Analysis of Cultivated Land Change and Its Driving Forces in Jiangsu Province, China

Abstract

Since 2000, the Yangtze River Delta region has undergone a period of rapid urbanization in China. A large area of cultivated land has been converted into construction land, which greatly affects food security. The decrease in cultivated land caused by urbanization is also very serious in Jiangsu Province, one of the main grain-producing areas in the Yangtze River Delta region. Based on the remote sensing data of Jiangsu Province in 2000, 2010 and 2020, this paper analyzes the land use changes occurring in 13 regional cities in Jiangsu Province from 2000 to 2020 by using the transfer matrix. Spatial and temporal geographical weighted regression models were used to analyze the differences in the economic, social and policy impacts of land use change across the province. The results show that the cultivated land area is decreasing, and the closer to the urban center, the faster the decrease in cultivated land in Jiangsu Province. Cultivated land was mainly transferred out to construction land, waters and woodlands. The human factors affecting the change in cultivated land area in the province can be divided into a population growth factor, economic development factor, rural development factor and land policy factor. Among them, population growth and economic development had a negative effect on cultivated land protection, while improvements in the agricultural production level and cultivated land protection policy had a positive effect on cultivated land protection. According to the analysis of spatial-temporal heterogeneity of cultivated land area change, the growing urbanization rate had the greatest impact on the cultivated land area in Southern Jiangsu, and the impact of real estate development on cultivated land has been reduced in small cities. The conclusion of this paper has important policy implications for promoting cultivated land protection and ensuring food security.

1. Introduction

Cultivated land is an important space carrier for human beings to engage in production and life and is also the basic means of production for agricultural production. Changes in cultivated land are closely related to food security and sustainable development. Since 2000, urbanization has entered into a period of rapid development in China. With the acceleration of urbanization, it is difficult to avoid massive losses of cultivated land resources into construction land [1,2]. Therefore, it is of great significance to examine land use change and its influencing mechanism [3] and set forward targeted measures for cultivated land protection to ensure food security and sustainable development [4,5,6,7]. As an important part of the Yangtze River Delta region, Jiangsu Province is not only economically developed but also contributes greatly to China’s grain production. In 2023, the grain output reached 37.975 million tons, accounting for 5.46 percent of the country’s total grain output. However, with the acceleration of urbanization, a large amount of cultivated land has been converted into construction land, and the cultivated land area has decreased from 5,016,300 hectares in 2000 to 4,139,600 hectares in 2023, a decrease of 17.5%, with an average annual reduction rate much higher than the national average level in the same period, showing a deep contradiction between economic development and cultivated land protection.

Many scholars at home and abroad have studied the impact of urbanization on cultivated land use. In recent years, the commonly used methods use satellite remote sensing data to analyze and compare land functional attributes at different time points through ArcGIS 10.6 software, including the static analysis method, dynamic change analysis method, transfer matrix method and so on [8,9,10,11]. Yang et al. (2021) [12] and Jayasinghe and Kumar (2023) [13] have applied analysis to agricultural production. Research on the influencing factors of land use change is generally divided into two categories: natural factors and human factors. Natural factors include temperature, rainfall, topography and so on. Human factors include regional economic development, population change, living standards, policies and so on. Scholars believe that land use change is the compound effect of the regional natural environment and social economy [14,15,16]. More and more scholars have shown that the main factors causing short-term land use change are more driven by human factors, and the main influencing factors are economic development, population growth, agricultural development, policy implementation and so on [17,18,19,20]. Similar research results were found for the Yangtze River basin [21,22,23].

This study contributes to the literature in two ways. First, previous studies have applied satellite remote sensing data to the impact of urbanization on cultivated land use, mainly from a geographical perspective. Few economic analyses have been made on the driving forces for its formation. This study attempts to bring new insights into this aspect. Second, the existing literature on the driving force of land use change focuses on the analysis of the factors that affect the whole province. We found few studies on the heterogeneity of the impacts of different subdivisions in the region. Based on the identification and comparison of satellite data in 2000, 2010 and 2020, combined with the statistical data on land use in Jiangsu Province in the past 20 years, this paper summarizes the characteristics of land use change and analyzes the influencing factors in the 13 main cities of Jiangsu Province. Through the study of population growth, economic growth, agricultural development and land policy, we empirically analyze the main factors affecting the changes in cultivated land area in Jiangsu Province. At the same time, the spatial and temporal differences of cultivated land change are explored to find out the main driving factors. The research conclusions can provide differentiated governance schemes for coordinating food security and regional development and have important practical significance for territorial space governance.

2. Methods and Data

2.1. Research Method

2.1.1. Land Use Dynamic Degree

The map of land use types in different periods of Jiangsu Province was made by ArcGIS10.6, and the unified coordinate system was WGS_1984_UTM_Zone_50N. According to the China Land Use/land Cover remote sensing monitoring data classification system, the land use types in the study area were divided into eight categories, including cultivated land, garden land, woodland, grassland, wetland, water area, construction land and unused land.

The dynamic degree of land use can quantitatively describe the speed of land use change in a certain period of time, as shown in Equation (1):

$$K={\displaystyle \frac{U_b-U_a}{U_a}}\times {\displaystyle \frac{1}{T}}\times 100\%$$

(1)

where K is the dynamic degree of a certain land use type in the study period. $U_a$ and $U_b$ are the areas of a certain land use type at the beginning and end of the study period, respectively. T is the research period. When the period of T is defined as a year, the value of K is the annual change rate of a certain land use type in the study area.

2.1.2. Transition Matrix Analysis

The land use transfer matrix is a method that studies the quantity and direction of mutual transfer between different land classes in different regions. Its principle is to obtain a two-dimensional matrix according to the changing relationship of land cover status in different time periods in the same region. Through the analysis of the transfer matrix, the mutual transformation between different land classes in two time periods can be obtained. The land use transfer matrix reflects the evolution direction and degree of each class in a certain period of time within a certain study area, which can not only quantitatively indicate the transformation among different land use types but also reveal the transfer rate among different land use types. The mathematical form of the transfer matrix is as follows:

$$S_{ij}=\left\{\begin{array}{ccccc}{S}_{11}& S_{12}& S_{13}& \cdots & S_{1n}\\ S_{21}& S_{22}& S_{23}& \cdots & S_{2n}\\ S_{31}& S_{32}& S_{33}& \cdots & S_{3n}\\ \cdots & \cdots & \cdots & \cdots & \cdots \\ S_{n1}& S_{n2}& S_{n3}& \cdots & S_{nn}\end{array}\right\}\left(i,j=1,2,3\cdots ,n\right)$$

(2)

where $S_{ij}$ is the area of type i land converted into type j land. N is the number of land use types. I and j are the land use types before and after the transfer, respectively.

2.1.3. Multiple Linear Regression Analysis

The multiple linear regression model is a commonly used system analysis model to explain land cover changes. Therefore, multiple linear regression analysis was used to analyze the influencing factors of the change in cultivated land area in Jiangsu Province. This paper establishes the following regression equation:

$$Y={\beta }_0+{\displaystyle \sum _{i=1}^n{\beta }_i{X}_i}+\mu$$

(3)

In the formula, the dependent variable Y is the cultivated land area. Independent variables X₁−X_n are the various factors affecting the cultivated land area, and the specific meanings are shown below; β is the constant term and the coefficient of influencing factors; μ is the random disturbance term.

Land use change is caused by many factors, including population, economy, industry, policy and so on. Four categories of indicators, including population growth, economic development, agricultural development and cultivated land protection policy, were selected in this study. The selection is based on the analysis of land resource changes and influencing factors in Jiangxi and Jiangsu provinces by Liu et al. [24], Guo Jie et al. [25], and Sun [26], and the availability and operability of data. Major driving forces include the total population at the end of the year, the rate of urbanization, the real estate investment, the proportion of secondary industry employees, the proportion of tertiary industry employees, the agricultural output value, the per capita housing area in rural areas, the total power of agricultural machinery and the number of cultivated land protection policies. These are shown in

Table 1. Major land use driving factors in Jiangsu Province.

Category	Independent Variable	Symbol
Population growth	Total population at end of year (10,000 persons)	X1
	Rate of urbanization (%)	X2
Economic development	Real estate investments (100 million yuan)	X3
	Proportion of secondary industry employees (%)	X4
	Proportion of tertiary industry employees (%)	X5
Development of rural areas	Agricultural output value (100 million yuan)	X6
	Per capita housing area in rural areas (m2)	X7
	Total power of agricultural machinery (10,000 kW)	X8
Land policy	Cultivated land protection policies (case)	X9

. In order to avoid serious multicollinearity, the variance inflation factor (VIF) test was used for independent variables and the VIF of each independent variable was less than 10, and most of them were less than 5.

2.1.4. Spatio-Temporal Geographically Weighted Regression Analysis

The spatio-temporal geographically weighted regression model was improved by Huang [27] on the basis of the original geographically weighted regression model. Its main advantage is that the time dimension is introduced into the geographically weighted regression model, which can analyze the influence of changes in the independent variables on dependent variables from both the time and space perspectives. The expression of the spatio-temporal geographical weighted regression model is:

$$y_i={\beta }_0\left(u_i,v_i,t_i\right)+{\displaystyle \sum _{k=1}^p{\beta }_k}\left(u_i,v_i,t_i\right)x_{ik}+{\epsilon }_i$$

(4)

In the formula, u_i and v_i are the longitude and latitude coordinates of the city and county center; $\left(u_i,v_i,t_i\right)$ is the spatio-temporal coordinates of the ith sample point; and ${\beta }_0\left(u_i,v_i,t_i\right)$ is the regression constant of point i, which is the constant term in the spatio-temporal geographical weighted regression. ${\beta }_k\left(u_i,v_i,t_i\right)$ is the Kth regression parameter at point i. The $x_{ik}$ is the value of the independent variable $x_k$ at point i, which is the value of each quantitative index in the index system of the spatio-temporal geographical weighted regression model. The ${\epsilon }_i$ is the residual term of the model.

In order to eliminate the influence caused by the difference in the order of magnitude and dimension of the data, the range standardization of the data was carried out with the value range [0, 1], and the formula is as follows:

$$A_{ij}={\displaystyle \frac{X_{ij}-\min (x_i)}{\max (x_i)-\min (x_i)}}$$

(5)

In the formula, i is the year, j is the index number and $\min (x_i)$ and $\max (x_i)$ are the minimum and maximum values of the index in consecutive years.

2.2. Data Source and Processing

2.2.1. Interpretation of Remote Sensing Data

Based on the remote sensing images of the “geospatial data cloud” in 2000, 2010 and 2020 as the main data source, the land cover was divided into cultivated land, garden land, forest land, grassland, wetland, water area, construction land, unused land and other categories through interpretation and identification. On the basis of the original spectral information of the image, the area of each class was extracted by ENVI5.3 software, and the results were analyzed. When remote sensing images are used to classify ground objects, the extraction of feature bands is an important factor in determining the classification accuracy. Spectral features were used to distinguish different ground objects by different combinations of spectral bands, which were widely used in remote sensing image classification. In the process of remote sensing data processing, data accuracy was improved by using multi-source data fusion and precision checking methods. At the same time, the ground measurement data were used to verify and correct the remote sensing interpretation results to reduce errors. Based on the feature indexes extracted from time-series Landsat TM and ETM remote sensing data, this paper classified ground objects in the study area and calculated the overall classification accuracy and KAPPA coefficient. The results show that the overall accuracy of this study is 88.35%, and the Kappa coefficient is 0.86.

2.2.2. Data of Influencing Factors

Data on the various factors affecting cultivated land area include the following (

Table 2. Statistical descriptions of influencing factors.

Variable	Obs	Mean	Std. Dev.	Max	Min
Total population at end of year (10,000 persons)	273	599.266	198.850	1274.960	284.490
Rate of urbanization (%)	273	57.632	13.860	86.800	25.500
Real estate investments (100 million yuan)	273	404.728	519.752	2686.468	2.772
Proportion of secondary industry employees (%)	273	22.464	7.198	40.633	8.455
Proportion of tertiary industry employees (%)	273	20.642	5.409	36.118	9.134
Agricultural output value (100 million yuan)	273	186.460	134.841	760.860	33.940
Per capita housing area in rural areas (m2)	273	48.313	12.767	76.000	21.200
Total power of agricultural machinery (10,000 kW)	273	306.850	176.234	765.190	93.240
Cultivated land protection policies (case)	273	28.048	31.107	152.000	0.000

): total population at the end of the year, rate of urbanization, real estate investment, proportion of secondary industry employees, proportion of tertiary industry employees, agricultural output value, per capita housing area in rural areas and the total power of agricultural machinery. These data are collated from the statistical yearbooks of Jiangsu Province and the 13 regional cities of Jiangsu Province from 2000 to 2020. The cultivated land protection policy data are selected as representatives of the cultivated land protection policies and regulations issued since 2000, as well as the relevant interpretation information measurements, which are obtained from the websites of the Jiangsu Provincial Department of Natural Resources and the 13 cities.

3. Empirical Results Analysis

3.1. Dynamic Situation of Cultivated Land

3.1.1. Overall Change in Cultivated Land

The total area of Jiangsu Province is about 107,200 km², among which cultivated land, water area and construction land are the main land use types (

Table 3. Area and proportion of various land use types in Jiangsu Province. Unit: 10,000 hectares.

Type of Land	2000		2010		2020		2000–2010	2010–2020	2000–2020
	Area	Proportion (%)	Area	Proportion (%)	Area	Proportion (%)	Dynamic Attitude	Dynamic Attitude	Dynamic Attitude
Cultivated land	501.63	46.77	460.43	42.93	407.59	38.00	−0.82	−1.15	−0.94
Garden land	29.67	2.77	31.50	2.94	22.9	2.14	0.62	−2.73	−1.14
Woodland	31.38	2.93	26.20	2.44	78.43	7.31	−1.65	19.94	7.50
Grassland	2.21	0.21	4.33	0.40	9.18	0.86	9.59	11.20	15.77
Water area	250.84	23.39	256.96	23.96	250.18	23.33	0.24	−0.26	−0.01
Construction land	164.99	15.38	221.75	20.68	248.52	23.17	3.44	1.21	2.53
Wetland	72.21	6.73	55.32	5.16	41.14	3.84	−2.34	−2.56	−2.15
Unused land	19.54	1.82	15.98	1.49	14.53	1.35	−1.82	−0.91	−1.28

). The area of cultivated land decreased from 5,016,300 hectares in 2000 to 4,075,900 hectares in 2020, a decrease of 940,400 hectares, or 18.7 percent. The area of construction land increased from 1,649,900 hectares in 2000 to 2,485,200 hectares in 2020, an increase of 835,300 hectares, or 50.6%, in Jiangsu Province.

As shown in Figure 1, Garden land, grassland, water area and construction land showed positive dynamic changes, while cultivated land, woodland and wetland showed negative dynamic changes from 2000 to 2010. Woodland, grassland and construction land showed positive dynamic changes, while cultivated land, garden land, water area and wetland showed negative dynamic changes from 2010 to 2020. Woodland, grassland and construction land showed positive dynamic changes, while cultivated land, garden land, water area and wetland showed negative dynamic changes from 2000 to 2020, among which cultivated land showed a continuous negative change and construction land showed a continuous positive change.

3.1.2. Change Characteristics of Other Land Converted to Cultivated Land

In terms of cultivated land transfer (

Table 4. The proportion of cultivated land transferred from various cities from 2000 to 2010 (%).

	Woodland	Grassland	Water Area	Construction Land	Others
Nanjing	21.58	0.62	44.47	33.32	0.01
Wuxi	10.30	2.51	33.12	53.87	0.20
Xuzhou	2.40	6.87	15.66	74.98	0.09
Changzhou	5.61	0.30	27.69	66.27	0.13
Suzhou	0.17	0.35	43.48	56.00	0.00
Nantong	0.30	0.24	20.45	78.98	0.03
Lianyungang	0.17	0.81	40.43	58.52	0.07
Huai’an	4.90	9.29	35.36	50.33	0.12
Yancheng	0.81	2.17	47.71	48.52	0.79
Yangzhou	1.24	9.12	58.23	31.38	0.03
Zhenjiang	8.56	0.14	38.01	53.29	0.00
Taizhou	0.02	8.49	41.23	50.09	0.17
Suqian	0.08	5.58	21.04	73.25	0.05
Jaingsu	4.07	3.87	36.09	55.81	0.16

), the increase in cultivated land area mainly comes from water bodies and construction land, accounting for 36.09% and 55.81% of the total transferred area during 2000–2010. The cultivated land transfer mainly comes from construction land, water bodies and woodland in major economically developed cities, such as Nanjing, Wuxi, Changzhou and Zhenjiang, accounting for more than 95% of the total cultivated land transfer area. The cultivated land transfer mainly comes from construction land, water bodies and grassland in economically emerging cities, such as Xuzhou, Huai’an, Yangzhou, Taizhou and Suqian, accounting for more than 95% of the total cultivated land transfer area. The transfer of cultivated land mainly comes from construction land and water bodies in Suzhou, Nantong, Lianyungang and Yancheng.

In terms of cultivated land transfer (

Table 5. The proportion of cultivated land transferred from various cities from 2010 to 2020 (%).

	Woodland	Grassland	Water Area	Construction Land	Others
Nanjing	36.62	0.59	20.52	42.26	0.01
Wuxi	27.39	2.18	0.61	69.82	0.00
Xuzhou	0.67	1.84	3.59	93.90	0.00
Changzhou	29.54	1.66	1.05	67.75	0.00
Suzhou	5.76	0.36	4.08	89.80	0.00
Nantong	0.18	0.21	15.56	84.05	0.00
Lianyungang	0.36	0.84	5.34	93.46	0.00
Huai’an	9.27	6.18	21.02	63.53	0.00
Yancheng	0.35	0.84	19.87	78.94	0.00
Yangzhou	4.74	17.51	8.46	69.29	0.00
Zhenjiang	35.47	0.73	3.27	60.53	0.00
Taizhou	0.09	4.78	1.95	93.18	0.00
Suqian	0.09	1.89	9.41	88.61	0.00
Jiangsu	9.05	2.64	10.84	77.47	0.00

), the increase in cultivated land area in Jiangsu Province mainly comes from construction land, accounting for 77.47% of the total transferred area from 2010 to 2020. This phenomenon is closely related to policies such as rural village withdrawal and linking the increase and decrease in construction land. In Nanjing and Huai’an, cultivated land transfer mainly comes from construction land, water bodies and woodland. Cultivated land transfer in Wuxi, Changzhou and Zhenjiang mainly comes from construction land and woodland. The transfer of cultivated land in Yancheng mainly comes from construction land and water bodies. Cultivated land transfer in Yancheng mainly comes from construction land and grassland. The transfer of cultivated land in other cities mainly comes from construction land.

3.1.3. Characteristics of Cultivated Land Converted to Other Land

According to the transfer of cultivated land from 2000 to 2010 (

Table 6. The proportion of arable land transferred out of various cities in Jiangsu Province from 2000 to 2010 (%).

	Woodland	Grassland	Water Area	Construction Land	Others
Nanjing	41.77	0.97	28.12	29.13	0.01
Wuxi	13.71	0.76	35.55	49.98	0.00
Xuzhou	0.83	1.45	35.07	62.63	0.02
Changzhou	10.46	0.87	54.86	33.81	0.00
Suzhou	4.01	0.05	66.69	29.25	0.00
Nantong	0.15	0.07	0.00	99.78	0.00
Lianyungang	0.02	0.49	50.39	49.10	0.00
Huai’an	11.60	4.43	42.99	40.97	0.01
Yancheng	0.01	0.11	69.63	30.25	0.00
Yangzhou	0.88	4.51	70.75	23.86	0.00
Zhenjiang	38.13	0.23	25.37	36.27	0.00
Taizhou	0.01	1.59	42.59	55.81	0.00
Suqian	0.03	0.93	35.84	63.19	0.01
Jiangsu	6.37	1.24	49.11	43.28	0.00

), the cultivated land in Jiangsu Province is mainly transformed into water bodies, construction land and woodland, accounting for 49.11%, 43.28% and 6.37% of the total transfer area, respectively. Cultivated land in Nanjing, Wuxi, Changzhou, Huai’an and Zhenjiang was mainly converted to construction land, water bodies and forest land. Cultivated land in Xuzhou, Suzhou, Lianyungang, Yancheng, Yangzhou, Taizhou and Suqian was mainly converted to construction land and water bodies. Almost all cultivated land in Nantong has been converted into construction land.

According to the transfer of cultivated land from 2010 to 2020 (

Table 7. The proportion of arable land transferred out of various cities in Jiangsu Province from 2010 to 2020 (%).

	Woodland	Grassland	Water Area	Construction Land	Others
Nanjing	3.43	0.97	7.25	88.32	0.03
Wuxi	10.62	1.8	10.95	76.63	0.00
Xuzhou	1.52	1.73	8.02	88.73	0.00
Changzhou	2.25	0.72	27.18	69.84	0.01
Suzhou	3.16	2.07	9.05	85.67	0.05
Nantong	5.57	0.9	8.65	84.88	0.00
Lianyungang	0.47	1.85	41.86	55.81	0.01
Huai’an	3.65	0.86	44.10	51.34	0.05
Yancheng	9.45	0.4	42.21	47.91	0.03
Yangzhou	0.29	1.16	5.90	92.58	0.07
Zhenjiang	4.30	0.18	59.71	35.81	0.00
Taizhou	2.36	0.43	6.26	90.95	0.00
Suqian	0.01	1.69	5.12	93.18	0.00
Jiangsu	3.93	1.06	24.67	70.32	0.02

), the cultivated land in Jiangsu Province is mainly converted into construction land and water bodies, accounting for 70.32% and 24.67% of the total transferred area, respectively. The cultivated land in each city is also basically converted into construction land and water bodies, among which Nanjing, Xuzhou, Suzhou, Nantong, Yangzhou, Taizhou and Suqian account for more than 80% of the cultivated land converted into construction land. In Lianyungang, Huai’an and Yancheng, the amount of cultivated land converted to construction land and water bodies is relatively balanced. More cultivated land in Zhenjiang has been converted into water bodies.

3.2. Multiple Regression Estimation

3.2.1. Analysis of Regression Results

The explained variable, cultivated land area y, establishes a multiple linear regression model for all explanatory variables, X₁–X₉, and introduces time variables and regional variables to control the time effects and individual effects so as to reduce the errors caused by missing variables in the regression results. Stata15.0 software was used for the calculations, and the results are shown in

Table 8. Statistical analysis of cultivated land area and influencing factors in Jiangsu Province from 2000 to 2020.

Independent Variable	Coefficient	Standard Deviation	t-Statistic
Total population at end of year (10,000 persons)	−0.1903 ***	0.0217	−8.75
Rate of urbanization (%)	−1.0701 ***	0.3719	−2.88
Real estate investments (100 million yuan)	−0.0090 **	0.0045	−2.02
Proportion of secondary industry employees (%)	−2.3038 ***	0.3830	−6.02
Proportion of tertiary industry employees (%)	1.2534 ***	0.3317	3.78
Agricultural output value (100 million yuan)	0.0248	0.0188	1.31
Per capita housing area in rural areas (m2)	−0.6387 ***	0.2468	−2.59
Total power of agricultural machinery (10,000 kW)	0.1127 ***	0.0188	6.01
Cultivated land protection policies (case)	0.1240 *	0.0674	1.84
Constant	488.1584 ***	31.8685	15.32

Note: * p < 0.1, ** p < 0.05, and *** p < 0.01.

.

From the regression results, the impact of the increase in the total population and the urbanization rate on the cultivated land area is negative in Jiangsu. From the perspective of employment, the increase in the proportion of employees in secondary industries has a negative impact on the cultivated land area, while an increase in the proportion of employees in tertiary industries has a positive impact on the cultivated land area, indicating that the expansion of the manufacturing industry is more likely to cause a decrease in the cultivated land area. The increase in real estate investment caused a decrease in cultivated land area. The agricultural output value has a positive effect on cultivated land protection, and an increase in rural per capita housing area caused a decrease in cultivated land. The total power of agricultural machinery represents the modern technical level of agriculture, and the high level is conducive to the protection of cultivated land. The implementation of the cultivated land protection policy has played a positive role in the protection of cultivated land.

3.2.2. Regression Model Test

The adjusted R² of the above model is 0.9972, so the establishment of the model is meaningful, and the overall explanation degree reaches 99.72%. At the same time, the significance test of the regression equation shows that F = 2380.61 and the significance p = 0.000 < 0.05, indicating that the model is statistically significant. After observing the t values of the explanatory variables in the above table, most of the nine variables are significant at the significance level of 10%, indicating that the explanatory variables can pass the t-test well.

3.3. Analysis of Spatial and Temporal Differences

The main indicators, such as the urbanization rate, real estate investment, rural per capita housing area, agricultural output value and cultivated land protection policy, were selected for spatio-temporal geographical weighted regression analysis. GTWR is used for regression in the ArcGIS 10.6 software, and the adjusted R² of the model is 0.9943, and the AICc is −1195.42, indicating that the model has a high degree of fit; the selected explanatory variables can explain more than 99% of the changes in cultivated land area. The regression coefficient of the model indicates the degree and direction of the influence of the independent variable on the dependent variable. The average values of the above factors from 2000 to 2010 and from 2011 to 2020 are visualized in space.

3.3.1. The Influence of Urbanization Rate on Cultivated Land Area

As shown in Figure 2, the average influence coefficients of the urbanization rate on cultivated land area from 2000 to 2010 and from 2011 to 2020 are −0.289 and −0.515, respectively. This indicates that the negative impact of the urbanization rate is generally strengthened. From the time span of 20 years, increases in the urbanization rate in southern Jiangsu has had the most dramatic impact on the cultivated land area due to its relatively higher level of developed economy.

3.3.2. The Influence of Real Estate Investment on Cultivated Land Area

As shown in Figure 3, real estate investment has a negative impact on the cultivated land area in most cities. The average coefficients of real estate investment from 2000 to 2010 and from 2011 to 2020 are −0.831 and −0.127, indicating that the negative impact of real estate investment on cultivated land area has been weakened. In general, the influence of northern Jiangsu is greater than that of central Jiangsu and southern Jiangsu. The influence of northern Jiangsu is greater than that of central and southern Jiangsu. From 2011 to 2020, the influence coefficients of Wuxi, Changzhou, Zhenjiang, Huai’an, Yangzhou and Taizhou are positive, indicating that the trend of the decrease in the amount of cultivated land caused by real estate development is decreasing.

3.3.3. The Influence of Rural per Capita Housing Area on Cultivated Land Area

As shown in Figure 4, the rural per capita housing area has a mainly negative impact on the cultivated land area. From the perspective of a time dimension, the negative impact of rural per capita housing area on cultivated land area shows an overall increasing trend. From the perspective of a spatial dimension, this negative effect mainly shows an increasing trend from southern Jiangsu to northern Jiangsu. The increase in per capita housing area had the largest negative impact on cultivated land area in Xuzhou from 2000 to 2010 and the largest negative impact in Yancheng from 2011 to 2020.

3.3.4. The Influence of Agricultural Output Value on Cultivated Land Area

As shown in Figure 5, the impact of agricultural output value on cultivated land area is positive. As the output representation of cultivated land, the agricultural output value will stimulate farmers to expand the production of cultivated land area with the increase in agricultural output value. In general, the impact of northern Jiangsu is less than that of central Jiangsu and southern Jiangsu, which is related to the low efficiency of grain production in northern Jiangsu.

3.3.5. The Influence of Cultivated Land Protection Policy on Cultivated Land Area

As shown in Figure 6, as an important factor in promoting agricultural development, the impact of cultivated land protection policy on cultivated land area has maintained a positive value, indicating that the enforcement of cultivated land protection has been increasing. From 2011 to 2020, the implementation of cultivated land protection policies in Nanjing, Xuzhou, Lianyungang, Yancheng and Suqian should be strengthened.

4. Discussion

Based on the identification and comparison of satellite data in 2000, 2010 and 2020, this paper clarified the changes in land use, especially cultivated land use, in Jiangsu Province in the past 20 years. On this basis, the influencing factors of cultivated land use change were summarized, and the typical influencing factors were selected to make a space–time differentiation analysis. Based on the above results, the main characteristics of land use transformation, the dominant factors affecting cultivated land change, as well as the spatial and temporal differentiation of the influencing factors, were discussed.

From the perspective of land use transformation in Jiangsu Province, the land use types in the past 20 years were mainly cultivated land, water area and construction land; the area of water area remains basically unchanged, the area of construction land has increased significantly, and the area of cultivated land has decreased. This is closely related to the impact of the rapid development of urbanization and industrialization in the province, with the population flowing from rural areas to towns, the continuous increase in land demand for industrial development, and residential demand by the urban population, leading to the continuous growth of construction land. At the same time, the transportation links between cities are constantly strengthened, and the continuous increase in transportation construction land is inevitably accompanied by a decrease in other land types. Cultivated land is flat and has low construction costs, so it is the first choice for construction land expansion. The transformation of cultivated land in Jiangsu Province is closely related to construction land and water bodies. On the one hand, construction land and water bodies occupy an absolute proportion of the sources of cultivated land transformation, and on the other hand, the vast majority of cultivated land is also converted into construction land and water bodies. The conversion of construction land to cultivated land is closely related to the withdrawal of villages and the transfer of construction land in most rural areas in northern Jiangsu. The conversion of cultivated land into construction land is closely related to the occupying of a large amount of cultivated land around urban areas. The interconversion between water and cultivated land is greatly affected by the use of cultivated land for aquaculture and the reclamation of land from the sea.

Through regression analysis, the following nine factors may affect changes in the cultivated land area: the total population at the end of the year, urbanization rate, the real estate investment, the proportion of employees in secondary industries, the proportion of employees in tertiary industries, the agricultural output value, the per capita housing area in rural areas, the total power of agricultural machinery and the number of cultivated land protection policies. It is concluded that the factors influencing the change in cultivated land area in Jiangsu Province can be divided into four categories as follows: the population growth factor, the economic development factor, the rural development factor and the land policy factor.

The population growth factor has a negative effect on cultivated land change, in general. The total population of Jiangsu Province at the end of the year increased from 73.27 million in 2000 to 84.77 million in 2020, an increase of 15.7%. This growth leads to a large demand for residential land, and most of this land comes from the conversion of arable land. This conclusion is basically consistent with that proposed by Li et al. [28]. The urbanization rate increased from 41.5 percent in 2000 to 73.4 percent in 2020, with an annual growth rate of nearly 1.60 percent. Improvement in the urbanization level represents an increase in the urban population, and people’s demands for construction land are increasing, resulting in a large decrease in cultivated land. With the increase in urban population, more and more people are engaged in secondary and tertiary industries, and an adjustment of industrial structure leads to an allocation of cultivated land resources to other land uses, which is the same as the conclusion proposed by Liu et al. [29].

Rapid economic development is one of the most important driving forces for urban spatial expansion and land use change. With a continuous increase in real estate investment in Jiangsu Province in recent years, the rapid development of urban construction also has an increasing demand for land, followed by a sharp decline in the amount of cultivated land, which is consistent with the conclusion proposed by Shi et al. [30]. At the same time, the development of urbanization has driven the development of the real estate industry and its related construction and material industries, thus also driving the development of the economy. The synergistic effect of economic development and urbanization has caused the reduction in cultivated land to be more drastic. It is worth noticing that the proportion of employees in secondary and tertiary industries has an opposite effect on the changes in cultivated land, which is because the development of the manufacturing industry has a more urgent demand for land. The tertiary industry is dominated by the service industry, which is mainly concentrated in cities and has relatively concentrated industrial employees, so it does not have a great impact on the reduction in cultivated land. This research finding is basically consistent with that of Wang et al. [31].

For agricultural development, improvements in agricultural output value increase farmers’ incomes and can promote cultivated land protection. Agricultural mechanization can significantly improve the efficiency of grain production, reduce production costs and improve production benefits. In recent years, the Jiangsu provincial government has continuously increased investment in agricultural science and technology, improved agricultural production conditions, improved relevant agricultural infrastructure and supporting facilities, increased farmers’ enthusiasm for grain planting, reduced the phenomenon of rural abandoned land, alleviated the contradiction between humans and land to a certain extent and ensured food security, which is consistent with the conclusions of Liu et al. [32]. In addition, with the increase in farmers’ income, there is also a demand for improving living conditions. Some affluent farmers build houses in rural areas, resulting in an inefficient use of land resources and a waste of cultivated land resources. This conclusion is also consistent with the conclusion proposed by Wang et al. [30].

Information release and communication of cultivated land protection policies is the window for the formulation and promotion of cultivated land protection policy implementation. Only when government departments attach importance to this work can they formulate policies and strengthen policy promotion. We discovered that the number of cultivated land protection policy information releases is positively correlated with cultivated land change, reflecting the important role of cultivated land protection policy in land use change, especially cultivated land protection.

The main indicators, such as the urbanization rate, real estate investment, rural per capita housing area, agricultural output value and cultivated land protection policy, were selected for spatio-temporal and geographically weighted regression analyses. The results showed that the implementation of population, economy, rural development and cultivated land protection policies had time and space differences. After 2010, the urbanization rate in Nanjing was basically stable with little increase, so it had little impact on the cultivated land area. The effect of real estate investment on cultivated land areas is also unbalanced in the whole province, and the negative impact of real estate investment on cultivated land areas has weakened since 2010, especially in Wuxi, Changzhou, Zhenjiang, Huai’an, Yangzhou and Taizhou, which are not hot real estate markets—with the strengthening of cultivated land protection policies, the impact on cultivated land reduction is becoming less and less. The rural per capita housing area has a negative effect on the cultivated land area in all regions and periods of time in the province. The increase in agricultural output value has a positive effect on the increase in cultivated land area in all regions and periods of the province, and the effect in northern Jiangsu is less than that in central Jiangsu and southern Jiangsu, which is related to the low efficiency of grain production in northern Jiangsu. The cultivated land protection policy has a positive effect on the increase in cultivated land area, but the number of cities with a negative effect since 2010 is increasing, which indicates that the policy implementation intensity in these cities is declining.

In this paper, the change in land use in Jiangsu Province in the past 20 years is studied, and the reasons for the decrease in cultivated land are analyzed, and some useful results are obtained. The deficiency of this paper is that, first, the reasons for changes in construction land are not deeply studied, and second, the selection of independent variables of policy factors is simple. These are the directions for future research.

5. Conclusions

This study clarified the characteristics of land use change in Jiangsu Province over the past 20 years through the identification and comparison of multi-year satellite data. This paper focuses on the changes in cultivated land layout and the influencing factors that cause these changes. Through the study of population growth, economic development, rural development and land policy, this paper empirically analyzes the main factors affecting the changes in cultivated land area in Jiangsu Province and analyzes the spatial-temporal differences in cultivated land change in Jiangsu Province. The main conclusions of this paper are as follows:

(1)

The cultivated land area in Jiangsu Province showed a decreasing trend, and there were significant spatial differences in the land use change between the surrounding areas of urban centers and the outer suburbs. From the perspective of land type conversion, cultivated land was mainly transferred into construction land, water bodies and forest land, while cultivated land was mainly transferred into water bodies and collective construction land. The conversion of cultivated land into construction land is closely related to the removal of villages and the transfer of construction land in most rural areas of northern Jiangsu. The urban scale in Jiangsu Province shows an expanding trend and produces strong industrial agglomeration, which leads to the expansion of construction land and a decrease in agricultural land, such as cultivated land.

(2)

According to the regression analysis of 20 years of data on factors that may affect changes to the cultivated land area, such as the total population at the end of the year, urbanization rate, real estate investment, the proportion of employees in secondary industries, the proportion of employees in tertiary industries, agricultural output value, rural per capita housing area, the total power of agricultural machinery and cultivated land protection policy, it is concluded that the factors influencing the changes to the cultivated land area in Jiangsu Province can be divided into the four following categories: the population growth factor, the economic development factor, the rural development factor and the land policy factor. Among them, the increase in population growth and urbanization rate will exacerbate the decrease in cultivated land area, and the real estate investment and the proportion of secondary industry employees will also have a negative impact on cultivated land. The continuous improvement in agricultural production levels promotes the enthusiasm of farmers to grow grain, and has a positive effect on cultivated land protection. Cultivated land protection policy formulation and implementation have a positive effect on cultivated land area protection.

(3)

According to the analysis of the spatial-temporal heterogeneity of the changes to the cultivated land area in Jiangsu Province, the impact of urbanization development on cultivated land area has been growing stronger since 2000, indicating that Jiangsu’s urbanization is accelerating. The negative impact of real estate investment on cultivated land area in Jiangsu Province has a weakening trend, and the range of positive impacts on cultivated land area has an expanding trend, indicating that the real estate opening has been convergent in small- and medium-sized cities due to market reasons. The area of rural human housing has a negative effect on the area of cultivated land, and from the perspective of space, this effect mainly shows a decreasing trend from the north of Jiangsu to the south of Jiangsu. However, agricultural development factors and cultivated land policy protection have a positive impact on cultivated land area, and there is a certain strengthening trend, indicating that improving the level of agricultural development and strengthening policy implementation can help to strengthen the protection of cultivated land area.

Based on the above research results in this paper, the following suggestions are proposed. First, urbanization is the main driving force for the decrease in cultivated land. In the process of promoting urbanization, the most important thing is to strengthen regional spatial planning, delimit the permanent basic farmland protection zone and realize the protection of cultivated land through the strict implementation of planning, especially in southern Jiangsu. The use of stock construction land should be given priority, and the occupation of cultivated land should be reduced; improve the use efficiency of construction land and avoid blind expansion. Second, it is necessary to intensify industrial transformation, encourage the development of service industries and high-tech industries, and reduce traditional manufacturing industries that rely heavily on land resources. Optimize the layout of industrial land, promote the centralized and intensive development of industrial parks and avoid the fragmentation of cultivated land caused by the scattered layout of industrial land. Third, we need to increase financial subsidies for grain production, especially regarding support for major grain-producing areas such as northern Jiangsu, so as to improve the economic returns of farmers from grain cultivation and reduce the phenomenon of farmland abandonment. The provincial government should strengthen the construction of irrigation and water conservancy facilities, improve the quality of cultivated land, enhance the ability to resist disasters and ensure the stability of grain production. Fourth, local governments should implement the cultivated land protection policy, encourage social forces to participate in the cultivated land protection and form a conducive atmosphere for the whole of society to jointly protect cultivated land.