Applied Geography 30 (2010) 271–281
Contents lists available at ScienceDirect
Applied Geography
journal homepage: www.elsevier.com/locate/apgeog
Towards realistic assessment of cultivated land quality
in an ecologically fragile environment: A satellite
imagery-based approach
Yansui Liu a, *, Yanyu Zhang a, b, Liying Guo c
a
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Graduate University of Chinese Academy of Sciences, Beijing 100049, China
c
Institute of Natural Resources and Regional Planning, CAAS, Beijing 100081, China
b
a b s t r a c t
Keywords:
Cultivated land quality
Land potential assessment
Land quality indicators
Remote sensing
Hengshan county
China
It is very difficult to realistically assess cultivated land quality (CLQ) because its contributing factors cannot be accurately quantified. This study aims at overcoming this difficulty
by using information objectively derivable from ETMþ and SPOT images for Hengshan
County, Northwest China. This objective approach is able to yield a comprehensive CLQ
assessment using five proposed indicators. They are slope gradient, proportion of sandy
land, water availability, soil fertility, and land use types organized into three indices of
pressure resistance, land state, and land use response. Therefore, this assessment takes
into consideration topographic setting, land degradation risk, moisture, vegetation
growing condition, and land use response of farmers. The developed CLQ is found to be
significantly correlated with the spatial distribution of water resources, suggesting that
water availability is a decisive factor influencing land productivity. CLQ is also correlated
closely with rural economic level, agricultural infrastructure investment, and the farming
system. The whole County was further classified into three cultivated land use zones based
on the calculated CLQ value. Each zone is best used for different purposes and requires
different strategies of protection. Such assessment outcomes are essential for the
prevention of land degradation and adjustment of agricultural structure to promote
sustainable use of cultivated land.
Ó 2009 Elsevier Ltd. All rights reserved.
Introduction
It is important to realistically assess cultivated land quality (CLQ) as it is related to land productive potential. Timely and
comprehensive assessment and dynamic monitoring of CLQ, defined as the ability to produce grains sustainably, are especially critical in the ecologically vulnerable region because CLQ, vulnerable to environmental change and human economic
activities, can change drastically over a short time. Such information is very useful at revealing the spatiotemporal variation of
land quality, and understanding the mechanism and process of regional environmental change induced by human economic
activities. It can also be used to monitor regional land degradation, optimize allocation of precious land resources, promote
sustainable land management, and serve as the scientific evidences for harmonizing the relationship between humans and
the land. With reliable information on CLQ it is possible to maximize the potential of the land without compromising the
sustainability of land productivity and negatively affecting the ecoenvironment. In addition, the long-term study of cultivated
* Corresponding author. Tel.: þ86 10 64889037; fax: þ86 10 64857065.
E-mail addresses: [email protected] (Y. Liu), [email protected] (Y. Zhang), [email protected] (L. Guo).
0143-6228/$ – see front matter Ó 2009 Elsevier Ltd. All rights reserved.
doi:10.1016/j.apgeog.2009.07.002
272
Y. Liu et al. / Applied Geography 30 (2010) 271–281
land quality and its temporal tracking are conducive to understanding the changing trend of regional arable land quality. It
also enables understanding of the change in variables that affect cultivated land quality. Periodic assessment of land quality is
able to timely identify the impact of human socioeconomic activities on land quality.
The realistic assessment of cultivated land quality relies on the identification of the variables crucial to the sustainable use of
regional land resources. The cultivated land system is a synthesis of natural and socioeconomic factors. Their interaction exerts
an impact on land quality. Natural factors are intrinsic, substantial, and relatively stable while socioeconomic factors are
extrinsic, invisible, and relatively dynamic. With the rapid development in rural social and economic conditions, socioeconomic
factors are playing an increasingly important role in the land resource system. This role is sometimes more important than that
of natural settings. Following the FAO framework for land evaluation and the subsequent literature, socioeconomic factors have
received more emphasis in land quality evaluation (FAO, 1976). So far various indicators have been identified in the literature.
For instance, Dumanski (1997) identified five sets of short-term indicators (nutrient balance, yield trends and variablity, land
use intensity, and use diversity and land cover), three sets of long-term indicators (soil quality, land degradation and agrobiodiversity), as well as water quality, forest land quality, rangeland quality and land contamination/pollution. Xu, Zhao, Liu, and
Wilson (2006) identified five critical soil quality factors as organic matter, texture, phosphorus, porosity, and microstructure.
The attributes that best served as indicators of soil quality were organic matter, hydraulic conductivity, antiscourability, cation
exchange capacity, invertase, mean weight diameter of aggregate, available phosphorous, and mean weight diameter of microaggregates. Land evaluation, which tries to predict land behaviour for each particular use, is not the same as soil quality
assessment as the biological parameters of the soil are not considered by land evaluation (De la Rosa, 2005). Shukla, Lal, and
Ebinger (2004) identified bulk density, water infiltration, aggregate size, and soil nitrogen/carbon, as soil quality indicators.
Zvomuya, Janzen, Larney, and Olson (2008) identified total organic carbon, total inorganic carbon, total nitrogen among the
most important soil quality indicators associated with variation in biomass production. At the regional scale Brejda, Moorman,
Karlen, and Dao (2000) selected total organic carbon and total nitrogen as the most sensitive indicators of soil quality.
It must be noted that an indicator is not useful in an assessment if it cannot be accurately quantified. Thus, the selected
land quality indicators must be reliably quantifiable from replicable and cost-efficient data. This is especially true for rapid,
dynamic CLQ assessment and monitoring at the regional scale. An ideal source of such data is satellite imagery. CLQ
assessment can be made more objective through incorporation of assessment indicators derivable from satellite imagery.
Landsat satellite imagery provides a continuous coverage of the Earth since 1972 (Michael et al., 2008). So far satellite images
have been widely used to acquire land information in the northern Loess Plateau, including monitoring and modeling land
use changes (Chen, Wang, Fu, & Qiu 2001; Fu, et al., 2006; Liu, Wang, & Guo, 2006), analysis of their driving forces (Fu, Chen,
Ma, Zhou, & Wang, 2000; Ostwald & Chen, 2006), and effects of land use and land change on the environment (Chen, Huang,
Gong, Fu & Huang, 2007; Fu, Wang, Chen, & Qiu, 2003; Gao & Liu, 2008; Long, Liu, Wu, & Dong, 2009). However, no
researchers have derived the variables from remotely sensed data for assessment of cultivated land quality. In particular, the
relationship between image spectral information and CLQ assessment indicators has not been explored quantitatively. It is
uncertain what kind of variables that are objectively quantifiable from satellite imagery can be used for the assessment.
The purpose of this study is to build a comprehensive CLQ assessment framework for the transitional zone of sandy desert
and Loess Plateau along the Great Wall. The specific aims are: (1) to identify the most useful socioeconomic indicators in the
assessment by incorporating the input of local farmers; (2) to propose and evaluate the effectiveness of a few indices
objectively derivable from satellite images for the assessment; (3) to establish an economical, easy to implement and timely
effective evaluation framework for assessing CLQ; and (4) to evaluate the current degradation potential of cultivated land in
Hengshan County and to prescribe a number of measures to minimize the environmental risk for lands of a low CLQ and to
maximize the potential for lands of a high CLQ. This assessment is considered objective in that it is based on the use of satellite
images that allow fast and accurate derivation of the assessment indicators related to cultivated land productivity (Liu, Zha, &
Gao, 2004; Zeng, 2007). Besides, the overall influence of both socioeconomic input of the local farmers and natural environmental parameters is taken into account in the assessment.
Study area
The study area is Hengshan County located in northern Shaanxi Province (Fig. 1), which is the most representative county in
the ecologically transitional zone between the Mu Us sandy desert and the Loess Plateau along the Great Wall. This area lies in
the transitional zone from farming to grazing in agriculture. It extends from 108 650 E to 110 020 E and from 37 220 N to 38 740 N,
covering a total area of 428,200 ha. This temperate are has a continental monsoon semi-arid steppe climate. Precipitation is
confined to July–September with a mean annual precipitation of 397 mm and potential evaporation of 2085.5 mm. Thus,
droughts are a very serious problem in this area. Two large rivers, Wuding and Lu, divide the county into two parts. North of the
Wuding River and west of the Lu River is the Mu Us sandy desert that has suffered from serious sandy desertification owing to
the sandy and loess being easily erodible. The Loess Plateau has been severely affected by soil erosion with prevalent deep-cut
gullies. Thus, soil erosion and land desertification co-exist in close proximity to each other in this County. Western and southern
Hengshan County has a higher elevation than its eastern and northern counterpart with an altitude between 890 and 1535 m.
In the ecologically transitional region between the Loess Plateau and the Mu Us desert along the Great Wall, cultivated
land has an inherently low production capacity (Liu, Gao, & Yang, 2003). Land quality is also prone to degradation as a result of
irrational land use practices, such as excessive reclamation of steep land, over-cultivation, and overgrazing. Degradation of the
quality of cultivated land can harm the relationship between humans and the environment, and cause soil erosion and land
Y. Liu et al. / Applied Geography 30 (2010) 271–281
273
Fig. 1. The study area of Hengshan County in northern Shaanxi Province, Northwest China.
desertification. Land degradation has seriously threatened cultivated land production capacity (Gao, Liu, & Chen, 2006; Liu,
Gao, & Yang, 2003; Zha, Liu, & Deng, 2008).
This area has been selected because it faces an unprecedented menace from both natural conditions and human activities.
The pace of industrialization and urbanization in the Loess Plateau catchment area was considerably quickened with the
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implementation of a large-scale development plan for West China in 1998. Northern Shaanxi Province, in which Hengshan
County is located, is being developed into a new strategic energy base. Industrial development and infrastructure projects all
have contributed towards the destruction of vegetation covers and aggravated water resource scarcity. More cultivated land
will be converted to industrial plants and development, drastically reducing land production capacity in western China.
Methodology
Selection of assessment indicators
In this study, the pressure-state-response framework proposed by Dumanski and Pieri (2000) was adopted. The assessment was carried out from three perspectives of resistance to pressure, current state, and land use response. Although
cultivated land productivity in the study area is severely restrained by its natural settings, the cultivated land system still
possesses certain qualities to resist land degradation, which helps to avoid degradation even under a mounting environmental pressure. The pressure resistance index (PRI) is proposed to capture this resistant tendency. A higher pressure
resistance index value denotes a stronger ability to withstand environmental pressure and a lower possibility to be degraded.
The current state reflects the two components of cultivated land quality: current quality and potential productivity in the
foreseeable future according to the current productivity situation under the influence of natural and human factors. This
perspective is represented by land state index (LSI). Land use response refers to future change in land quality, either negative
or positive. A negative change implies degradation while a positive change means just the opposite. This perspective is
represented by land use response index (LURI). It is put forward to predict the future quality under different land use
responses. Each of the three perspectives is considered equally important to CLQ, hence receives the same weight, or
LQI ¼ ½PRI þ LSI þ LURI=3
(1)
Representing the overall quality of the cultivated land resource and integrated economic, ecological and management factors
of land use, LQI is indicative of cultivated land use sustainability. The environmental pressure facing cultivated land in the
study area stems from soil erosion and land desertification. Soil erosion risk is an important factor to consider in complex and
varied terrains (Messing, Fagerström, Chen, & Fu, 2003). This risk is taken into account via slope gradient of the land surface.
The risk of land desertification is considered through proportion of sandy land or proportion of sandy area ratio in a pixel
(SARP), or
PRI ¼ ½SLOPE þ SARP=2
(2)
Because the study area is located in the semi-arid transitional zone between sandy desert and loess plateau, water
availability is the most essential and a decisive productivity factor. Current land state hence refers to soil and vegetation
moisture, and soil fertility. Moisture is studied via Soil and Vegetation Moisture Index (SVMI). Soil fertility is reflective of
vegetation growing conditions, which is studied via modified soil adjusted vegetation index (MSAVI). MSAVI is regarded as
the surrogate for soil fertility. Thus,
LSI ¼ ½SVMI þ MSAVI=2
(3)
Land use activities, embodied mainly in the form of land use types, are farmers’ response to CLQ. In Hengshan County
infertile cultivated land has a low productivity. The quality of cultivated land is changed via different measures, such as more
labor investment, and application of fertilizers. With more investment into the land, cultivated land will become more
productive in the future (Liu, 1999; Smit, Metzger, & Ewert, 2008). Cultivated land use type is thus an indispensable indicator
of CLQ.The five indicators are in broad agreement with the outcome of a survey (through interviews) of 107 households in
2003 and 2004 to generate the appropriate CLQ assessment indicators from local farmers. They identified slope, water
availability, crop yield, and land desertification as the most important factors to CLQ (Zhang, Wang, Shi, & Li, 2006). Therefore,
the selected indicators are practical to the local situation and can reflect the cultivated land productivity realistically.
Derivation of assessment indices
The five selected land quality indicators should be enumerated conveniently, rapidly, stably, and repetitively, if they are of
practical value in the dynamic monitoring of CLQ. The best data source that can meet these requirements is satellite imagery.
The four indicators of slope, proportion of sandy land area, SVMI, and modified soil adjusted vegetation index (MSAVI) were
extracted from satellite images (Fig. 2). Sandy area ratio in a pixel was extracted from an ETMþ image of June 2004 at a spatial
resolution of 15 m using the spectral linear mixing method, and used to evaluate land desertification risk. Slope and
proportion of sandy land within a pixel were integrated and transformed via the inverse value assignment to obtain pressure
resistance index. SVMI and MSAVI were obtained directly from the ETMþ image and were integrated. Land use response index
was obtained from the land use map derived from a SPOT image of June, 2004 at a spatial resolution of 20 m. These satellite
imagery derived indicators are in agreement with the local farmers’ indicators (Ericksen & Ardo’n, 2003). A slope map was
obtained from a digital elevation model constructed from a digital topographic map of 1:50,000.
Y. Liu et al. / Applied Geography 30 (2010) 271–281
275
Fig. 2. Thematic maps of indices extracted from satellite imagery.
In addition, 209 soil samples were collected from the study site and analyzed in the lab to determine soil properties
(Table 1). Organic carbon was determined using the Walkley and Black dichromate method, total nitrogen using the standard
micro-digestion method (Kjeldahl) with colorimetric determination by spectrophotometer, and available P using the Bray and
Kurtz method. For soil water determination, oven drying method was used and for soil bulk density determination, ring
sword method was used.
Table 1
Soil properties and physical properties of various types of cultivated land.
No. of samples
Land types
Organic matter (%)
Total N (%)
AP (mg/kg)
Moisture (%)
Bulk density (g/cm3)
SARP
SVMI
MSAVI
SLOPE
10
53
23
21
102
Paddy fields
Irrigated land
Terraced fields
Dry land
Sloping fields
0.814
0.748
0.559
0.568
0.502
0.062
0.056
0.045
0.035
0.038
7.003
6.046
4.644
5.241
3.761
18.41
9.88
7.13
5.41
6.94
1.424
1.303
1.244
1.310
1.253
24.892
30.474
43.853
45.023
43.674
14.185
8.375
3.248
2.905
3.755
0.405
0.395
0.273
0.269
0.280
6.575
13.212
23.110
21.553
19.836
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Table 2
The indicator system and scores for a particular indicator value in the assessment.
Slope Gradient ( )
Portion of sandy land
Soil/vegetation
moisture index
Modified soil
adjusted vegetation index
Value
Score
Value
Score
Value
Score
Value
Score
<6
6–10
10–25
25–35
>35
100
100–75
75–45
45–15
<15
0–30
30–50
50–70
70–90
90
100–75
75–60
60–30
1–30
1
6
6 to 2
2 to 6
6–14
14
1
1–25
25–60
60–98
98–100
0.12
0.12–0.23
0.23–0.33
0.33–0.42
>0.42
1
1–25
25–80
80–98
98–100
Indicator grading and scoring
Based on the joint consideration of the above information and the expert knowledge about the local conditions, all
indicators or indices were graded into five categories (Table 2). A score within the range of [0,100] was assigned to an indicator
or index via linear interpolation (Kalogirou, 2002). Of particular notice is that the proportion of sandy area within a pixel and
slope were assigned a score inversely. Namely, a smaller observed value receives a larger score. In this way, the influence of
the external environment is converted into the ability of cultivated land to resist land degradation. For instance, cultivated
land on a steep terrain faces a higher risk of land degradation, and hence its ability to resist degradation is weaker. A
correspondingly smaller score is assigned to it.
A cultivated land use map was produced from the SPOT image through a combination of computer-assisted classification
and visual interpretation based on personal experience (Fig. 3). Cultivated land is classified into six types of paddy fields,
vegetable plots, irrigated land, terraced fields, sloping fields, and dry land. According to a 2005 investigation, local farmers
ranked cultivated land quality as vegetable plots being better than paddy fields, followed by irrigated land, terraced fields,
sloping fields, and dry land. This sequence has been verified by their soil nutrients disparity (Zhang, Wang, Shi, & Li 2006).
On the basis of the quantitative analysis method by Liebig and Doran (1999), the land use response index was obtained by
scoring dry land as 40, sloping fields 50, terraced fields 60, irrigated land 70, paddy fields 80, and vegetable plots 100,
accordingly.
Fig. 3. Spatial distribution of cultivated land use types.
Y. Liu et al. / Applied Geography 30 (2010) 271–281
277
Results
Quality of assessment indicators
The relevance of all the selected indicators to cultivated land productivity is verified through their correlation with soil
properties, such as organic matter, total nitrogen, available phosphorus, soil moisture content and bulk density, all of which
determine cultivated land fertility and productivity. The average of slope, proportion of sandy land within a pixel, SVMI, and
MSAVI was calculated for each type of cultivated land by grid value statistics for paddy fields, irrigated land, terraced fields,
dry land and sloping fields, with vegetable plots excluded because of their negligible presence in the study area.
Correlation analysis of CLQ with 12 socioeconomic factors obtained from the 2002 statistical data of the local government
is conducive to revelation of the effects of socioeconomic development on CLQ quality change, according to which social,
economic and management measures should be undertaken to adjust land use activities and promote sustainable land use.
The mean indicator values were correlated with soil properties of different cultivated land use types. The derived correlation
coefficients (Table 3) show that there is a strong correlation between the selected indicators and soil properties. Specifically,
there is a significant negative correlation between portion of sandy area within a pixel and organic matter, total nitrogen,
available phosphorus, and soil moisture content. Also, there is a significant positive correlation between slope and organic
matter, total nitrogen and soil moisture content. This demonstrates that the two indicators can be used to study cultivated
land degradation. Conversely, a significant positive correlation exists between SVMI and soil moisture content. This means
that SVMI can competently reflect water availability of cultivated land. Moreover, MSAVI bears a significant positive correlation with the soil fertility indicators (e.g., organic matter and total nitrogen), indicating that MSAVI is a competent reflector
of cultivated land fertility. Soil properties obviously vary with land use types. Therefore, all the five proposed indicators can be
used to distinguish fertility levels of cultivated land use types. They are all suited to replace traditional ground-based
assessment indicators in building the CLQ assessment indicator system based on satellite images.
Land use and cultivated land quality
Due to serious sandy desertification, there is more cultivated land to the south of the Wuding River and to the east of the Lu
River than elsewhere except for a small area along the river tributaries. Paddy fields, vegetable plots, and irrigated land are
located mainly along the Wuding and Lu Rivers and their tributaries that provide a favorable condition for irrigation. By
comparison, terraced fields, sloping fields, and dry land are found in the opposite settings. They are dispersed widely to the
south of the Wuding River. The land with good water availability makes up 12.15% of the cultivated land. By comparison,
87.85% of the total cultivated land is terraced land that has with some water conservation facilities, and relative flat dry land,
or sloping fields with a surface over 15 . This relative proportion indicates a severe water shortage for agricultural production.
The assessment results of cultivated land were grouped into five classes (excellent, good, fair, poor and deteriorated) using
unsupervised classification (Table 4). Listed in this table are the acreage and relative percentage of each type of cultivated
land. According to this table, nearly three quarters of cultivated land has a poor quality state with land state index below 60.
Analysis of the PRI spatial pattern shows that excellently cultivated land is distributed mainly along the rivers or near the
reservoir. Good cultivated land with irrigation is found mainly in the central County. Fair cultivated land spreads evenly
throughout the whole County. Always situating at a high altitude, poorly cultivated land has suffered from mild desertification. Deteriorated cultivated land is mainly distributed in the southern tableland where water availability is the scarcest. In
fact, the land quality state bears a unique correlation with the spatial distribution of water, suggesting that water resources
are its primary influencing factor. Tableland in southern Hengshan encounters double environmental pressures from both soil
erosion and sandy desertification. Consequently, cultivated land located in this area faces the highest degradation risk. Land
along the rivers, near the reservoir and irrigation stations all has a gentle topography and good water availability, hence is the
least likely to be degraded. Other lands are either encroached by soil erosion or affected by land desertification, or both. As
a whole, cultivated land in the study area faces a large pressure with nearly two thirds of the land having an enormous risk of
being degraded. This situation deserves more attention of protection via the adoption of appropriate measures.
The land quality index shows that the deteriorated land is distributed in the South far from the Rivers. Excellent land
spreads out along the Rivers and has a relatively dense concentration around the Hengshan Town, capital of Hengshan
County. Good land is radiative from the Hengshan Town and Yuhemao Town, both being a trading centre. Fair land and poor
Table 3
Correlation coefficients between selected assessment indicators and soil properties.
Indicators
SLOPE
SARP
SVMI
MSAVI
Organic matter
Total nitrogen
Available phosphorus
Soil moisture
%
%
mg/kg
%
g/cm3
0.928*
0.973**
0.936*
0.963**
0.888*
0.958*
0.921*
0.941*
0.849
0.882*
0.871
0.853
0.941*
0.916*
0.979**
0.837
0.877
0.813
0.89*
0.737
**Correlation is significant at the 0.01 level; *Correlation is significant at the 0.05 level.
Bulk density
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Y. Liu et al. / Applied Geography 30 (2010) 271–281
Table 4
CLQ classification based on different aspects of land quality.
Class
Deteriorated
Poor
Average
Good
Excellent
PRI
LSI
LURI
LQI
Score
Area (ha)
Percent (%)
Score
Area (ha)
Percent (%)
Score
Area (ha)
Percent (%)
Score
Area (ha)
Percent (%)
1–30
30–45
45–60
60–80
80–100
26212.3
18950.8
33155.6
25716.2
17403.9
21.58
15.61
27.30
21.18
14.33
1–30
30–40
40–60
60–80
80–100
42425.9
16499.8
31018.8
21982.1
9512.4
34.94
13.59
25.54
18.10
7.83
40
50
60
70
100
81034.4
10384.1
15267.6
10487.9
4264.9
66.73
8.55
12.57
8.64
3.51
1–25
25–40
40–50
50–60
60–100
11549.6
34935.8
45653.3
10637.7
18662.5
9.51
28.77
37.59
8.76
15.37
land spread extensively all over the whole County, but have a relatively high concentration in the north for fair land, in the
south for poor land. Quantitative statistics demonstrate that excellent and good lands combined account for less than
a quarter of the total area, but nearly 40% lands have a poor quality.
Quality of socioeconomic variables
As illustrated in Table 5, the four indices are all positively correlated with grain yield, further proving their suitability for
representing cultivated land productivity. The significant negative correlation between proportion of dry land and the four
CLQ indices affirms the aforementioned fact that droughts are the most serious problem to agricultural production in the
study area. Dry land has a poor irrigation condition. Also, the proportion of dry land has the closest correlation with land use
response index and land quality index. This means that water availability exerts the most significant effects on land use
activities and the overall cultivated land productivity. Cultivated land having a higher pressure resistance index, land state
index, land use response index and land quality index value corresponds to a higher grain per capita, indicating that good
quality land has a sound ability to guarantee food self-sufficiency at present. However, CLQ indices are significantly, negatively
correlated with cultivated land per capita (Table 5). This is because good quality land attracts more people to settle down,
leading to a lower cultivated land per capita. In order to produce a higher yield from a limited amount of cultivated land,
agricultural land use will be intensified in the future. The continued increase in population will have a negative effect on CLQ,
as indicated by the negative correlation between CLQ and population density. Proper control of population growth is
necessary in the area having a high CLQ.
Correlation analysis also demonstrates that land use activities exert a significant effect on CLQ. More investment improves
land quality, judging from the significant positive correlation between investment per hectare and CLQ indices. Also, the ratio
of total rural expenditure to total income has a significant positive correlation with CLQ. In the study area most of the
expenditure is spent on constructing farmland infrastructure (e.g., roads, water conservancy facilities and terrace). Such
investments do not bring short-term benefits. Thus, a higher ratio of total rural expenditure to total income represents
a larger investment in farmland infrastructure. Obviously, capital investment can bring a significant improvement to CLQ.
Given the general low CLQ at present, an increase in capital investment is fundamental to farmland improvement. Additionally, afforestation holds a positive correlation with CLQ because forest decreases the risk of cultivated land degradation.
Application of organic fertilizers to land of a low fertility ameliorates the land properties. This explains the significant positive
correlation between the populations of live pigs and CLQ. In summary, increases in capital and organic fertilizer input, and
rehabilitation of the physical environment of farmland are the three important measures to improve CLQ.
Table 5
Correlation coefficients between CLQ indices and socioeconomic indicators based on mean value of PRI, LSI, LURI, and LQI in each township.
Indicators
PRI
LSI
LURI
LQI
Grain yield (kg/ha)
Proportion of dry land (%)
Grain per capita (kg)
Cultivated land
per capita (ha./person)
Population density (persons/km2)
Input per ha.(yuan)
Rural output/input ratio
Afforested area
in same
year (ha)
Numbers of live pigs
Proportion of cash
crops (%)
Per capita
rural income (yuan)
Agricultural income ratio
0.79**
0.79**
0.81**
0.60**
0.85**
0.87**
0.85**
0.64**
0.86**
0.90**
0.87**
0.66**
0.88**
0.90**
0.89**
0.64**
0.23
0.62**
0.57*
0.75**
0.12
0.73**
0.58*
0.57*
0.25
0.76**
0.60**
0.69**
0.24
0.74**
0.63**
0.72**
0.68**
0.32
0.53*
0.53*
0.53*
0.57*
0.62**
0.49*
0.87**
0.66**
0.66**
0.78**
0.60**
0.62**
0.65**
0.66**
**Correlation is significant at the 0.01 level (2-tailed); *Correlation is significant at the 0.05 level (2-tailed).
Y. Liu et al. / Applied Geography 30 (2010) 271–281
279
Cultivated land of a good quality is always associated with cash crops, so the proportion of cash crop acreage bears
a positive correlation with CLQ. Cash crops are able to bring a high economic return than grains. Thus, productive cultivated
land is companied by a higher per capita income for farmers. Agriculture is well developed in areas of a good CLQ as agricultural development helps to promote CLQ. In these areas industrial development should be restricted to a certain extent in
order to avoid damage to the ecosystem and the cultivated land resources system.
CLQ zoning and protection
Cultivated land in Hengshan County was classified into three zones based on the CLQ assessment results, taking into account
the regional natural and socioeconomic settings (Fig. 4). They are oasis characteristic agricultural zone, riverine high-efficiency
agricultural zone, and high dry land farming zone. Given the geographical differences over the study area, prevention of cultivated
land degradation and reduction of local poverty should be featured prominently in promoting sustainable use of land resources.
Strengthening the construction of water and soil conservation facilities and the windbreak system can prevent erosion and
desertification of cultivated land. Due to the strong correlation between CLQ and socioeconomic variables, agricultural structure
should be adjusted to enhance the regional agricultural competitiveness and further increase farmers’ income so as to reduce
poverty-induced irrational land use and boost protection of the cultivated land resources.
Oasis characteristic agricultural zone
The oasis characteristic agricultural zone is located in northern and western Hengshan County, with a smaller amount of
cultivated land mostly along the Wuding River and its tributaries. Cultivated land in this zone has a high productivity owing to good
irrigation, sufficient sunlight, a favorable temperature, and flat terrain. Oasis is the main form of agricultural production in this zone.
Efforts should be directed at expanding oasis characteristic agricultural production in future development. Desertification in the
vicinity of the oasis is the most serious threat to cultivated land quality, and prevention of arable land from desertification should be
the top priority in land management.
Irrational land use activities around the oasis should be strictly controlled to restore the self-rehabilitating function of the
ecosystem. Ecological projects should include construction and maintenance of the windbreak system through afforestation. Sandy
land should be protected for vegetation growth, and to prevent dune encroachment. Cultivated land showing signs of desertification should be reverted to forest and grassland to prevent further deterioration in land quality (Long et al., 2006).
The current agricultural production structure based solely on grain cultivation should be diversified to take advantage of the
excellent natural environment. Competitive rural industries should be developed to make this zone an important base of fruits
and flowers. Moreover, the threat of droughts to CLQ can be reduced through development of a water-efficient agricultural
production system, use of water-saving techniques in nearby industrial areas, and implementation of an industrial water recycling
and residential water-saving scheme.
Fig. 4. Zoning of cultivated land resources in Hengshan County (OCAZ-oasis characteristic agricultural zone; RHAZ-riverine high-efficiency agricultural zone;
DHFZ-dry highland farming zone).
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Y. Liu et al. / Applied Geography 30 (2010) 271–281
Riverine high-efficiency agricultural zone
The riverine high-efficiency agricultural zone spreads out along the Wuding and Lu Rivers where paddy fields and irrigated
land are mainly distributed. Generally, this zone has a relatively good CLQ thanks to sufficient water availability, flat terrain, and
advantageous location (e.g., adjacency to the Hengshan town and Yuhemao town). Owing to such a good economic development
environment, the core strategy for this zone should be development of highly efficient agriculture to maximize the return of the
cultivated land resources. Most cultivated land in this zone should be demarcated as the primary farmland protection areas and
hence are subject to the stringent control according to the general land use plan. Land reclamation should be strengthened to
expand cultivated land area. CLQ may be improved by increasing investment into agricultural production infrastructure,
improving unproductive arable land, and boosting the yield of primary productive farmland.
The highly favorable conditions in this zone mean that it is best developed as an eco-agriculture base, specializing in highquality rice and vegetables. In addition, special animal husbandry should be promoted by enclosing the hillsides for livestock
grazing. The scale of production is increased through pan feeding pigs and cattle. The aim is to develop an eco-friendly animal
husbandry base with cashmere goats as the representative.
High dryland farming zone
Located in the central and southern County, the high dryland farming zone is a typical Loess Plateau hilly region, with deep
gullies where it is very difficult to construct and manage any irrigation channels. Dry land is the main form of cultivated land use
type because of the dry climate, strong winds and an inadequacy of sunshine and water availability. The crops widely cultivated
here are low yield and drought-resistant grains such as millet and sorghum. They bring a low economic return that stimulates
further reclamation of slope land to improve economic predicament. This zone is highly vulnerable to erosion due to concentrated
rainfall, highly erodible sandy and loess soil, and a sparse vegetative cover over steep slope land. The core development strategies
should be conservation of soil and water resources and dry land farming through investment into agricultural infrastructure, such
as water conservancy facilities, reservoirs, terraced fields. Reclaimed farmland should be reverted to forestry. Thirdly, dry land
farming technologies should be promoted urgently to boost dry land output. Advanced water-saving technologies, such as drip
irrigation, sprinkler irrigation, and drought-resistant cash crops (e.g., high-quality beans) should be introduced. Comprehensive
measures, including engineering projects, biotechnologies and management should be adapted to promote sustainable farming of
dry land.
Conclusions
This paper establishes a proper framework for objective CLQ assessment from the perspective of pressure, state, and response;
they are represented by three indices of pressure resistance index, land state index and land use response index. Four indicators of
slope, portion of sandy area within a pixel, SVMI, and MSAVI that are objectively derivable from satellite images are proposed for
CLQ assessment. These indices are found to be reliable reflectors of, respectively, soil erosion risk, land desertification, water
availability, and vegetative cover, the most important concerns to local farmers as revealed in a ground survey. The results show
that pressure resistance index, land state index, and land quality index all bear a significant correlation with grain yield, and can
reliably reflect CLQ from different perspectives. It is feasible to use them to assess CLQ in the transitional region of sandy desert and
loess plateau along the Great Wall in northwestern China. They produce a comprehensive evaluation outcome based on pressure,
state, and response. Their combination led to the land quality index that provides a holistic depiction of the overall cultivated land
quality from multiple perspectives. The constructed CLQ assessment framework is able to reflect the present land quality, the
effects of environmental pressure on it, and land use response to different land use types.
The assessment results demonstrate that the CLQ value is generally low for the whole Hengshan County. This value has
a significant correlation with the spatial distribution of water resources, suggesting that water availability is one of the most
important influencing factors. Two-thirds of the cultivated lands face a serious land degradation risk, especially in the mountainous South where soil erosion and land desertification co-exist. Accordingly, corresponding measures should be adopted to
avoid land degradation in this part.
CLQ is significantly correlated with socioeconomic factors, such as the rural economic level, investment into agricultural
infrastructure, and farming system. CLQ improvement and promotion of sustainable land use require adjustment in these three
aspects. The cultivated land system was classified into three zones of oasis characteristic agricultural zone, riverine high-efficiency
agricultural zone, and highland dry farming zone, each zone requiring different measures to protect the cultivated land resources.
The two most important ones to promote sustainable use of cultivated land are prevention of land degradation and adjustment of
agricultural structure. These results are valuable in theoretically analyzing land use change and remedying the decision making of
human economic activities. This study makes a significant contribution towards understanding of how to rapidly and accurately
assess and monitor CLQ over a large geographic area.
Acknowledgments
This research was supported by the National Natural Science Foundation of China (grant numbers 40871257 and
40635029), the National Basic Research Program of China (No. 2006CB400505), and the Knowledge Innovation Program of
Chinese Academy of Sciences (grant number KSCX-YW-09).
Y. Liu et al. / Applied Geography 30 (2010) 271–281
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