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: 13787838@qq.com

* Corresponding author. E-mail address: 13787838@qq.com (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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