AHP-Based Priority evaluation of Basic Farmland Delimitation ...1970/02/09 · quartering method...
Transcript of AHP-Based Priority evaluation of Basic Farmland Delimitation ...1970/02/09 · quartering method...
AHP-Based Priority evaluation of Basic Farmland Delimitation in XinFu
District
GUO Yong-long 1, WANG Li-huan2,*
1. College of Resources & Environment, Shanxi Agricultural University, Taigu, Shanxi,
China,030801
2. College of engineering, Shanxi Agricultural University, Taigu, Shanxi, China,030801
E-mail: [email protected]
* Corresponding author. E-mail address: [email protected] (WANG Li-huan)
Abstract: By taking Xinfu District as an example, this article establishes a multi-level evaluation index system
for the priority of basic farmland delimitation against the basic farmland reallocation caused by construction land
occupation. In addition, the article uses AHP method to quantifiably evaluate the priority of the basic farmland
delimitation in Xinfu District, Xinzhou City, Shanxi Province,China. The evaluation result shows that the land in
the research area can be classified into 9 grades according to the priority of basic farmland delimitation. The
land of grade 2 to 6 can be classified into the major components of basic farmland. The evaluation result of the
priority of the basic farmland delimitation in the research area can be used as an important reference for the
studies on the basic farmland reallocation in the research area.
Keywords: AHP; Basic farmland delimitation; Priority evaluation.
1. Introduction
Basic farmland refers to the arable land[1] unable to be occupied that is determined in accordance with the
overall plan for land utilization according to the population and socioeconomic development’s demand for
agricultural products at stated periods. By priority evaluation on basic farmland delimitation, as a major basis for
basic farmland delimitation and supplement, is meant to make an evaluation on the basic production capacity of
the existing farmland according to those influence factors comprehensively made up of the terrain and
geomorphic conditions of arable land, the features of parent material, the infrastructures on farmland, the levels
of fertility and the physicochemical characters of soil[2]. Priority evaluation on basic farmland delimitation is
multi-attribute decision-making, and its key is to determine the weight of relevant factors scientifically[3]. At
present, there are a few methods for the determination of factor weight, which are Delphi method [4], analytic
hierarchy process method [5], two term coefficient method[6], principal component analysis method [7], entropy
weight method[8] and deviation and mean square difference method[9], etc. And this article uses AHP method[10]
to determine the weight of each influencing factor.
2.Overview of the research area
Xinfu District is located in north central Shanxi Province and western Xinding Basin. The northern latitude is
between 38°13′ and 38°41′, and the east longitude is between 112°17′ and 112°58′. It is 50km long in east-west
direction and 41km wide in north-south direction, the total land area of the whole district being 1984.37 km2.
The district’s topography is high in the west and low in the east, being oblique gradually from the west to the
east. In addition, it is ringed on northern, western and southern side by mountains. The eastern region, open and
flat, is the main body of Xinding Basin. The southeastern region is the northern slope of Zhoushan Mountain, the
western half is a mountainous area, and the central region is hilly land. The mountainous region covers the
largest land, the area being 919.07km2, accounting for 46.32%, followed by the flat region, the area being
703.77km2, accounting for 36.15%, followed by the hilly region, the area being 361.53km2, accounting for
18.21%.
3. Research data
The farmland to be evaluated in Xinfu District is mainly composed of dry land, irrigable land and a few paddy
fields, and the soil is primarily made up of moisture soil and cinnamon soil (Fig.1). The research data comes
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from Xinfu District’s 1,908 soil samples collected between 2010 and 2011. The soil samples were selected after
the harvest of the preceding crop and before the cultivation of the afterreap crop, and the sampling sites were
positioned with GPS. The soil samples in 1 plough layer were collected according to around 1 hm2.5 points were
laid out randomly like the shape of “S” in each sampling point. After the topsoil was collected and mixed,
quartering method was used to take about 1-kg soil for lab analysis. Through a field investigation on such
attribute data as the distance between the sampling points and the villages, the longitude and latitude, the
altitude, the ground water depth, and the households in the natural villages, and through the use of Xinfu
District’s 10-m contour line to generate DEM, such attributes as gradient, exposure and relief amplitude were
obtained. The physicochemical attribute of the sampling sites is based on the superposition of soil map and
present land use map for forming evaluation units, according to the number and area of the evaluation units, and
within the control range of the total amount of sampling sites. The rational distribution is based on the
preliminary determination of the layout of the sampling points, and such factors as delineation sizes, cropping
systems, crop strains and yield levels, etc. are used to determine the distributing number and point locations of
evaluation units. In addition, according to the agrotype and cultivated area as well as different ecological
conditions and the land parcels with different fertility levels, the researchers chosen representative soil for spot
sampling and random sampling for lab analysis.
Fig.1 The distribution map of farmland in Xinfu District
4 Research method 4.1 Hierarchical structure model for priority evaluation on basic farmland delimitation
The hierarchical structure model for the assessment indicator system of the priority of basic farmland
delimitation in the research area is composed of 5 elements and 15 factors (Tab.1)
4.2 Methods for determining the weight factor of priority evaluation on basic farmland
delimitation Evaluation takes the priority of basic farmland delimitation as the destination Layer (G layer), takes the site
condition, soil structure, infrastructures on farmland, relatively stable physicochemical properties and labile
chemical characters, which affect the priority of basic farmland delimitation, as the criterion layer (C layer), and
then takes the items, which affect the factors in the criterion layer, as the indicator layer (A layer), to set up a
hierarchical chart for priority evaluation on the priority of basic farmland delimitation. On this basis, 36 experts
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judged the importance of all the participating factors in different layers, to build judgment matrixes in different
layers, to finally gain the combination weight of all evaluation factors.
(1) AHP method to determine weight factor
When applying AHP method, it’s necessary to first build a judgment matrix among indexes, to make a
comparison between any two indexes and bring in a judgment scale to quantize it. When the judgment matrix is
used to calculate feature vectors, the scale should be between 1 and 9. Quantize it according to the judgment
scale (Tab.2) [11] proposed by Professor T. L. Saaty.
Table.1 The assessment factors system on the priority of basic farmland delimitation
Factor Average value Mode value Suggestive value
Site condition (C1) 1.6 1 (17) 1
Soil structure (C2) 3.7 3 (15) 5 (13) 3
Relatively stable physicochemical
properties (C3) 4.47 3 (13) 5 (10) 4
Labile chemical characters (C4) 4.2 5 (13) 3 (11) 5
Infrastructures on farmland (C5) 1.47 1 (17) 1
Topographic position (A1) 1.8 1 (23) 1
Parent material (A2) 3.9 3 (9) 5 (12) 5
Topographic slope (A3) 3.1 3 (14) 5 (7) 3
Available soil thickness (A4) 2.8 1 (14) 3 (9) 1
Topsoil thickness (A5) 2.7 3 (17) 1 (10) 3
Profile pattern (A6) 2.8 1 (12) 3 (11) 1
Topsoil texture (A7) 2.9 1 (13) 5 (11) 1
Unit weight (A8) 5.3 7 (12) 5 (11) 6
Organic material (A9) 2.7 1 (14) 3 (11) 3
Degree of salinity (A10) 3.0 1 (13) 3 (10) 1
PH(A11) 4.5 3 (10) 7 (10) 5
Available phosphorus (A12) 1.0 1 (31) 1
Rapidly available potassium (A13) 2.7 3 (16) 1 (10) 3
Probability of irrigation (A14) 1.2 1 (30) 1
Level of garden (terrace) style (A15) 4.5 5 (15) 7 (7) 5
Tab.2 The criteria scaled definition table
Intensity of
importance Definition Explanation
1 Equal Importance Two activities contribute equally to the objective
2 Weak or slight
3 Moderate importance Experience and judgement slightly favour one activity over another
4 Moderate plus
5 Strong importance Experience and judgement strongly favour one activity over another
6 Strong plus
7 Very strong or demonstrated
importance
An activity is favoured very strongly over another; its dominance
demonstrated in practice
8 Very, very strong
9 Extreme importance The evidence favouring one activity over another is of the highest
possible order of affirmation
Reciprocals
of above
If activity i has one of theabove
non-zero numbers assigned to it
when compared with activity j,
then j has the reciprocal value
when compared with i
A reasonable assumption
1.1-1.9 If the activities are very close
May be difficult to assign the best value but when compared with
other contrasting activities the size of the small numbers would not
be too noticeable, yet they can still indicate the relative importance
of the activities.
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The calculating flow for AHP method to determine weight is as follows:
①Build factor judgment matrix A according to the exports and their relevant researches.
②Calculate the column sum Sj of the factors in each column of the judgment matrix A.
1
=n
j ij
i
S a=
å , ( j=1,2,…,n)
③Divide each factor in the judgment matrix A by the sum Sj of the column in which it is located and work
out a normalized new matrix A*. Here, the normalized matrix refers to a matrix in which the sum of each
column is equal to 1. Given*
1 (a )ijA = , then:
* /ij ij ja a S= , ( i,j=1,2,…,n)
④Calculate the mean value Wi of each row in the new matrix Al and obtain feature vector W. It is the
weight of C1, C2, …, and Cn, all evaluation indicators in the judgment matrix A, to evaluation objectives.
(x)f F= , ( i=1,2,…,n)
⑤Calculate the maximum eigenvalue maxl of comparison matrix.
max
1
(AW)ni
i inWl
=
= å
⑥ Consistency check
Define coincidence indicator CI as:
max( n)(n 1)
CIl -
=-
Define random consistency ratio CR as:
CICRRI
=
In the equation, RI refers to average random consistency index.
When CR≤0.1, the judgment matrixes have satisfactory consistency; When CR>0.1, the judgment matrixes
are inconsistent and have to be corrected.
5. AHP-based Priority evaluation of Basic Farmland Delimitation 5.1 Definition of the index weight of priority evaluation of basic farmland delimitation
As shown in Tab.1 and Fig.1, the evaluation index system for the priority of the basic farmland delimitation in
the research area is a secondary indicator system. In this research, AHP method is used to weigh criterion layer
and measure layer.
When making a comprehensive assessment, this article sets up factor comparison matrix for the 5 elements
and 15 factors, to test the consistency of classification and calculate the internal weight of classification. Finally,
it establishes a comprehensive weight table (Tab.3) for the priority of basic farmland delimitation.
5.2 Factor quantification of priority evaluation indicator of basic farmland delimitation By factor quantification is meant to score all evaluation factors according to evaluation objects’ evaluation
requirements. Centesimal system is usually used for factor quantification. 0% means least appropriate, 100%
means most appropriate, and the percentages between 0% and 100% refer to different degrees of
appropriateness. Different quantitative methods are adopted according to all evaluation factors’ nature
difference, mainly including grade scoring method, function scoring method and spatial analysis method. Finally,
data standardization method is used to adjust factor data between 0 and 100.
(1) Grade scoring method
Grade scoring method is used to quantize scatter factors, including agricultural acreage and farmland grading,
etc. It makes classifications according to the significant variation range of evaluation indexes and then scores
them according to the degree that they affect evaluation items.
(2) Function scoring method
Function scoring method is used to quantize threshold factors. Critical path method, etc., is adopted to find out
the action laws of evaluation factors, to analyze its impact on research objects, and then mathematical function is
used to simulate the relationship between them. The general model is:
(x)f F=
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Table.3 The analytic hierarchy process of assessment factors system on the priority of basic farmland
delimitation
Indicator layer
Criterion layer
Comprehensive
weight
∑CiAi C1
0.3592
C2
0.1198
C3
0.0899
C4
0.0719
C5
0.3592
A1 topographic position
0.6522 0.2343
A2 parent material 0.1304 0.0468
A3 topographic slope 0.2174 0.1286 0.0781
A4 available soil
thickness 0.1428 0.0513
A5 topsoil thickness 0.4286 0.0171
A6 texture structure 0.3704 0.0513
A7 topsoil thickness 0.0617 0.0333
A8 soil bulk density 0.1235 0.0055
A9 organic material 0.3704 0.0111
A10 degree of salinity 0.0740 0.0333
A11PH
0.7500 0.0068
A12 available
phosphorus 0.0539
A13 rapidly available
potassium 0.2500 0.0180
A14 probability of
irrigation 0.8333 0.2993
A15 terrace water
storage rate 0.1667
In the equation: f—the effect value of an evaluation factor, and x—the attribute value of an evaluation factor.
(1) Spatial analysis method
Spatial analysis method is a method used to quantize spatial diffusion factors. In the research, ArcGIS spatial
analysis function is mainly used to compute the spatial distribution of all factors in Xinfu District that affect the
changes.
(2) Data standardization
After the spatial distribution value of all the evaluation factors is obtained by spatial calculation, the factors
should be standardized, that is, the factors should be adjusted between 0 and 100. The general model is:
min max min100 (x x ) / (x x )if = ? -
In the formula, f—the effect value of an evaluation factor, xi—the attribute value of an evaluation factor, xmax—
the maximum attribute value of an evaluation factor, and xmin—the minimum attribute value of an evaluation
factor.
The result of factor quantification finally shows the membership of the evaluation factors for the priority of
basic farmland delimitation: ①Conceptual evaluation factor:The membership and description of all evaluation
factors (Tab.4); ②Numeric evaluation factor:The classification and membership of all evaluation factors
(Tab.5) and membership function (Tab.6).
5.3 Evaluation result of the priority of basic farmland delimitation (1) Determination of evaluation unit
The superposed delineation of the soil map, present landuse map and basic farmland protection map of Xinfu
District is used as the basic evaluation units. At least one soil sample is collected from the similar evaluation
units for analysis, and the attribute data of the evaluation units is linked on the evaluation unit map.
(2) Calculation of the priority of basic farmland delimitation
In this article, index pulsing method is adopted to calculate the comprehensive indexes for the priority of basic
farmland delimitation, that is, to multiply the combination weight of all the evaluation factors by the
corresponding factor grade value (that is, the membership degree obtained by expert experience method or fuzzy
comprehensive evaluation method) for accumulation:
i iIFI B A= ?å (i=1,2,3,……,15)
In the equation: IFI—the priority of basic farmland delimitation;
Bi—the grade value of the ith evaluation factor;
Ai—the combination weight of the ith evaluation factor.
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Afterwards, accumulate the indexes of all factors for the priority of basic farmland delimitation and figure out
a comprehensive index of every evaluation unit for the priority evaluation of basic farmland delimitation. Next,
determine a grading scheme (Tab.7) for the comprehensive index of the priority of basic farmland delimitation
according to the cumulative frequency curve method or method of equal interval that tells the distribution of the
comprehensive index of the priority of basic farmland delimitation, to calculate the priority of basic farmland
delimitation and make a grade map for the priority of basic farmland delimitation (Fig.2).
6.Conclusion
This research classifies the farmland in the research area into 9 grades according to the priority of basic
farmland delimitation. The land of grade 1 to 6 can be classified into the major components of basic farmland.
The evaluation result can serve for an important reference for the studies on the basic farmland reallocation in
the research area. The research result indicates that AHP method can make a good priority evaluation on the
basic farmland delimitation in the research area.
Table.4 The membership degree and description of conceptual assessment factors system on the priority
of basic farmland delimitation
Topographic
position
Description Wash
land
First
terrace
Second
terrace
High level
terrace
Thick
land Diluvial fan(up, middle, down) Inclined plain Hard land
Loess
hilly
land
Hillside Gully
Membership 0.7 1.0 0.9 0.7 0.4 0.4 0.6 0.8 0.8 0.2 0.2 0.1 0.6
Type of
parent
material
Description Diluvium River alluvium Loess-like
alluvium Saprolite Baode clay Malan loess Lishi loess
Membership 0.7 0.9 1.0 0.2 0.3 0.5 0.6
Texture
configuration
Description Full
loam
Clay
sand
Bottom
sand
Clay
loam
Loamy
sand
Clay
sand
Full
clay
Gravel
soil
Bottom
gravel
Less
gravel
More
gravel
Less lime
concretion
Shallow
lime
concretion
More lime
concretion
Full
sand
Shallow
calcisol
With
chestnut
soil
Chestnut
soil at
the base
Membership 1.0 0.6 0.7 1.0 0.9 0.3 0.6 0.4 0.7 0.8 0.2 0.8 0.4 0.2 0.3 0.4 0.4 0.7
Topsoil
texture
Description Sandy soil Sandy loam Light loam Medium-weight loam Heavy loam Clay
Membership 0.2 0.6 0.8 1.0 0.8 0.4
Level of
terrace
(garden) style
Description
The land is flat and the
level of garden system
is high
The land is basically
flat and the level of
garden system is
relatively high
High-level
terrace
Gentle-slope terrace
The degree of maturation is above 5
years
Newly-built
terrace Slope cropland
Membership 1.0 0.8 0.6 0.4 0.2 0.1
Degree of
salinity
Description
No Light Middle Heavy
Salt
content
Dominated by soda, <0.1% 0.1%-0.3% 0.3%-0.5% ≥0.5%
Dominated by chloride, <0.2% 0.2%-0.4% 0.4%-0.6% ≥0.6%
Dominated by sulfate, <0.3% 0.3%-0.5% 0.5%-0.7% ≥0.7%
Membership 1.0 0.7 0.4 0.1
Probability of
irrigation
Description Fully satisfied Basically satisfied Generally satisfied No irrigation condition
Membership 1.0 0.7 0.4 0.1
Table.5 The classification and membership degree of numerical assessment factors system on the priority of
basic farmland delimitation
Evaluation factor Dimension Grade 1 Grade 2 Grade 3 Grade 4 Grade 5 Grade 6
Magnitude Magnitude Magnitude Magnitude Magnitude Magnitude
Surface slope <2.0 2.0-5.0 5.1-8.0 8.1-15.0 15.1-25.0 ≥25
Availavle soil thickness Cm >150 101-150 76-100 51-75 26-50 ≤25
Topsoil thickness Cm >30 26-30 21-25 16-20 11-15 ≤10
Soil bulk density g/cm3 ≤1.10 1.11-1.20 1.21-1.27 1.28-1.35 1.36-1.42 >1.42
Organic material g/kg >25.0 20.01-25.00 15.01-20.00 10.01-15.00 5.01-10.00 ≤5.00
PH 6.7-0 1-9 8.0-8.5 8.6-9.0 9.1-9.5 ≥9.5
Available phosphorus mg/kg >25.0 20.1-25.0 15.1-20.0 10.1-15.0 5.1-10.0 ≤5.0
Rapidly available potassium mg/kg >200 151-200 101-150 81-100 51-80 ≤50
Probability of irrigation Fully satisfied Basically satisfied Basically satisfied Generally satisfied No irrigation condition
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Table.6 The function of numerical assessment factors system on the priority of basic farmland delimitation
Function type Evaluation factor Empirical equation C Ut
Minor type Surface slope (°) y=1/[1+6.492×10-3×(u-c)2] 3.0 ≥25
Major type Effective soil thickness (cm) y=1/[1+1.118×10-4×(u-c)2] 160.0 ≤25
Major type Topsoil (cm) y=1/[1+4.057×10-3×(u-c)2] 33.8 ≤10
Minor type Soil bulk density (g/cm3) y=1/[1+3.994×(u-c)2] 1.08 ≥1.42
Major type Organic material (g/kg) y=1/[1+2.912×10-3×(u-c)2] 28.4 ≤5.00
Minor type PH y=1/[1+0.5156×(u-c)2] 00 ≥9.50
Major type Available phosphorus (mg/kg) y=1/[1+3.035×10-3×(u-c)2] 28.8 ≤5.00
Major type Rapidly available potassium (mg/kg) y=1/[1+5.389×10-5×(u-c)2] 228.76 ≤50
Table.7 The priority grade standard of basic farmland delimitation
Grade Comprehensive index of production capacity Area (hm2) Area ratio (%)
I ≥0.855 144.41 0.19
I ≥0.84<0.855 10822.14 13.91
II ≥0.825<0.84 11349.75 14.59
IV ≥0.82<0.825 8151.00 10.48
V ≥0.80<0.815 10785.76 13.86
VI ≥0.60<0.80 21355.82 27.44
VII ≥0.42<0.60 3396.26 4.36
VIII ≥0.11<0.42 1994.86 2.56
IX <0.11 9813.2 12.61
Fig.2 The priority grade of basic farmland delimitation
Acknowledgement: This work is supported by the National Natural Science Foundation of China (No. 411711151) and Social
Sciences Federation of Shanxi Province (No. SSKLZDKT2012101)
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