57% of the whole province's area altogether, and the area maximum is 1122454.281 hectares wherein third-classly, accounts for the whole province's cultivated area 17.25%.Ground areas such as 14 and 15 are less, and both account for 1.25% of the whole province's area altogether; Wherein ground area minimums such as 15 are 13638.5046 hectares, account for X and economize 0.21% of cultivated area; Show that X province arable land nature quality is totally more excellent.。

X province cultivated land utilization quality etc. is divided 14 grades altogether, on the distributed number, and first-class and second-class ground area minimum, both areas only account for 0.49% of the whole province's area.Area is bigger is five grades, six etc., seven etc., ten first-class, ten second-class and ten third-class, and wherein ten third-class ground area maximums are 797987.62 hectares, and ten first class take second place.

Economic quality etc. are divided 14 grades altogether, and each grade area waits until that from one ten fourth class have trend of rising gradually.Ten third-class ground area maximums account for the whole province's area ratio and reach 18.69%; Taking second place to ten fourth class, is 18.36%; First-class, second-class and third-class ground area is less, three's area sum only accounts for 0.87% of the whole province's area ratio, and it is lower to show that X economizes agriculture inputoutput economic benefit.

4X economizes the composite type space and divides situation:

The design of factors combine adopts the X that participates in evaluation and electing to economize the farmland quality leveling factors, and each index rank of 8 factors that participate in evaluation and electing is represented with digital code, and combination promptly forms each factors combine type of ploughing one by one again.Every type arable land has unique factors combine, and each factors combine is represented the arable land of unique type, and arable land factors combine code determines that with reference to the result of the factor index criteria for classifying different indexs is given different digital codes.For example: irritate Fraction for fully satisfy, table soil property ground is configured as entire body earth, organic matter for light earth, the soil body 2.0 evaluation unit, its factors combine is 1111.Material elements index and digital code corresponding relation see the following form:

Table 11 factor index and digital code corresponding tables

It is inequality that different control indexes district influence the factor kind and the quantity of farmland quality, and the quantity of factors combine is also inequality accordingly.But for making factors combine have comparability inside the province at X, in the design of factors combine, the relative significance level that the code combination of factor influences farmland quality according to eight factors is carried out the stationary arrangement of position in proper order, and promptly the factor of the same position representative of various combination code is identical.Location order is fixed as filling Fraction, effective soil layer thickness, table soil property ground, soil body configuration, the gradient, organic matter, the soil salinization, draining situation.Those the non-limiting factor of not selecting because can fully ensure in different control indexes district, still leave the position, and it is 1 that code is set, by using the method for above-mentioned foundation arable land factors combine model, X is economized evaluating data conclude and gather, finally obtain X and economize 1107 kinds of arable land factors combine types.

In order clearly to cause factors combine type situation of change owing to farmland quality grade difference.Choose 1,2 etc. and 14,15 etc. and represent the higher and lower arable land of nature quality respectively, and choose wide, the representational factors combine type of tool distribution area and compare analysis, see the following form.

The all types of district of table 12 arable land nature quality 14,15 grade regional factor assemblage characteristics

The all types of district of table 13 arable land nature quality 1,2 grade regional factor assemblage characteristics

Shown in table 12 and table 13: 14,15 grade factors combine type areas are 253848.83 hectares, and only accounting for 3.90%, 1, the 2 grade factors combine type areas that whole X economizes cultivated area is 1127675 hectares, accounts for whole X and economizes 17.32% of cultivated area.

X economizes the composite type space and divides situation as follows:

(1) X economizes area greater than in 100000 hectares the factors combine, and 21111411,41111411,11111411 these 3 kinds of arable land factors combine type distribution are quite extensive, and area is all greater than 2 * 105 hectares, and the area summation accounts for X and economizes 12.69% of cultivated area.

(2) X economizes area in the factors combine of 50000-100000hm2, have 21331411,41111421,11111511,41331411,21111411,21111511,11331311 these 7 factors combine types, the total area accounts for X for 507909.6 hectares and economizes cultivated area 7.8%.

(3) X economizes area in the factors combine of 10000-50000hm2, and 21111311 and 41111111 this arable land factors combine type distribution are quite extensive.

(4) it is more less than the factors combine type of 10000hm2 that X economizes area, all has in eight two-level index districts of the whole province

Star distributes.This is because X economizes the topography and geomorphology complexity, and natural conditions differ greatly and cause.Each distinguishes the soil parent material heterogeneity; Obvious difference between the light warm water heat condition various places, wherein distribution of water resources is unbalanced, causes filling Also there is notable difference the fraction various places, and how restricted drainage condition is also; The salination situation originally just belongs to some areas and distributes.

Claims (4)

1, a kind of province-wide comparison tillage quality evaluation method based on GIS, it mainly is technology platform with GIS, index system by provincial parameter macro-control and unified standard, adopt investigation basic data at county level, the calculating of prefecture-level unified Modeling and set up the pattern of four level quality checks, reached the comparable of province territory farmland quality evaluation result.Concrete steps are for adopting the stacked technology in GIS space and adopting hierarchical clustering method to divide the control indexes district, principal component analysis (PCA) is determined factor of evaluation, fuzzy stratification is determined weight, the piecewise function method is with the factor index standardization, adopt modular designs to calculate exponential sum economic dispatch indexes such as index, utilization such as arable land nature quality, make up farmland quality factors combine analogy system, frequency method and equidistant method combine and divide the farmland quality grade.

2, according to the foundation of the farmland quality assessment technique parameter system described in the claim 1, it is characterized in that based on the stacked technology in GIS space, by stacked integration to climatic map and topography and geomorphology figure, form composite diagram, thereby determine farmland quality control indexes one-level district, on one-level subregion basis, according to secondary district index, with the villages (towns) is base unit, adopt hierarchical clustering method respectively Nei Ge township, one-level district to be sorted out, realize the Preliminary division in farmland quality control indexes secondary district by the calculating in following each step.

(1) adopt the raw data standardization of standard deviation transformation approach to every index, its formula is:

$Z_{\mathrm{ij}}=\frac{X_{\mathrm{ij}}-{\stackrel{\‾}{X}}_j}{{\mathrm{\σ}}_j}$ (formula 1)

Wherein: ${\mathrm{\σ}}_j=\sqrt{\frac{1}{n-1}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}(X_{\mathrm{ij}}-X_j)},$ In the formula, X _IjBe the actual value of every index, X _jBe X _IjAverage; σ _jBe X _IjStandard deviation.

(2) the employing Euclidean distance is calculated the clustering distance between every index, and its formula is:

$d_{\mathrm{ij}}=\sqrt{\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{(X_{\mathrm{iK}}-X_{\mathrm{jK}})}^2}$ (formula 2)

(3) adopt the sum of squares of deviations method to carry out cluster analysis, promptly establishing Gp and Gq and class is Gr, and then the distance of Gr and another kind of Gk is:

$D_{\mathrm{Kr}}^2=\left(\frac{n_i+n_p}{n_i+n_r}\right)D_{\mathrm{Kp}}^2+\left(\frac{n_i+n_q}{n_i+n_r}\right)D_{\mathrm{Kq}}^2+\left(\frac{n_i}{n_i+n_r}\right)D_{\mathrm{pq}}^2$ (formula 3)

(4) the control indexes zoning is divided product test

According to cluster analysis result, if comprise the secondary district of varying number in each one-level district, net result need return all the secondary districts in the one-level district declares check.Its mathematical method and step are summarized as follows:

1. calculate the average of respectively distinguishing discriminant criterion respectively.

2. by computer solving, obtain the undetermined coefficient λ of decision function, and the equation of listing discriminant function in

3. calculate the discriminant critical value R in two districts _Ij, computing formula is:

$R_{\mathrm{ij}}={\mathrm{\λ}}_a\left[\frac{\mathrm{\Σ}{A}_i+\mathrm{\Σ}{A}_j}{n_i+n_j}\right]+{\mathrm{\λ}}_b\left[\frac{\mathrm{\Σ}{B}_i+\mathrm{\Σ}{B}_j}{n_i+n_j}\right]+\·\·\·\·\·\·+{\mathrm{\λ}}_n\left[\frac{\mathrm{\Σ}{N}_i+\mathrm{\Σ}{N}_j}{n_i+n_j}\right]$ (formula 4)

In the formula, R _IjBe the discriminant critical value in i district and j district, ∑ A _i, ∑ B _i..., ∑ N _iIt is each index sum of i district; ∑ A _j, ∑ B _j..., ∑ N _jIt is each index sum of j district; n _i,, n _jBe respectively the i district, j district representative sample is counted.

4. calculate and wait to declare the discriminant value of sampling point, and belong to differentiation.Each desired value substitution discriminant function of sampling point will be waited to declare, the discriminant value R of this sampling point can be tried to achieve.Belong to differentiation according to following condition then: when R at R _Ij→ R _iOne side, i district due to; When R at R _Ij→ R _jOne side, j district due to.

Farmland quality evaluation participate in evaluation and electing factor and Weight Determination

(1) farmland quality evaluation the determining of factor that participate in evaluation and electing

The utilization principal component analysis (PCA) is studied the influence factor of arable land sample, for foundation is sought in the farmland quality evaluation.By SPSS14.0 software, realize each step of principal component analysis, the selected characteristic value is greater than 1.5, and the accumulation contribution rate replaces original factor greater than 75% major component.

(2) farmland quality the determining of factor weight that participate in evaluation and electing

Adopting fuzzy stratification to carry out restriction factor chooses with weight definite.At first carry out fuzzy clustering, find optimal classification, regard it the classification of certain decision attribute as, can obtain k collection of equal value by certain hypothesis decision attribute according to used characteristic attribute.According to same method, delete single attribute C successively _iCarry out fuzzy clustering again, thereby obtain conditional attribute C-{c _iThe equivalence collection; Degree of dependence γ (C-{c between design conditions attribute and the decision attribute then _i, D), try to achieve attribute c _iSignificance level; Utilize the method for normalization significance level to find the solution the weight of each factor at last. concrete steps are as follows:

Step1: object set is X={x ₁, x ₂..., x _n, the conditional attribute value is (x _I1, x _I2..., x _Im), obtain raw data matrix, classify by the general step of fuzzy cluster analysis.

$A=\left[\begin{array}{cccc}{x}_{11}& x_{12}& \·\·\·& x_{1m}\\ x_{21}& x_{22}& \·\·\·& x_{2m}\\ \·& \·& \·& \·\\ \·& \·& \·& \·\\ \·& \·& \·& \·\\ x_{n1}& x_{n2}& \·\·\·& x_{\mathrm{nm}}\end{array}\right]$

Step2: determine best confidence level threshold value λ by F-statistic method, find out optimal classification

Y＝{Y ₁，Y ₂，...，Y _s}，

Y _iRepresent a collection of equal value. this classification is used as the set of the equivalence collection of certain decision attribute.

Step3: deletion conditional attribute c _i(i=1,2 ..., m) after, the raw data matrix after obtaining deleting is classified by the method for fuzzy cluster analysis to this matrix, utilizes the F-statistic to determine best confidence level, finds out deletion conditional attribute c _iAfter optimal classification, deleted c successively _iCategory set:

E={E1, E2 ..., Em} (formula 5)

Wherein: for different i values, k also can be different; $\mathrm{Ei}=\{Y_1^{\left(i\right)},Y_2^{\left(i\right)},\·\·\·,Y_k^{\left(i\right)}\}$ The classification collection of equal value that obtains behind i conditional attribute of expression deletion;

L collection of equal value of resultant classification behind i conditional attribute of expression deletion.

Step4: utilize the rough set relative theory, find the solution the significance level of each attribute. find the solution the union of each following approximate collection that collects of equal value of decision attribute respectively, formula is:

${\mathrm{POS}}_{C-\left\{c_i\right\}}\left(D\right)=\{C-\{c_i\left\}\right\}-\left(D\right)=\∪\left\{\right\{C-\left\{c_i\right\}\}-Y_i\}$ (formula 6)

1≤l≤s wherein, and by conditional attribute C-c _iThe equivalence collection of decision classification is $\mathrm{Ei}=\{Y_1^{\left(i\right)},Y_2^{\left(i\right)},\·\·\·,Y_k^{\left(i\right)}\},$ Calculate the degree of dependence of two property sets by rough set definition 3

γ (C-{ci}, D)=| POS _C-{ci}(D) |/| U| (formula 7)

Define 4 solving condition attribute c by the rough set correlation theory again _iSignificance level SGF (c _i, C, D).

Step5: the significance level according to each conditional attribute, assign weight with normalization processing method, the weight allocation formula is

$W_i=\mathrm{SGF}(c_i,C,D)/\underset{k=1}{\overset{m}{\mathrm{\Σ}}}\mathrm{SGF}(c_k,C,D)$ (formula 8)

(3) the farmland quality factor index score value that participates in evaluation and electing is determined, by subordinate function and indirect assignment method, adopts centesimal system relative value method to calculate each natural quality factor index score value that participates in evaluation and electing.

3, according to the farmland quality factors combine analogy system construction described in the claim 1, it is characterized in that by to influencing each factor analysis of cultivated land resource quality, each factor of evaluation is carried out classification, and the different stage of each factor of evaluation form with digital code made up one by one, on to comprehensive conclusion of each combination and the continuous basis of dividing of class, intuitively express farmland quality with the factors combine type, thereby set up the factors combine analogy system of reflection farmland quality, and with this as connecting the territory, county, the bridge of control indexes district and province's each level of territory, the expression way of simplification different scale cultivated land resource quality.