Ecosystem Services and Ecological Risks of Land Resource

Spatiotemporal Variation of Cultivated Land Security and Its Drivers: The Case of Yingtan City, China

  • KUANG Lihua ,
  • YE Yingcong ,
  • GUO Xi ,
  • XIE Wen ,
  • ZHAO Xiaomin , *
  • Key laboratory of Poyang Lake Watershed Agricultural Resources and Ecology of Jiangxi Province, Jiangxi Agricultural University, Nanchang 330045, China
*: ZHAO Xiaomin, E-mail:

Received date: 2020-10-15

  Accepted date: 2020-12-26

  Online published: 2021-05-30

Supported by

The Research Project of Humanities and Social Sciences in Colleges and Universities of Jiangxi Province(JC19221)

The National Natural Science Foundation of China(41361049)

The Ganpo “555” Talent Research Funds of Jiangxi Province, China(201295)


Maintaining an adequate security level of cultivated land is essential for the healthy and sustainable survival of China’s large and growing population. We constructed a cultivated land security evaluation index system, combined with an improved TOPSIS method by taking into account the balance and stability of quantitative, qualitative, and ecological security. We applied this improved method to an evaluation of the state of cultivated land security and analyzed its spatiotemporal variation in Yingtan City (Jiangxi Province, China) from 1995 to 2015. The drivers of the changes in cultivated land security were investigated via a spatial regression model, which can eliminate the effect of spatial autocorrelation. The results showed that cultivated land security decreased rapidly from 1995 to 2005, although it tended to rise slowly in the subsequent period from 2005 to 2015. Areas deemed to be in a highly dangerous state were mainly distributed in the Yuehu District, while those that were secure appeared primarily in the southern mountainous area, with the area in a generally dangerous state extending to the west in the same direction as urban development. Among the examined drivers, social-economic factors and policy factors significantly influenced the cultivated land security. Our work suggests that government managers should take appropriate measures to improve cultivated land security according to its spatiotemporal variations and the underpinning drivers in this region.

Cite this article

KUANG Lihua , YE Yingcong , GUO Xi , XIE Wen , ZHAO Xiaomin . Spatiotemporal Variation of Cultivated Land Security and Its Drivers: The Case of Yingtan City, China[J]. Journal of Resources and Ecology, 2021 , 12(2) : 280 -291 . DOI: 10.5814/j.issn.1674-764x.2021.02.014

1 Introduction

The on-going increases in population and consumption are placing unprecedented demands on agriculture and natural resources (Foley et al., 2011). The global population will be expanded by two to three billion between 2015 and 2050, so agricultural production must increase substantially to meet this rising food demand (Zhang et al., 2015). Cultivated land is the key material base to ensure food security (Wang et al., 2012), and this connection is particularly pronounced in China because of its very large population and limited per capita resources (Lichtenberg and Ding, 2008; Tan et al. 2009; Yu et al., 2011; Song et al., 2015). Since China’s reform and opening-up, a series of severe and challenging problems have emerged during the course of its rapid urbanization and industrialization, such as a sharp decline in the quantity and quality of cultivated land accompanied by a degraded ecological environment (Bradbury et al., 1996; Islam and Hassn, 2013; Liu and Guo, 2015). The problem of cultivated land security is becoming more and more serious, and poses tremendous threats to China’s food security and sustainable social development (Zhao and Ma, 2014; Deng et al., 2017). Not surprisingly, this issue of cultivated land security is of great concern to society, and is currently a hot topic for research (Takoutsing et al., 2016; Shi et al., 2019).
The government of China has been particularly concerned about cultivated land protection (Liu et al., 2019). Despite the implementation of many protective measures, the government of China has failed to prevent the loss of cultivated land over the past 20 years (Tan et al., 2009; Song, 2014). Cultivated land has been mainly converted to construction land, especially for economic development zones, transportation networks, and rural residential areas (Song, 2014; Liu et al., 2015; Cheng et al., 2017). Human activities not only impact the quality of cultivated land indirectly, but they also directly change the crop yield, thereby further influencing future food security and sustainable agricultural development (Foley et al., 2005; Liu et al., 2010). China’s average cultivated land quality is currently low, as the cultivated land with high quality represents just 33% of the total cultivated land area, and its yield that more than 15000 kg ha-1 amounts to only 6.09% (Agricultural Land Grading Report, 2015). The policy of occupation and balance of cultivated land ensures that the net area of cultivated land is maintained (Liu et al., 2019), however, most of the occupied land is high-quality cultivated land whereas its replacement consists of land with poor infrastructure conditions and low soil quality (Cumming et al., 2014; Song et al., 2015; Li et al., 2017a; Chen et al., 2018). Under the pressures of scale effects and marginal benefits, the likelihood that farmers will abandon their cultivated lands is increased (Li et al., 2017b; Liu et al., 2017; Wang et al., 2020).
The pollution of cultivated land in China is now widespread. The excessive use of pesticides and fertilizers, land film residues, and industrial sewage discharge have together substantially impaired the resilience of cultivated land soil and its ability to adjust to inputs of water, fertilizer, gas, and heat (Sun et al., 2012; Wang et al., 2018). Approximately 30% of cultivated land in China is damaged by soil erosion (Gao et al., 2016) and 20% is polluted with industrial waste and pesticide residues (Yu and Zheng, 2003). The current changes in the quantity, quality, and ecology of cultivated land could directly lead to changes in the level of cultivated land security.
Cultivated land security refers to a stable state of cultivated land when the drivers reach a balance in the processes of material circulation and energy transformation. Under the interactions of various drivers, cultivated land achieves stable quantity, good quality, and ecologically harmonious development. Thus, the effective supply of cultivated land resources can meet the demands of economic development, population growth, resource and environmental protection, and achieving the “trinity” of cultivated land quantitative, qualitative, and ecological security. Among the three key aspects shaping cultivated land security, quantity is the cornerstone, and a sufficient quantity of cultivated land could meet the great food demand of China’s population of 1.3 billion (Zhang et al., 2018). Quality is its essence, and if maintained at a high enough level, the cultivated land can provide many kinds of safe products. Ecology is its guarantee, in that the quality of cultivated land persists in a good ecological environment, and produces healthy products (Xie et al., 2015; Xie et al., 2017). When taken together, cultivated land in a truly secure state necessarily fulfills the triple criteria of quantity, quality, and ecology. However, most researchers today still mainly focus on one aspect of cultivated land security (Niu et al., 2011; Liu et al., 2014; Bernués et al., 2016; Takoutsing et al., 2016), ignoring the integration of quantitative, qualitative, and ecological security in cultivated land. As such, cultivated land security has generally been overestimated (Song et al., 2011).
In 2017, the Chinese government released Opinions of the State Council of the CPC Central Committee on Strengthening the Protection of Cultivated Land and Improving the Balance between Occupation and Compensation. In particular, this governmental document emphasized that we should increase our effort to strengthen the simultaneous protection of the quantity, quality, and ecology (as a “trinity”) of our cultivated land. Here, we selected the years 1995 to 2015 for our assessment, since this corresponds to the most dramatic period of economic development and cultivated land change in the selected study site: Yingtan City, China. The objectives of this research were to: 1) consider the quantity, quality, and ecology in order to evaluate the cultivated land security in Yingtan City from 1995 to 2015 comprehensively; 2) explore the spatiotemporal variation of cultivated land security; and 3) identify and analyze its drivers to provide a robust empirical reference for the rational utilization, protection, and management of cultivated land.

2 Materials and methods

2.1 Study region

Yingtan City is located in the northeast of Jiangxi Province, and corresponds to the middle and lower reaches of Xinjiang River (27°35'-28°41'N, 116°41'-117°30'E), which is a key channel connecting the southeastern coastal areas with central China. The total area of the city is 3556.7 km2, covering Yuehu District, Guixi City, and Yujiang County, and including 37 township units. In terms of its terrain, Yingtan City is high in the southeast but low in the northwest (see Fig. 1). It has a mild subtropical humid monsoon climate and the annual rainfall is 1750 mm. Since 1995, Yingtan’s urban population has increased by 29.71%, while its GDP increased by 61.09 billion yuan.
From 2005 to 2015, the area of cultivated land occupied by construction in Yingtan City totaled 2239.00 ha. The adjustment of the agricultural land structure occupied another 949.13 ha of the cultivated land. The cultivated land in Yingtan City is mainly affected by industrial wastes and agricultural chemicals. The three forms of industrial wastes (gas, water, and residue) in the industrial zones endanger the ecological environment of the surrounding cultivated land, while the excessive use of pesticides and fertilizers threatens the soil of the cultivated lands. The metal smelting industry in Yingtan City leads to heavy metal pollution of the cultivated land as well (Liu et al., 2018; Zhou et al., 2018).
Fig. 1 Elevation and administrative divisions of Yingtan City Note: 1. Chengqu; 2. Bailu; 3. Tongjia; 4. Xiabu; 5. Guixi subdistrict Community; 6. Binjiang; 7. Baitian; 8. Erkou; 9. Hetan; 10. Hongtan; 11. Jintan; 12. Leixi; 13. Lengshui; 14. Liukou; 15. Longhushan; 16. Luohe; 17. Pengwan; 18. Shangqing; 19. Sili; 20. Tangwan; 21. Wenfang; 22. Tianlu; 23. Zhangping; 24. Zhiguang; 25. Zhoufang; 26. Chuntao; 27. Dengbu; 28. Huaqiao; 29. Huangzhuang; 30. Huangxi; 31. Jinjiang; 32. Liujiazhan; 33. Maqun; 34. Pingding; 35. Yangxi; 36. Zhongtong; 37. Honghu.

2.2 Data sources

We collected data on Yingtan City for two periods (the first period is 1995-2005, and the second period is 2005-2015).
(1) Socio-economic data: City-level data for 1995, 2005, and 2015 were obtained from the Yingtan City statistical yearbook. Township-level data came from the surveys of administrative departments and towns of Yingtan City conducted from July to August, 2017.
(2) Natural geographical data: Cultivated land quality data came from the land evaluation and annually updated database of cultivated land quality in Yingtan City. Digital elevation model (DEM) data were generated with ArcGIS 10.2 (ESRI Inc., Redlands, USA) using a 1:250 000 contour map of Jiangxi Province. Heavy metal data came from sampling and testing done by the Jiangxi Provincial Department of Agriculture, China; however, this set was missing data for 1995 so we estimated it by referring to available research results of Yingtan City for 2005, and then applied that data to Yingtan City as whole via the spatial interpolation method of Gauss Kruger. Average annual rainfall and annual accumulated temperature data were obtained from the Resource and Environmental Science Data Center of the Chinese Academy of Sciences (
(3) Land use data: Data for standard land use categories were obtained through an interpretation of Landsat 4-5 TM and Landsat 8 OLI remote sensing imagery (Geospatial Data Cloud:, provided by the Chinese Academy of Sciences and Google Earth:

2.3 Evaluation index system

Cultivated land is influenced by a combination of natural, economic, and social systems (Niu et al., 2011), and its attributes can change in either an improving or declining direction under the combined actions of the various subsystems and factors involved. The quantitative security of cultivated land is a prerequisite for the basic survival and sustainable development of human society, both of which are indispensable conditions that must be met simultaneously. The qualitative security of cultivated land expresses its natural quality and production conditions. The ecological security of cultivated land indicates how secure the agricultural production processes are, and these processes are affected by both the natural environment and human activities.
After a comprehensive analysis, factors were preliminarily selected for use in the evaluation of cultivated land security. These preliminary indicators were selected based on the scoring opinions of experts. These experts included researchers from universities, the managers from the departments of local government, and the representatives of farmers. Finally, the cultivated land security evaluation index for Yingtan City was constructed (Table 1).

2.4 Security evaluation method

The Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) is an analytical method suitable for comparing and selecting multiple schemes based on multiple indicators (Yoon and Kim, 2017). In recent years, TOPSIS has achieved highly respected research results in the field of land evaluation (Hong et al., 2015; Bagherzadeh and Gholizadeh, 2016). Importantly, using the TOPSIS method is suitable for a systematic analysis of the existing gap between cultivated land security and its ideal state, thus truly capturing the problems and barriers to achieving that ideal state in practice.
Developing an improved TOPSIS method for evaluating cultivated land security involved six major steps:
(1) Selecting the evaluation unit. The size of the evaluation unit could directly influence the accuracy and precision of the results (Chen and Cai, 2010). Our source data included natural geographical data and socio-economic data, which clearly differed in representativeness and scale. Previous work has shown that the surface roughness features
Table 1 Detailed structure of the comprehensive index system designed to evaluate cultivated land security
Criteria layer Indicator layer Indicator description Weight
Cultivated land retention (n1) The area of cultivated land in the year / the area of
cultivated land in 2015
Per capita cultivated area (n2) Cultivated land area / population (ha per person) 0.3186
Cultivated land supplement coefficient (n3) Increased area of cultivated land in the same year /
reduced area of cultivated land
Qualitative security Natural qualities Effective soil thickness (n4) Reflecting the depth of soil effective tillage 0.0817
Soil texture (n5) Reflecting soil physical properties 0.0686
Soil pH (n6) Reflecting the acidity and alkalinity of cultivated soil 0.0883
Soil organic matter (n7) Reflecting the soil fertility level of cultivated land 0.1972
Farming conditions Irrigation guarantee rate (n8) Reflecting the ability of irrigation and water conservancy 0.1750
Drainage condition (n9) Reflecting the ability of arable land to drain water 0.1051
Plot flatness (n10) Reflecting the steepness of the cultivated land surface 0.0431
Road density (n11) Reflecting road accessibility, Road length / spot area (m ha‒1) 0.1606
Cultivated land concentration (n12) The concentration of cultivated land in space 0.0804
Ecological security Forest coverage (n13) Forest area / total land area 0.3381
Proportion of soil erosion area of cultivated land (n14) Soil erosion area / total cultivated area 0.1362
Fertilizer load per unit area (n15) Fertilizer application rate / cultivated area (kg ha‒1) 0.1957
Pesticide load per unit area (n16) Pesticide application rate / cultivated area (kg ha‒1) 0.1587
Heavy metal pollution (n17) Reflecting the degree of damage by heavy metals 0.1713
become more significant under simulated scales of 250 m and 500 m (Zhang et al., 2006). So we chose to use a 250 m × 250 m grid network unit in this study.
(2) Determining the indicator system. First, we standardized the indicators to obtain index values of 0-1, and then determined their respective weights by using the techniques of analytic hierarchy process and entropy weighting (Chen et al., 2014). We used the averaged index weights of both methods, as they not only utilize objective information but also meet the subjective will of decision makers, so this approach provides more reasonable and reliable index weights.
(3) Building a standardized decision matrix. The quantitative security index (IN), qualitative security index (IQ), and ecological security index (IE) of cultivated land, for each year of 1995, 2005, and 2015 in Yingtan City, were calculated by using the multi-factor weighted sum method as follows:
${{I}_{N}}=\sum\limits_{n=1}^{3}{{{{{x}'}}_{mn}}}\times {{w}_{n}}\ \ (m=1,2,3)$
${{I}_{Q}}=\sum\limits_{n=4}^{12}{{{{{x}'}}_{mn}}}\times {{w}_{n}}\ \ (m=1,2,3)$
${{I}_{E}}=\sum\limits_{n=13}^{17}{{{{{x}'}}_{mn}}}\times {{w}_{n}}\ \ (m=1,2,3)$
where m = 1, 2, and 3, represent the years 1995, 2005, and 2015 respectively; x'nm is the standardized value of the index n in the year m; and wn is the weight of the index n.
According to the “trinity” principle of the quantity, quality, and ecology of cultivated land, the security of each component is of equal importance to the overall cultivated land security. Therefore, the calculated IN, IQ, and IE of cultivated land directly constitute the normalized matrix Ii×j:
${{I}_{i\times j}}\text{=}\left( \begin{matrix} {{I}_{1N}} & {{I}_{1Q}} & {{I}_{1E}} \\ {{I}_{2N}} & {{I}_{2Q}} & {{I}_{2E}} \\ \cdots & \cdots & \cdots \\ {{I}_{rN}} & {{I}_{rQ}} & {{I}_{rE}} \\ \end{matrix} \right)$
where Iij is the jth security index of the ith evaluation unit, i = 1, 2, …, r; and j = 1, 2, and 3, represent the IN, IQ, and IE, respectively.
(4) Determining the ideal solutions ${{Z}^{+}}$ and ${{Z}^{-}}$. The positive ideal solution, ${{Z}^{+}}$, is conveyed by the best value for the jth attribute among the i alternatives; and conversely, the negative ideal solution, ${{Z}^{-}}$, consists of the worst value for the jth attribute among the i alternatives.
${{Z}^{+}}=(I_{iN}^{+},\ I_{iQ}^{+},\ I_{iE}^{+})=(Z_{N}^{+},\ Z_{Q}^{+},\ Z_{E}^{+})$
${{Z}^{-}}=(I_{iN}^{-},\ I_{iQ}^{-},\ I_{iE}^{-})=(Z_{N}^{-},\ Z_{Q}^{-},\ Z_{E}^{-})$
(5) Calculating the distances D+ and D- to the ideal solution. The TOPSIS approach measures the Euclidean distances between alternative Ij and ${{Z}^{+}}$, which is denoted by Di+, and likewise between Ij and ${{Z}^{-}}$, which corresponds to Di-.
$D_{i}^{+}\text{=}\sqrt{\sum\limits_{\text{j}=1}^{3}{{{({{I}_{ij}}-{{Z}_{j}}^{+})}^{2}}}}\ \ (i=1,\ 2,\ \cdots ,\ r)$
$D_{i}^{-}\text{=}\sqrt{\sum\limits_{\text{j}=1}^{3}{{{({{I}_{ij}}-Z_{j}^{-})}^{2}}}}\ \ (i=1,\ 2,\ \cdots ,\ r)$
where $Z_{1}^{+},\text{ }Z_{\text{2}}^{+},\text{ }Z_{\text{3}}^{+},$ represent the . $Z_{Q}^{+}$ and $Z_{E}^{+},$respectively; while $Z_{1}^{-},\ Z_{2}^{-},\ Z_{3}^{-}$represent the $Z_{N}^{-},\ Z_{Q}^{-}$ and $Z_{E}^{-},$ respectively.
(6) Calculating the degree of closeness Ci to the ideal solution.
where the smaller Di+ is, the closer it is to the positive ideal solution, and thus the better the security state; while the larger Di- is, the farther it is from the negative ideal solution, indicating a better security state. Hence, overall, the larger the Ci, the higher the security level (Zareie et al., 2018).
In assessing cultivated land security based on the “trinity” of quantity, quality, and ecology, all three aspects should meet specific security levels, namely, stable quantity, good quality, and harmonious ecological development. In other words, the effective supply of cultivated land resources can simultaneously satisfy the growing demand for economic development, population growth, and resource and environmental protection (Kuang et al., 2018). Hence, we should consider the integration of quantity, quality, and ecology in cultivated land security. To do this explicitly, we introduced the coefficient of variation K, which expresses the degree of dispersion of each security index in the system.
where Iij is the jth security index of the ith evaluation unit, i =
1, 2,…, r; and j = 1, 2, and 3, represent the IN, IQ, and IE, respectively. $\overline{I}$ is the average of the three security indices..
The larger the value of K is, the higher the dispersion degree of each security index in the system, and the lower the security level of cultivated land. Therefore, the degree of closeness is modified to ${{C}_{i}}^{\prime }$ as follows:

2.5 Security classification method

Some scholars use the comparative judgment method to determine the security threshold according to the majority principle, the median principle, or the average principle; or they arrive at a security rating based on the “principle of equipartition” (Zheng et al., 2015). According to the view of system theory, there is no absolutely secure object, and risk factors exist in any security state (Kuang et al., 2018). In this study, the last approach was adopted, with the corrected degree of closeness [0, 1] divided into five equal levels of highly secure [0.8, 1.0], generally secure [0.6, 0.8), critically secure [0.4, 0.6), generally dangerous [0.2, 0.4), and highly dangerous [0, 0.2].

2.6 Driver model construction

2.6.1 Selecting the drivers
Changes to cultivated land security arise from human activities and natural factors combined (Mitsuda, 2011; Hernández et al., 2016; Negasa et al., 2017). The evaluation indicators would cause the cultivated land security to change directly. However, the drivers in this research are mainly indirect influencing factors that lead to a change in one or more direct factors, so they fundamentally underpin the changes in cultivated land security (Lambin et al., 2003; Díaz et al., 2015). We chose the specific drivers on the basis that the relevant data could be obtained, quantified, and spatialized, and we selected the representative drivers factors to avoid data redundancy, which would affect the accuracy of the results. According to their nature, drivers of changes in cultivated land security may be classified and divided into natural, socio-economic, and policy factors (Table 2).
Table 2 Preliminary selection of the drivers of changes in cultivated land security
Feature class Driver Instruction Predicted influence direction
Natural factors The effective accumulated temperature (X1) Sum of the average temperature>10 ℃ per year +
Precipitation (X2) Unit: mm +
Socio-economic factors Per capita income of farmers (X3) Changes in farmers’ income levels +
Agricultural mechanization level (X4) Unit: ha per agricultural machine +
Policy factors Investment in environmental governance as
a proportion of GDP (X5)
Environmental governance investment / GDP +
Protection of cultivated land (X6) Increased area of cultivated land / reduced area of cultivated land +
Input of agricultural technicians (X7) Unit: ha per person +

Note: Both independent and dependent variables are the changing amount of the factor values from 1995 to 2005 (the first period) and from 2005 to 2015 (the second period), namely the value of 2005 minus that of 1995 for the first period, and likewise the value of 2015 minus that of 2005 for the second period.

2.6.2 Implementing the driver model
The large-scale land use data may contain spatial autocorrelation and sample non-independence, which a spatial regression model can effectively overcome (Fang et al., 2016). In this method, Anselin (1988) and Elhorst (2003) consider spatial correlation in terms of a traditional ordinary least squares (OLS) model. By introducing a spatial lag term and a spatial error term, the problem of spatial co-dependence is effectively and simply solved (Lambin et al., 2003; Zdruli et al., 2014).
The spatial lag model took into account the interdependence of the dependent variables of the adjacent units, expressed as follows:
$y=\beta \times X+\rho \times Wy+\varepsilon$
where y is the dependent variable, that is, the change of the cultivated land security index (C°); X is the independent variable; β is the regression coefficient of independent variables; ρ is the spatial autocorrelation coefficient, reflecting the direction and degree of influence of an adjacent element on element y, that is, spatial interdependence; Wy is the spatial lag term; and ε is a “well behaved” error, with mean 0 and variance matrix σ2.
The spatial error model took into account the process of spatial interaction of the variables on the error term, expressed as follows:
$y=\beta \times X+\lambda \times W\mu \text{+}\varepsilon$
where λ is the regression coefficient of the spatial error term and is the spatial error term. The other variables are the same as in formula (13).
According to the suggestions of Anselin (Anselin, 1988), the maximum likelihood method was used to estimate the parameters of the two spatial regression models. The estimation was implemented in the GeoData 1.14 (https://geoda

3 Results

3.1 Changes in cultivated land security

From 1995 to 2015, the period strongly affected by economic development and urban construction, the IN of cultivated land in Yingtan City’s central area declined rapidly; this low level of quantitative security then continued to extend to the west, while increasing slightly in the eastern and southern areas. In the first period (1995-2005), the IQ of cultivated land was characterized by a centralized distribution of high-value areas in the west but low values in the central and eastern areas. In the second period (2005-2015), the overall quality level improved, but this improvement was clearly less concentrated. Considering the third aspect of cultivated land security, the IE was greatly affected by human activities, declining sharply from 1995 to 2005 and then declining slowly from 2005 to 2015. These low IE values were mainly concentrated in the central part, whereas good ecological conditions persisted in the southern mountainous area. The level of ecological security was lower in the northern area than in the southern area, but higher than that in the central part (Fig. 2).
We used the improved TOPSIS method to obtain the comprehensive index for cultivated land security. The portion of cultivated land with low security was mainly distributed in the central part of Yingtan City, where the level deteriorated year by year and the low-security area extended to the west. In 1995, the areas with good security conditions were primarily found in the northern and southwestern regions, while in 2005 and 2015, such conditions were mostly restricted to the mountainous areas in the south. Table 3 and Fig. 3 show how the state of cultivated land security and the area at each security level changed in Yingtan City from 1995 to 2015.
In 1995, the state of the cultivated land security in Yingtan City mainly included three upper and moderate levels of generally secure, critically secure, and generally dangerous. Among them, a critically secure level accounted for 91.32% of all cultivated land which was distributed throughout the whole territory. Cultivated land assigned a generally secure level was mainly distributed in the northern and southwestern corners. We did find a small amount of generally dangerous cultivated land which was mainly located in the Bailu Community of the Yuehu District.
Table 3 The state of cultivated land security in Yingtan City from 1995 to 2015, expressed as a proportion of its total area in 1995, 2005, and 2015. (%)
Security state 1995 2005 2015
Generally secure 8.01 6.14 5.95
Critically secure 91.32 48.96 60.72
Generally dangerous 0.67 37.55 27.38
Highly dangerous 0 7.36 5.95
In 2005, the state of cultivated land security showed a downward trend, falling to moderate and lower levels, with a decline in the proportion of cultivated land at a generally secure level that had become scattered in the southern area. The proportion of cultivated land that was generally dangerous increased to 37.55%, and was mainly distributed in the north central part. Notably, the proportion of cultivated land at a highly dangerous level was 7.36%, and mainly distributed in the Bailu Community, Zhongtong Town, and Huangxi Town in the midwest.
In 2015, the proportion of cultivated land with a generally secure level decreased slightly, and became scattered in the south. Cultivated land in a generally dangerous state occurred chiefly in the western towns of Guixi City and central towns of Yujiang County, which are relatively fast-developing towns undergoing rapid economic development. The area of cultivated land deemed highly dangerous declined slightly, and was found largely in the Yuehu District and Zhongtong Town.
Fig. 2 Spatial distribution of values for the quantitative security (a-c), qualitative security (d-f), and ecological security (g-i) indexes of cultivated land in Yingtan City, China, in 1995, 2005, and 2015.
Fig. 3 Spatial distribution of values for the comprehensive index of cultivated land security (a-c) and areas with different security levels (d-f) in Yingtan City, China, in 1995, 2005, and 2015. Note: The meaning of the numbers in maps is the same as that of the numbers in Fig. 1.

3.2 Drivers of changes in cultivated land security

To highlight the advantages of using a spatial regression model, we also generated the OLS regression results for a comparative analysis. After checking for collinearity among the factors, those strongly associated with each other were excluded, and our results showed that both the Lagrange Multiplier (lag) and Lagrange Multiplier (error) terms were significant. Therefore, the spatial regression model performed better than traditional OLS. According to the suggestions of Anselin (Anselin, 1988), the SEM was deemed more suitable than the SLM due to its higher Log-likelihood value, its lower Akaike information criterion value, and its lower Schwarz criterion value (see Table S1). Here, we adopted the SEM regression results to analyze the drivers which caused the changes of the cultivated land security (see Table 4).
(1) Among the natural factors examined, the effective accumulated temperature had a positive influence on the cultivated land security in the second period, however it had no significant influence on the cultivated land security in the first period. The precipitation had a negative influence on the cultivated land security in the first period and a positive influence in the second period.
Table 4 Driving factors of cultivated land security in Yingtan City from 1995 to 2015 based on the SEM
Variable The first period (1995-2005) The second period (2005-2015)
Coefficient Std. Error P Coefficient Std. Error P
Constant 0.307 0.066 <0.01 ‒0.873 0.228 <0.01
l 0.816 0.009 <0.01 0.941 0.007 <0.01
The effective accumulated temperature (X1) ‒0.001 0.001 0.720 ‒0.025 0.004 <0.01
Precipitation (X2) ‒0.001 0.001 <0.01 1.007 0.256 <0.01
Per capita income of farmers (X3) 0.001 0.001 <0.01 0.002 0.007 0.795
Agricultural mechanization level (X4) 0.001 0.002 0.921 0.036 0.018 0.046
Investment in environmental governance as a proportion of GDP (X5) 0.357 0.034 <0.01 0.073 0.019 <0.01
Protection of cultivated land (X6) 0.003 0.001 <0.01 0.001 0.002 <0.01
Input of agricultural technicians (X7) 0.001 0.001 <0.01 0.001 0.001 0.803
(2) Among the socio-economic factors, the per capita income of farmers had a positive influence in the first period and no significant influence in the second period; while the agricultural mechanization level had a positive influence in the second period and no significant influence in the first period.
(3) Among the policy factors, in both periods, the investment in environmental governance as a proportion of GDP had a positive influence on the cultivated land security. The protection of forest land had a significant influence on the cultivated land security in both periods, however in the first period the influence was negative and in the second period the influence was positive. The input of agricultural technicians only had a significant positive influence in the first period.

4 Discussion

4.1 Implications of the natural factors influences on the cultivated land security

In the first period (1995‒2005), the effective accumulated temperature had no significant influence on the cultivated land security, while in the second period (2005‒2015), its influence was significantly negative. The effective accumulated temperature is known to be of great importance to the growth of crops, especially to the sowing time, harvesting time and yield (Eckersten et al., 2010). From 1995 to 2015, the effective accumulated temperatures in Yingtan City were unstable, showing an upward tendency in the first period and a downward tendency in the second period (see Fig.4a). The precipitation had a significant influence on the cultivated land security, but in the first period its influence was negative, while in the second period its influence was positive. Overall, the precipitation was in an unstable state from 1995 to 2015 (see Fig. 4b). In the first period, the change of precipitation showed a downward tendency, and the direction changed to upward in the second period. Similar to the effective accumulated temperature, the precipitation is also an important factor for guaranteeing the harvest of crops (Wiik and Ewaldz, 2009).
Though the effective accumulated temperature and precipitation both showed unstable trends from 1995 to 2015, the precipitation had quite a positive influence on the cultivated land security, and the land consolidation projects are the reason which explains that difference. Since 2006, the Yingtan City has invested a great deal of money on land consolidation projects. Land consolidation projects are an important means to protect the quantity, quality and ecological environment of cultivated land (Song et al., 2019). Through land leveling works, the height differences between different cultivated land plots can be reduced, and the irrigation and drainage ditches can be improved (Zhang et al., 2014). For cultivated land plots without a stable water source, new channels had been built to connect the water sources and the plots, so as to meet the water demands for crop growth in dry seasons. For waterlogged areas, new drainage channels had been built to ensure that the excessive water in the plots could be discharged rapidly during the continuous rainy season.
Fig. 4 The changing of natural factors in Yingtan City from 1995 to 2015, (a) Effective accumulated temperature; (b) Precipitation.

4.2 Implications of the social-economic factor influences on the cultivated land security

In the first period, the per capita income of farmers had a positive influence but it had no significant influence in the second period, while the agricultural mechanization level only had a positive influence in the second period. There are two notable reasons which explain these two phenomena.
Firstly, from 1995 to 2015, the income structure of farmers in Yingtan City had changed dramatically in the ensuing 20 years. The income structure of farmers had changed from being mainly dependent on crop cultivation to one with much more income diversification. In the first period (1995-2005), the income of farmers mainly depended on crop cultivation. After 2006, with the economic development of Yingtan City, the development of industry and service industries prompted many farmers to work in the city, and since then “part-time farmers” appeared. This is a common phenomenon in the eastern area of China, and the changing income structure of farmers is the fundamental reason for farmers to transfer out of cultivated land (Wang et al., 2020a). The urbanization increased labor costs, farming profits declined until they became losses, and the farmland was finally abandoned (Wang et al., 2020b). According to a farmer survey in 2017, the gap between agricultural income and industrial income in Yingtan City had reduced farmers' time and cost investments in agricultural production, which resulted in the phenomenon of abandoning cultivated land in some areas, and weakened the influence of the per capita income of farmers on the cultivated land security.
So why did the changing of farmers’ income structure not have a negative influence on the cultivated land security? Land circulation policy explains the influence of the agricultural mechanization level on the cultivated land security. From 1995 to 2005, farmers' income was relatively low, and it was not enough to bear the cost of purchasing agricultural machinery. Thus, the agricultural machinery level remained quite low and its influence on the cultivated land security was not significant. After 2006, the government of China took timely measures and put forward the land circulation policy (Lu et al., 2018; Xu et al., 2018). By transferring the right of land management to scale operators, this policy not only ensured crop planting, but also improved the utilization rate of agricultural machinery in agricultural production. With the improvement of the agricultural machinery level, the demand for human resources had been reduced. Deep ploughing and rotary ploughing also created more favorable soil conditions for crop growth and improved the safety of cultivated land security.

4.3 Implications of the policy factor influences on the cultivated land security

The investment in environmental governance as a proportion of GDP had a positive influence on the cultivated land security. From 1995 to 2015, industrial wastewater negatively impacted the ecological environment of cultivated land, as it polluted the soil of cultivated land with heavy metals that would seriously endanger human health. Yingtan City is well known as the “copper capital”, because its industrial wastewater discharge was primarily produced by the metal smelting process, in which heavy metals were more likely to exceed the standard allowances (Zhou et al., 2018). This metal smelting and processing industry was mainly located in Guixi City, an area where the comprehensive index for heavy metal pollution exceeds the 0.7-value threshold for safety (Long et al., 2006). Although it is concentrated in Binjiang Town, this pollution is also scattered among other areas. Early in the second period, the villages around the Guixi smelting plant were so far over the standard for heavy metals, the villages of Sumen, Pangyuan, and Qiqiao near the smelting plant had to be relocated. As industrial wastewater could seriously endanger human life and health, government departments were increasingly paying attention to this environmental issue, by providing more funding and technical input to mitigate such situations. This policy influence can be seen from the significance of our results in both the first and second periods.
The protection of cultivated land had a significant positive influence on the cultivated land security. As urbanization progressed, a large amount of cultivated land had been transferred to other land use types due to accelerated land use and cover changes (Zhu, 2008; Sterling et al., 2013; Elhorst, 2003; Zhong et al., 2018). In our study region, the Yuehu District was the political, economic, commercial, cultural, scientific, educational, and transportation center and hub of Yingtan City, so its cultivated land security was the lowest, being heavily affected by these human activities. Guixi City was an industrial county, which ranked among the top 100 counties in China, and its northern and southern mountainous areas at high elevations were the areas with the strongest cultivated land protection. As a new center of urban development, Yujiang County had witnessed the fastest decline in cultivated land security since 2006.
The input of agricultural technicians had a positive influence on the cultivated land security in the first period but no significant influence in the second period. This phenomenon could be explained by the land circulation policy (Wang et al., 2020a). Especially since 2006, after land circulation, compared with small-holding farmers, scale operators generally had a higher education background, greater economic strength, and better cooperation with agricultural companies, scientific research institutions and sales companies. These advantages ensured that scale operators had multiple channels for agricultural technology training. In order to improve the scientific and technological level of farmers, the Agricultural Management Department of Yingtan City had arranged technical training programs for farmers. Under the new situation of land transfer, the agricultural management department should provide targeted, customized and detailed agricultural technical services according to the cultivation characteristics of the scale operators.

5 Conclusions

This study investigated in detail the spatiotemporal variation of cultivated land security and its drivers in Yingtan City in Jiangxi Province, China, from 1995 to 2015, from the perspective that the quantity, quality, and ecology of cultivated land are equally important. Cultivated land security in Yingtan City decreased rapidly from 1995 to 2005, but it improved slightly from 2005 to 2015. The cultivated land in a low-security state was mainly distributed in the central and northern parts of the main urban construction areas, and it continued to extend in the same direction as urban expansion. By analyzing the factors driving cultivated land security in Yingtan City based on the SEM, our research found that the socio-economic and policy factors were significantly influencing the cultivated land security. In order to improve cultivated land security, we should establish a balance-safeguard mechanism to better coordinate urban development and cultivated land protection.
Table S1 Statistical results for the driver analysis of changes in the cultivated land security in Yingtan City (Jiangxi Province, China) from 1995 to 2015
Variable The first period (1995-2005) The second period (2005-2015)
Constant 0.235*** 0.049*** 0.307*** ‒0.618*** 0.013 ‒0.873***
X1 ‒0.041*** ‒0.001 ‒0.001 ‒0.043*** ‒0.026 ‒0.025***
X2 ‒0.206*** ‒0.001*** ‒0.001*** 0.650*** 0.007 1.007***
X3 0.026*** 0.001 0.001*** 0.065*** 0.006 0.002
X4 ‒0.076*** ‒0.002*** 0.001 0.031*** 0.008 0.036**
X5 0.192*** 0.083*** 0.357*** 0.071*** 0.013** 0.073***
X6 0.081*** 0.002*** 0.003*** 0.015*** ‒0.001 0.001***
X7 0.002*** 0.001*** 0.001*** 0.003*** 0.001 0.001
l 0.816*** 0.941***
W_Y 0.798*** 0.925***
Lagrange multiplier (lag) 18841.548*** 46087.474***
Robust LM (lag) 249.744*** 256.640***
Lagrange multiplier (error) 19681.217*** 53157.894***
Robust LM (error) 10894.13*** 7327.059***
Log likelihood ‒4587.287 ‒2049.645 ‒2004.538 1186.945 3950.090 4056.011
Akaike info criterion 9192.574 4119.289 4026.716 ‒2355.890 ‒7880.172 ‒7994.020
Schwarz criterion 9265.655 4200.490 4099.797 ‒2283.502 ‒7799.744 ‒7921.640

Note: Numbers in the table are the regression coeffcients; *** P < 0.01, ** P < 0.05, and * P < 0.1.

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