Quantitative Evaluation and Driving Forces of Green Transition of Cultivated Land Use in Major Grain-Producing Areas—A Case Study of Henan Province, China

Abstract

Exploring the spatiotemporal evolution and driving forces for the green transition of cultivated land (GTCL) has become an important part of the deepening research on cultivated land use transition, and has significant implications for addressing the environmental issues of agriculture development. This study took the cities in Henan province, the main grain-producing area in central China, as the research objects, and established an evaluation system for GTCL based on the subsystems of spatial, functional, and mode transition. The entropy weight method and spatial autocorrelation model were used to measure the index of GTCL and analyze the spatial pattern; then, the geographic detector model was used to explore the driving forces. The index of GTCL from 2010 to 2020 showed stable growth, exhibiting significant spatial heterogeneity with a decrease from southeast to northwest. The growth of the three subsystems of GTCL is inconsistent, with the order of index value growth being functional transition, mode transition, and spatial transition. The global Moran’s index of the index of GTCL in cities in Henan province showed positive values, indicating significant spatial dependence and spillover effects. The population density, urbanization rate, per capita GDP, and irrigation index have always been important driving forces for GTCL, and agricultural modernization would promote the GTCL in the main grain-producing areas. The research results provide a reference for exploring the path of GTCL, promoting green utilization of cultivated land and sustainable agricultural development in China’s major grain-producing areas.

1. Introduction

The development of cultivated land resources provides necessary materials for human survival and plays an irreplaceable role in agricultural production and sustainable socio-economic development. However, human demand for cultivated land resources is becoming increasingly excessive and the negative impact of sacrificing the environment to improve the output efficiency of cultivated land, especially in developing countries, is becoming increasingly prominent [1]. Research shows that the amount of fertilizer applied per hectare of cultivated land in China is four times the world average, with an annual pesticide application of up to 1.8 million tons, of which 70% are highly toxic pesticides [2]. The use of pesticides and fertilizers has led to problems such as land fertility degradation, thinning of the plow layer, and farmland pollution, which in turn seriously damages China’s high-quality and safe supply of agricultural products [3]. As the fundamental resource for agricultural development and the main carrier of grain production, the utilization status of cultivated land is crucial for the green transition of agricultural development, food security, and social stability [4]. Under the practical requirements of ecological civilization construction and sustainable agricultural development, the GTCL, through achieving green development of cultivated land resources to seek efficient output and environmentally friendly agricultural development, has become an urgent task for achieving high-quality development.

Land use transition (LUT), as a temporal and historical research framework for land use/cover change (LUCC) research that integrates socio-economic and environmental changes, represents a main scientific issue studied by the Global Land Project (GLP) [5]. LUT refers to the morphological transition process of national/regional land use over a period of time corresponding with the changes and innovations in the stage of economic and social development. Land use morphologies include dominant morphologies (quantity, structure, pattern, etc.) and recessive morphologies (quality, ownership, efficiency, function, etc.) [6]. LUT has been shown to provide significant advantages in analyzing the relationships and interactions between regional land use and socio-economic development, garnering widespread attention [7,8]. As the primary land use type in the context of rapid socio-economic development, cultivated land is more frequently converted from other land types, and its transition process has become a significant aspect of LUT. Many scholars have conducted a series of studies on the spatiotemporal characteristics [9] and driving mechanisms [10] of the cultivated land use transition, but these studies mainly focus on the dominant morphological transition of cultivated land use, with less attention given to the recessive morphological transition. In contrast to dominant morphological transition, research on recessive morphological transition is more closely related to land resource management, and the revelation and characterization of evolutionary patterns of recessive morphological transition are crucial for innovative practices in land resource management. With the emergence of ecological issues in cultivated utilization, the transition of ecological efficiency morphologies in cultivated land use has become a hot topic in research on LUT. GTCL is an extension of cultivated land use transition, which refers to the spatiotemporal transition process of national/regional cultivated land green utilization, mainly in the recessive morphologies such as functions and modes, that evolve alongside the transition of social and economic development. Compared with the traditional cultivated land use transition, the GTCL emphasizes “green development” and pursues the improvement of ecological efficiency of cultivated land use, that is, while realizing the cultivated land use transition, relying on technological progress, paying more attention to resource conservation, environmental sustainability, spatial intensification, and increased output benefits [11,12].

With the global popularity of the green transition of agricultural development, the GTCL has attracted increasing attention from scholars [13,14]. Previous studies on GTCL have mainly been conducted based on analyzing the spatial-temporal characteristics of dominant spatial morphology transition and the recessive functional morphology transition of cultivated land green utilization, and exploring its driving mechanism [15,16]. The impact of GTCL on food security [17], ecological protection [18], and socio-economic development [19] has also received widespread attention. Existing research has focused on the dynamics, interrelationships, and mechanisms of different morphologies of cultivated land green utilization, providing important references for understanding the connotation, evaluation methods, and regulatory pathways of GTCL. However, the fragmented interpretation mechanism of the existing literature has failed to reveal the core meaning of the new proposition of GTCL under high-quality development, which leads to the inability of existing research to scientifically and accurately evaluate the changes in green land use. The conclusions derived from empirical analysis are insufficient to accurately represent the impact mechanism of GTCL. Additionally, the GTCL, as an emerging extension of LUT research, lacks a mature theoretical paradigm and research framework, and requires ongoing enrichment through research practice. It is necessary to clarify the concept of GTCL and develop a measurement index system to address the gaps in GTCL research. This will help meet the evolving demands of contemporary agricultural green development and further investigate the relationship between the spatial variations in GTCL evolution and regional agricultural development preferences.

China, as the world’s most populous developing country, has long solved the dilemma of feeding over 21% of its population with 7% of the world’s arable land by overexploiting land resources [20,21,22]. This situation of cultivated land utilization cannot meet the requirements of China’s high-quality agricultural development, rural revitalization, and food security strategies, nor can it adapt to the development trend of green transition in cultivated land utilization. In addition, the irrational transition of cultivated land utilization has led to the emergence of phenomena such as abandoned farmland and non-grain conversion of farmland [23,24]. The report of the 20th National Congress of the Communist Party of China proposed to adhere to the green and low-carbon development of agriculture, and the rural revitalization strategy also emphasized the importance of promoting the comprehensive transition of agriculture towards green development [25], which has gradually made the GTCL an emerging hot topic attracting increasing interest from Chinese scholars. The research on China’s GTCL mainly focuses on establishing a measurement system for GTCL from the perspective of the “input-output” system [26], analyzing the mechanism of GTCL through spatial analysis and quantitative statistics [11]. Some scholars have also considered the path of GTCL and proposed to implement the “three in one” protection strategy of farmland quantity, quality, and ecology [27]. Scholars believe that China’s GTCL is closely related to ecology, food production, social security, and economic development [28,29]. Previous studies have mainly focused on the static efficiency evaluation of GTCL, and the research areas are also mainly concentrated in national and economic zones. There is a lack of research on the GTCL from a dynamic perspective, and also little attention paid to specific functional areas such as major grain-producing areas.

This study aims to address the shortcomings in research on the GTCL and to construct a research framework based on defining the concept and connotation of GTCL. Then, taking Henan province as a typical study area, we established a measurement index system for the GTCL to tackle the issue of highly intensive land use in traditional grain-producing areas of China. By clarifying the trends and influencing factors of the GTCL, we explored the spatial heterogeneity and driving mechanisms behind this transition. This research provides theoretical support for promoting the practice of GTCL and advancing the high-quality development of agriculture.

2. Theoretical Framework for GTCL

The cultivated land use transition is described as a trend transfer in cultivated land use from dominant spatial transition to recessive functional transition [6]. Dominant spatial transition refers to the transition of cultivated land utilization in terms of quantity and structure, while recessive functional transition refers to the different expressions in production, living, and ecology contexts, as well as the transition of management methods brought about by the manifestation of the market value of cultivated land resources. The GTCL not only emphasizes “transition”, but also pays more attention to “green”, emphasizing the coordinated development of cultivated land utilization and ecological protection [30,31]. The GTCL is a continuous improvement and innovation of the elements of the cultivated land utilization system centered on green development, and guides cultivated land utilization activities to achieve the coordinated development of traditional farmland resource utilization and green agricultural production factor allocation [26]. “Cultivated land use” includes the interaction process between people and land in rural areas, and “green transition” is an active response to the increasingly severe issues such as the ecological constraints of farmland production and food supply security, in order to promote high-quality agricultural development [32]. Therefore, the GTCL includes two meanings: firstly, “green” is the integration of cultivated land utilization and environmental protection on the basis of maintaining the natural attributes of cultivated land, achieving the goal of a virtuous cycle between cultivated land utilization and a cultivated land ecosystem through element substitution under the constraint of resource carrying capacity [14]. Secondly, “transition” refers to the deeper transformation of utilization modes, including resource conservation, technological progress, and green production, based on both dominant spatial transition and recessive functional transition. This is manifested in the large-scale management of land cultivation, modern technological innovation, and the achievement of the goal of coordinated development between farmland utilization and the environmental protection [33], ultimately achieving an organic integration of dominant spatial transition, recessive functional transition, and utilization mode transition (Figure 1).

3. Materials and Methods

3.1. Study Area

Henan province is located in central China (116°39′~110°21′ E and 31°23~36°22 N), with the Yellow River flowing through its northern part and covering a total area of 167,000 km². The terrain is high in the west and low in the east (Figure 2). Henan province enjoys vast plains in its central and eastern parts, abundant agricultural resources, and a long history of farming. It uses 1/16 of China’s cultivated land to produce nearly 1/10 of China’s grain and 1/4 of China’s wheat [34]. It is one of the 13 major grain-producing areas and the second-largest grain-producing province in China. The utilization of cultivated land in Henan province plays a significant role in ensuring national food security. In 2023, the permanent population of Henan province reached 98.15 million, the urbanization rate was 58.08%, and the regional GDP reached 816.382 billion US dollars. Henan province is currently experiencing rapid urbanization and economic development, and its agricultural production is facing a dual dilemma of growing demand for cultivated land production and the increasing loss of rural population [35,36].

Henan province is an important and historical grain production base in China, but driven by the goal of increasing production and economic benefits, unreasonable farming methods such as excessive cultivation, and heavy development over maintenance have led to problems such as soil erosion, thinning of soil layers, and the degradation of cultivated land, seriously hindering the sustainable use of cultivated land. According to data from the National Bureau of Statistics of China, in 2021, the total area of crop sowing in Henan province accounted for 8.72% of the total arable land in China; the amount of fertilizer used was 6.2466 million tons, accounting for 12.03% of the national total, and the amount of pesticide applied was 97.40 thousand tons [37]. The excessive use of fertilizers and pesticides has led to increased agricultural non-point source pollution and strengthened constraints on agricultural production resources. Given the important strategic position of Henan province in ensuring national food security, and in the new stage of national food security moving towards a higher demand for equal emphasis on quantity and quality, yield and production capacity, and food production and ecological protection in cultivated land utilization, analyzing the spatiotemporal pattern and driving factors of GTCL in Henan province is of great significance for further deepening the high-quality green agricultural development in China.

3.2. Data Sources

The main data used in this study are the socio-economic statistics related to agricultural production (such as population, grain yield, agricultural mechanization, agricultural output value, etc.) in Henan province and the cities under its jurisdiction, mainly from the 2011–2021 Henan Statistical Yearbook and the statistical yearbooks of relevant cities. Rainfall, agricultural irrigation, etc. were sourced from the official Henan Water Resources Bulletin over the years, the third national land survey data bulletin, etc. The geographic spatial data used comes from vector data provided by the National Geographic Information Resource Catalog Service System (http://www.webmap.cn, accessed on 12 November 2024).

3.3. Methods

3.3.1. Entropy Weight Method

The entropy weight method is used to determine the weights of each indicator in a multi-indicator evaluation system. The core idea is to measure the information content of each indicator based on the size of information entropy, and to determine the importance of the indicator accordingly. The smaller the information entropy, the greater the amount of information and the higher the weight should be. This method can prevent the interference of subjective factors, making the determination of weights more scientific and objective. The calculation steps of the entropy weight method for evaluating the GTCL are as follows: constructing an indicator system, standardizing the indicator data, calculating the information entropy, and then determining the indicator weights based on the information entropy (Figure 3).

On the basis of in-depth exploration of the connotation of GTCL, we closely combined the positioning of Henan province in the national strategy for food security, fully considered the availability of data, and referred to existing research results to construct an evaluation index system for GTCL in Henan province [32,38,39] (

Table 1. Evaluation index system for GTCL.

Targets	Factors	Indicators	Attribute	Illustration	Weight
Spatial transition	Quantity	Per capita cultivated land	+	Cultivated land area/total population	0.0505
		Land reclamation rate	+	Cultivated land area/total area	0.0678
	Structure	Grain crop sowing ratio	+	Grain crop sowing area/cultivated land area	0.0878
		Multiple-crop index	+	Crop sowing area/cultivated land area	0.062
Functional Transition	Production	Grain yield per ha	+	Grain yield/sowing area of grain crops	0.0711
		Agricultural output value per ha	+	Agricultural output value/cultivated land area	0.1371
	Living	Per capita grain output	+	Grain production/total population	0.0759
		Proportion of agricultural employment population	+	Agricultural employment population/total employment population	0.0486
	Ecological	Population carrying capacity per ha	−	Rural population/cultivated land area	0.0265
		Fertilizer usage per ha	−	Fertilizer usage/cultivated land area	0.1219
Mode transition	Green production	Proportion of water-saving irrigation	+	Water-saving irrigation area/cultivated land area	0.0748
	Technological innovation	Agricultural machinery power per ha	+	Total power of agricultural machinery/cultivated land area	0.0648
	Eco-friendly	Organic fertilizer input intensity	+	Green manure sowing area/cultivated land area	0.0656
		Pesticide use per ha	−	Pesticide usage/cultivated land area	0.0454

The attribute value of “+” indicates a positive correlation, while “−” indicates a negative correlation.

). The spatial transition reflects the dominant morphological changes in the utilization of cultivated land, and constructing a spatial transition with dual attributes of quantity and structure can improve the resilience of the evaluation system. Therefore, indicators were selected from both the quantity and structure of cultivated land use. The land reclamation rate can characterize the overall level of cultivated land use in the study area, and the per capita cultivated land area reflects the situation of cultivated land use at the individual level. The functional transition mainly reflects the recessive morphological changes in the utilization of cultivated land. Indicators were selected from the perspectives of production function, living function, and ecological function. Among them, the grain yield per ha and the per capita agricultural output value are the direct manifestations of production function of cultivated land. The transition of living function of cultivated land is reflected in the guarantee of food security and agricultural labor employment. The transition of ecological function of cultivated land is mainly characterized by the degree of pollution and changes in carrying capacity of cultivated land. The mode transition is particularly evident in multiple dimensions such as eco-friendly, technological innovation, and green production. To quantify its specific characteristics, our study selected key evaluation indicators such as agricultural machinery power per ha, effective irrigation rate, and pesticide use per ha. The established indicator system not only reflects the significant improvement of cultivated land utilization towards modernization and intensification, but also provides a quantitative evaluation of the GTCL.

Due to the magnitude, dimension, and positive/negative differences between indicators, in order to eliminate the impact of these differences on data analysis, we standardized the collected data and then used the entropy weight method to determine the weights.

Positive indicators:

$$y_{ij}=\left(x_{ij}-{minx}_{ij}\right)/\left({maxx}_{ij}-{minx}_{ij}\right)$$

(1)

Negative indicator:

$$y_{ij}=\left({maxx}_{ij}-x_{ij}\right)/\left({maxx}_{ij}-{minx}_{ij}\right)$$

(2)

where x_ij and y_ij are the original and standardized values of the i-th (i = 1, 2, ∙∙∙, m) year and j-th (j = 1, 2, ∙∙∙, n) indicator, respectively; and maxx_ij and minx_ij are the maximum and minimum values of the j-th indicator, respectively. The formula for calculating the information entropy of the j-th indicator is as follows:

$$p_{ij}=y_{ij}/{\sum }_{i=1}^m{y}_{ij}$$

(3)

$$k=1/\ln m$$

(4)

$$e_j=-k{\sum }_{i=1}^m{p}_{ij}\ln p_{ij}$$

(5)

where p_ij is the proportion of the standardized value of the j-th indicator in the i-th year, m represents the total number of years involved in the evaluation, and e_j is the information entropy of the j-th indicator. We calculate the anisotropy coefficient and weight from e_j using the following formulas:

$$g_j=1-e_j$$

(6)

$$w_j=g_j/{\sum }_{j=1}^n{g}_j$$

(7)

where g_j is the coefficient of difference for the j-th indicator, and w_j is the weight of the j-th indicator. After determining the weights of each indicator using the entropy weight method, we multiply them with the standardized indicator values to obtain the contribution of each indicator to the GTCL. The calculation formula is as follows:

$$v_i={\sum }_{j=1}^n{w}_j{y}_{ij}$$

(8)

where i = 1, 2, 3, ∙∙∙, m.

3.3.2. Spatial Autocorrelation Model

The core of spatial autocorrelation analysis lies in calculating the spatial autocorrelation coefficient, exploring the intrinsic connections between various things in geographic space, and presenting the analysis results through visualization methods in order to clarify their patterns and trends [40]. This study used spatial statistical tools in ArcGIS software 10.8 to calculate the global Moran’s index and analyze the spatial clustering of GTCL. The calculation formula is as follows:

$$I={\displaystyle \frac{K}{{\sum }_{s-1}^K{\sum }_{t-1}^K{W}_{st}}}\times {\displaystyle \frac{{\sum }_{s-1}^K{\sum }_{t-1}^K{W}_{st}\left(x_s-\overline{x}\right)\left(x_t-\overline{x}\right)}{{\sum }_{s=1}^K{\left(x_s-\overline{x}\right)}^2}}$$

(9)

where x_s and x_t, respectively, represent the attribute values of the i-th and j-th spatial units, which are the index of GTCL; $\overline{x}$ is its average value; and K is the total number of spatial units. The definition of W_st is based on geographical location: if two regions are geographically adjacent, their corresponding W_st value is 1; on the contrary, if it is not adjacent, the value of W_st is 0.

The global spatial autocorrelation test cannot delve into the local spatial clustering characteristics under the influence of spatial heterogeneity. Therefore, it is necessary to further use the local Moran index for calculation. The local autocorrelation index LISA can test the heterogeneity changes in data calculation, and its calculation formula is as follows:

$$I_{local}={\displaystyle \frac{K\left(x_s-\overline{x}\right){\sum }_{t=1}^K{W}_{st}\left(x_t-\overline{x}\right)}{{\sum }_{s=1}^K{\left(x_s-\overline{x}\right)}^2}}$$

(10)

When $I_{local}>0$, it indicates that there is a small difference in the index of GTCL between the region and adjacent areas, and there is a spatial positive correlation. Specifically, if the index value of the region is high and relatively high compared to the surrounding areas, it belongs to the “high–high” type; on the contrary, if the index value of the region is low and relatively low compared to the surrounding areas, it belongs to the “low–low” type. When $I_{local}<0$, it indicates that there is a significant spatial difference in the index of GTCL between the region and its neighboring areas, indicating a negative spatial correlation. Specifically, if the index value of the area is higher than that of the surrounding area, it belongs to the “high–low” type; on the contrary, if the index value of the area is lower than that of the surrounding area, it belongs to the “low–high” type.

3.3.3. Geographic Detector Model

The geographic detector model, a powerful analytical tool, aims to deeply explore and effectively utilize the heterogeneity in geographic space. Its core components include four aspects: risk detection, factor influence detection, ecological detection, and multi factor interaction detection [41]. This study used the factor influence detection function to identify factors that have a significant impact on the GTCL. Using the K-means method in SPSS Statistics 2024 software to discretize numerical variables and import them into a geographic detector [16], the influence value q of different factors on the GTCL were obtained. Due to the mutual independence between different driving factors, this study selected the method with the highest q value as the optimal parameter for the geographic detector [17,18]. The calculation formula is as follows:

$$q=1-{\displaystyle \frac{1}{N{\sigma }^2}}{\sum }_{h=1}^L{N}_h{\sigma }_h^2$$

(11)

where N and ${\sigma }^2$ represent the number of sample units and variance in the entire study area, and $N_h$ and ${\sigma }_h^2$ represent the number of sample units and variance in the h-th layer (h = 1, 2, ∙∙∙, L). The value range of q is limited to [0, 1], and its magnitude is positively correlated with the strength of its impact on the GTCL.

4. Results

4.1. The Index of GTCL

The value of the index of GTCL in various cities in Henan province is relatively low and generally below 0.6 from 2010 to 2020, but shows an accelerating growth trend (

Table 2. The index of GTCL in various cities of Henan Province from 2010 to 2020.

Cities	2010	2015	2020
Zhengzhou	0.33	0.37	0.46
Kaifeng	0.54	0.59	0.69
Luoyang	0.36	0.4	0.51
Pingdingshan	0.34	0.34	0.46
Anyang	0.46	0.5	0.58
Hebi	0.53	0.58	0.66
Xinxiang	0.43	0.47	0.58
Jiaozuo	0.47	0.53	0.62
Poyang	0.51	0.57	0.68
Xuchang	0.47	0.53	0.67
Luohe	0.53	0.58	0.65
Sanmenxia	0.3	0.38	0.52
Nanyang	0.4	0.43	0.58
Shangqiu	0.53	0.53	0.69
Xinyang	0.5	0.5	0.62
Zhoukou	0.54	0.58	0.67
Zhumadian	0.54	0.56	0.68
Jiyuan	0.35	0.38	0.46
Average	0.45	0.49	0.6
Range	0.24	0.25	0.23

). The average value of the index of GTCL in 2010 was 0.45, which increased to 0.49 in 2015 and significantly increased to 0.60 in 2020. In the two time periods of 2010–2015 and 2015–2020, the index of GTCL increased by 8.72% and 23.25%, respectively, indicating significant progress in GTCL in Henan province. During the research period, the index of GTCL in Zhumadian and Kaifeng was at a relative high in various cities. Both Zhumadian and Kaifeng are located in the hinterland of the North China Plain, with superior cultivated land resources and a good foundation for agricultural development. They have laid a good foundation for the GTCL by constantly challenging the cultivated land planting structure and developing the agricultural green economy. Zhengzhou and Pingdingshan both have a relatively low index of GTCL. Pingdingshan is a resource-based city, and the mining of mineral resources has caused problems such as farmland pollution and damage to the cultivated layer, resulting in low efficiency in cultivated land utilization; Zhengzhou is the economically developed provincial capital city, and its economic development occupies a large amount of cultivated land. In order to achieve high yields from the limited remaining cultivated land resources, a significant amount of fertilizers and pesticides were used. These factors have reduced the index of GTCL and slowed down the green transition process in Zhengzhou and Pingdingshan. The range of the index of GTCL in Henan province in 2010, 2015, and 2020 was 0.24, 0.25, and 0.23, respectively. The range gradually narrowed, indicating that the differences in GTCL in the study area were continuously decreasing. This suggests that in the process of promoting GTCL, different cities in Henan province are gradually achieving balanced development, and the overall development trend is relatively stable.

4.2. Spatiotemporal Characteristics of GTCL

4.2.1. The Spatiotemporal Differences in GTCL

From 2010 to 2020, the index of GTCL of various cities in Henan province showed significant spatial differentiation, with an overall upward trend and a spatial pattern characterized by highs in the east and lows in the west, and a decreasing trend from southeast to northwest (Figure 4). The high-value areas are mainly distributed in the southeast, such as Kaifeng, Zhoukou, Zhumadian, etc., indicating that these cities are leading in the GTCL. The low-value areas are mainly concentrated in the central and western cities, such as Zhengzhou and Pingdingshan, and the process of GTCL is relatively lagging behind. During the study period, there was a significant increase in the index of GTCL in various cities. In 2010, seven cities had an index below 0.45. By 2020, the index of GTCL of all cities had exceeded 0.45, and more than half of the cities had an index exceeding the high level of 0.61.

The differences in spatial transition, functional transition, and mode transition of subsystems in the GTCL can reflect the mutual feedback differences between cultivated land utilization and nature, economy, input-output, etc. The spatial evolution of the three subsystems of GTCL from 2010 to 2020 exhibited different spatial heterogeneity (Figure 5).

(1)

Spatial transition: the spatial transition presents a difference in being higher in the northeast and lower in the southwest. The spatial transition index values of places such as Sanmenxia and Luoyang in the west have been consistently low, while Zhoukou in the east has been consistently high. The spatial transition index of various cities has generally increased, with an average increase of 0.10, among which Zhoukou, Xuchang, and Puyang show the most significant growth.

(2)

Functional transition: the overall trend of functional transition is high in the south and low in the north. The functional transformation index values of Zhengzhou and Pingdingshan have always been low, while the value of Xinyang in the south has always been high. The functional transformation index of various cities in Henan province has grown significantly, with an average increase of 0.18. Among them, the growth rates of Sanmenxia, Jiaozuo, and Shangqiu have all exceeded 0.2.

(3)

Mode transition: mode transition presents a spatial pattern of high in the north and low in the south. The model transition index values of Sanmenxia, Nanyang, and Luoyang in the southwest have always been low, while the values of Puyang, Hebi, and Jiyuan in the north have always been high. Unlike spatial transition and functional transition, the model transition index of various cities in Henan province has not been consistently increasing. From 2010 to 2015, the model transition index values of eight cities including Xinyang, Sanmenxia, and Pingdingshan showed a decline, but by 2020, the values of each city had shown a significant increase. The average value of the model transition index in various cities from 2010 to 2020 increased by 0.11, with Zhengzhou, Xuchang, and Luoyang showing the largest growth.

4.2.2. The Spatial Agglomeration of GTCL

Using ArcGIS software to calculate the global Moran’s index of the index of GTCL for various cities in Henan province in 2010, 2015, and 2020, the results were 0.5571, 0.4674, and 0.3580, respectively, and all passed the 1% significance level test, indicating that the index of GTCL in the 18 cities in Henan Province is not isolated or randomly distributed, and there is spatial dependence and spillover effect among them. The global Moran’s index of the index of GTCL has been continuously decreasing, indicating that the spatial agglomeration of the index of GTCL in various cities in Henan province is continuously weakening.

To further analyze the intrinsic connections and changing patterns of the GTCL in the research area, a local autocorrelation analysis was conducted on the GTCL in 18 cities in Henan province, and a LISA clustering map of GTCL was obtained (Figure 6). By analyzing the spatiotemporal changes and differentiation patterns of high–high or low–low and low–high or high–low in cultivated land utilization, it is convenient to study the spatial evolution characteristics of GTCL in Henan province.

Overall, there are more non-significant cities than significant cities in the GTCL, and significant cities are mainly characterized by high–high spatial agglomeration types. The cities located in the eastern part of Shangqiu and Zhoukou are characterized by a high–high spatial agglomeration type. Zhoukou and Shangqiu are the core grain-producing areas in central China. Zhoukou is a national level agricultural high-tech demonstration zone, while Shangqiu is under the jurisdiction of multiple national level grain growing counties and national level agricultural industrialization leading enterprises. They play as a leading and demonstrative role in agriculture green development in surrounding areas and have become hot spots in the GTCL. The spatiotemporal differentiation characteristics of the low–low agglomeration type are not significant. Luoyang has always been a low–low agglomeration type, and in 2015, Nanyang also showed a low–low agglomeration type. Luoyang and Nanyang are located in the mountainous and hilly areas of southwestern Henan province. Due to location and terrain limitations, their agricultural development is relatively backward, with low grain production and agricultural mechanization, making them lag behind in the GTCL in the surrounding areas. The high–low agglomeration types only occurred consistently in Jiaozuo; low–high agglomeration types did not occur during the study period.

4.3. The Driving Factors for the GTCL

4.3.1. Selection of Driving Forces

The evaluation index system for GTCL considers its green transition from the inherent nature and utilized attributes of cultivated land. The spatiotemporal evolution of GTCL is effective feedback of its external environmental mechanism. Therefore, it is necessary to further explore its driving forces from natural environmental conditions, socio-economic development, agricultural development process, etc. Based on previous research [21,37], we selected indicators from the following aspects to analyze the driving forces of GTCL (

Table 3. The driving forces for the GTCL.

Factors	Indicators	Explanation of Indicators
Social	Population density (X1)	Population/total area
	Urbanization rate (X2)	Urban population/total population
Economic	Per capita GDP (X3)	GDP/total population
	Proportion of agricultural output value (X4)	Agricultural GDP/Total GDP
Environmental	Rainfall (X5)	Annual rainfall
	Temperature (X6)	Annual temperature
Agricultural Modernization	Agricultural mechanization (X7)	Total power of agricultural machinery/sowing area of crops
	Irrigation index (X8)	Effective irrigation area/cultivated land area

).

In terms of social factors, this study has specifically chosen two indicators: population density and urbanization rate. The high population density puts pressure on the ecological and resource utilization of regional cultivated land, thereby exacerbating the tense human–land relationship [28,42]. Urbanization would lead to the gradual transfer of labor to non-agricultural industries, thereby changing the mode of agricultural production tending towards scale expansion and mechanization [43]. This transition not only improves agricultural production efficiency, but also affects the GTCL.

In terms of economic factors, two indicators are selected: per capita GDP and the proportion of agricultural output value. Under different levels of economic development, there will be significant differences in the quantity and scale of input of production factors in cultivated land, which directly affects the endowment and allocation efficiency of cultivated land resources, and affects the GTCL [44]. The proportion of the agricultural output value in a certain region directly reflects its level of agricultural economic development, which affects the recognition and acceptance of green land use and sustainable ecological protection by the users of cultivated land, and directly promotes the GTCL [45].

In terms of environmental factors, rainfall and temperature are selected as the two indicators. Natural conditions are the fundamental factors that affect the pattern and potential of green utilization of cultivated land. Natural factors such as temperature and precipitation are the most basic influencing factors in grain production, affecting the internal elements and material circulation and energy exchange with the external elements of the cultivated land utilization system, changing the adjustment and functional play of various elements in cultivated land utilization [46,47], and thus affect the GTCL. Although natural factors such as soil and light are closely related to cultivated land use, their data have weak intuitive measurement, so they were not selected.

In terms of agricultural modernization factors, two indicators were selected: agricultural mechanization and irrigation index. The improvement of these two indicators will increase production efficiency and improve the effective utilization of cultivated land, which play an important role in promoting agricultural modernization and sustainable development, and directly promote the transition of green utilization of cultivated land [48,49].

4.3.2. Driving Forces for GTCL

We discretize the above indicators using the K-means method in SPSS software and import the results into the geographic detector model. We obtain the impact values of various factors on the GTCL in 2010, 2015, and 2020, and explore the temporal variation characteristics of different factors (

Table 4. Analysis results of GeoDetector.

Indicators	2010		2015		2020
	q Value	Rank	q Value	Rank	q Value	Rank
X1	0.8425 *	1	0.4883 *	5	0.6896 *	1
X2	0.7425 *	2	0.5012 *	3	0.6749 *	3
X3	0.6721 *	4	0.4963 *	4	0.6834 *	2
X4	0.4412 *	6	0.3736 *	7	0.5757 *	6
X5	0.3906 *	7	0.6981 *	1	0.3932 *	8
X6	0.3523 *	8	0.2856 *	8	0.6412 *	5
X7	0.5110 *	5	0.4541 *	6	0.5656 *	7
X8	0.7037 *	3	0.6972 *	2	0.6594 *	4

‘*’ indicates passing the 1% significance level test.

).

During the study period, population density, urbanization rate, per capita GDP, and irrigation index have always played an important driving role in the GTCL. Rainfall was the top driving factor for GTCL ranking in 2015, but in 2020, with the improvement of modern agriculture, the driving effect of rainfall on GTCL decreased. The implementation of strict regulations on farmland protection, coupled with the core position of grain production areas, has led decision-makers in Henan province to invest more attention in the GTCL, thereby promoting the modernization of agricultural production and having a profound impact on achieving GTCL. With the continuous deepening of urbanization, rural residents are increasingly inclined to migrate to cities, which has led to a decreasing rural labor force that cannot meet the needs of traditional small-scale farming. This phenomenon has had a profound impact on traditional agricultural production methods, promoting the transformation of cultivated land utilization towards greater scale and mechanization. This transformation not only improves the efficiency of agricultural production, but also promotes the rational allocation and efficient use of agricultural resources, which has a significant impact on the GTCL. The flat terrain and relatively sparse distribution of rural population in Henan province provide unique conditions for the widespread promotion of agricultural mechanization production. Among the various influencing factors, the irrigation index has consistently ranked among the top. This is because the precipitation in Henan province is relatively low and the spatial and temporal distribution is uneven. Most areas of Henan province rely heavily on irrigation for agricultural production. Given the terrain characteristics and agricultural production needs of Henan province, improving the effective irrigation rate has become a key measure to enhance grain production efficiency, which has affected the GTCL. The low per capita GDP and relatively backward economic development in Henan province directly affect the investment and technology in the development of cultivated land resources, and have an impact on the GTCL. The synergistic effect of these factors has had a profound impact on the GTCL in Henan province, and it is necessary to comprehensively consider and take corresponding measures to promote the process of GTCL.

5. Discussion

5.1. The Effectiveness and Evolution of GTCL

The GTCL is an extension of research on LUT centered on the concept of green development. The GTCL is not only the evolution and analysis of cultivated land use morphologies, but also a scientific approach aimed at enhancing the efficiency of cultivated land green utilization based on the understanding of the existing level of green utilization in cultivated land [50]. This study took Henan province, a major grain-producing and intensive agricultural area in China, as a case study to develop a research framework for the GTCL, and analyzed the level and evolution of GTCL. The findings indicate that the index of GTCL in the study area is relatively low but shows a continuous improvement trend, which is consistent with the research results of Ke (2024) and Gao (2023) conducted on the GTCL in the Yangtze River Economic Belt and Northeast China [51,52], respectively. Unlike other regions where the index of GTCL has consistently remained below 0.5 [12,53], Henan province exhibits a relatively high index, with most cities surpassing 0.6 in 2020. As a traditionally intensive agricultural area, Henan province has long grappled with limited cultivated land resources and scarce irrigation water. Its cultivated land use demonstrates higher output efficiency and greater water-saving utilization, contributing to China’s GTCL. We also found that the primary factor contributing to the improvement of the GTCL is functional transition, followed by pattern transition and spatial transition. Functional transition exhibits a trend of being higher in the south and lower in the north, whereas pattern transition and spatial transition tend to be more pronounced in the north and less so in the south. This discrepancy may be attributed to the climatic differences in Henan province, with the south experiencing high temperatures and humidity, while the north faces drought and colder conditions. Compared to existing research that primarily focuses on the singular evolution of GTCL, this study deeply reveals the spatiotemporal evolution of its subsystems, which helps to further explore the mechanisms underlying the GTCL and in adopting differentiated regulatory strategies. These findings can provide theoretical guidance and a reference for the practice of GTCL.

5.2. Implications for Cultivated Land Green Use

The GTCL is the result of the game between the spatial, functional, and mode systems of cultivated land use within a certain period of time. It is a direct reflection of the regional cultivated land utilization status and an external expression of the coordinated development of cultivated land utilization and natural ecology, presenting specific spatiotemporal evolution laws. The research results show that great progress has been made in the GTCL in Henan province during the study period, but further coordinated development is still needed. It is necessary to accelerate the process of GTCL and promote the balanced evolution of GTCL among cities. The policy implications for cultivated land green use are as follows:

(1)

Strengthen the linkage effect and coordination relationship among the three subsystems of space, function, and mode of GTCL. The GTCL in Henan province is constantly advancing, but there is still room for improvement. It should actively seek a balancing point between the spatial, functional, and mode transitions of cultivated land and the productivity of cultivated land, refine the design of green transition paths for cultivated land utilization, and maximize the linkage effects between various subsystems. Various cities in Henan province should improve the mechanism for green utilization of cultivated land, promote green agricultural production, and stimulate the coordinated development of spatial, functional, and systematic transitions of cultivated land utilization.

(2)

Implement differentiated green utilization strategies for cultivated land that are tailored to local conditions. For example, the green transition process of cultivated land utilization in cities such as Zhengzhou and Luoyang, which have developed urbanization and industrialization, is relatively slow, while cities such as Zhumadian and Zhoukou, which are relatively economically backward, are leading in the process of GTCL. For cities with a developed industrial economy and urbanization, it is necessary to transform the agricultural development according to actual land demand, introduce ecological and organic agriculture from the perspective of regional food ecological security to internalize the external costs of occupying cultivated land, strengthen cultivated land protection through optimizing the green utilization mode and functional transition of cultivated land, and change the situation where the ecological service value of cultivated land is lower than the price of agricultural products. For cities with a high proportion of agricultural output value, it is necessary to promote the transition of agricultural production from traditional agriculture to modern agriculture, balance ecological and economic benefits, and achieve coordinated development between humans, land, and ecology.

(3)

Stimulate the intrinsic vitality of the driving mechanism for GTCL. Economic and social factors are the main driving forces for the GTCL. Only by ensuring the stable development of agricultural production can they provide impetus for the GTCL. It should continue to promote the upgrading of ecological technology in agricultural production, strictly constrain the utilization mode of cultivated land with low ecological benefits, and give full play to the role of agricultural technology in promoting the GTCL. It should enhance agricultural mechanization to promote the efficient utilization of cultivated land, stimulating the driving role of economy, society, environment, and population in the GTCL. It should accelerate the GTCL through the development of agricultural ecology, modernization, and industrialization.

5.3. Limitations and Future Research

The spatiotemporal characteristics of the gradual increase in the index of the GTCL in Henan province are in line with the requirements of the sustainable development goals for cultivated land [52,53]. This study proposed targeted strategies for future transition by analyzing the driving forces of the spatiotemporal evolution of GTCL in Henan province, providing theoretical support for the GTCL in China’s major grain-producing areas. However, there are still some limitations in the study that need further research in the future. (1) The coupling mechanism between the spatial morphologies and functional morphologies of cultivated land utilization still needs further exploration. In the process of GTCL, different morphologies are interrelated and interact with each other. Therefore, it is necessary to incorporate the internal interaction mechanism of the transition morphologies into the basic theoretical research framework of GTCL in the future. (2) The scale of research on GTCL needs to be further enriched. There are significant spatial differences in the process of GTCL. To compare the process of GTCL in different regions, research in counties, townships, and even villages can be expanded, especially in typical case studies of urban–rural junctions where land use changes are severe and the situation of cultivated land protection is severe.

6. Conclusions

This study clarified the connotation of GTCL and established a comprehensive research framework. A measurement index system for the GTCL was developed, encompassing three subsystems: spatial transition, functional transition, and mode transition. Using Henan province as a case study, the research examined the spatiotemporal evolution and driving forces behind GTCL in China’s major grain-producing areas. The findings contribute to the practical understanding of GTCL and hold significant value for advancing the comprehension of GTCL, promoting sustainable cultivated land use, and fostering high-quality agricultural development. The main research conclusions are as follows:

(1)

The index value of GTCL from 2010 to 2020 was relatively low but showed a continuous upward and stable growth trend, indicating significant progress in the GTCL in Henan province, and there were obvious differences between different cities. The index of GTCL in Kaifeng is the most prominent, while Zhengzhou is relatively low. The index of Kaifeng’s GTCL has always been more than 1.5 times that of Zhengzhou.

(2)

The GTCL in Henan province shows a significant trend of spatial agglomeration, especially in the east–west direction, showing obvious differences and differentiation. And, it has significant spatial dependence and spillover effects. Cities with high index values are mainly concentrated in the southeast of Henan province, while cities in the northwest maintain medium and low index values.

(3)

The three subsystems of GTCL have all achieved significant but inconsistent improvements. The spatial transition index has increased by an average of 0.1, showing a spatial trend of being higher in the northeast and southwest regions. The functional transition index has increased by an average of 0.18, showing a spatial trend of high in the south and low in the north. The model transition index of various cities showed fluctuations from 2010 to 2015, with an average growth of 0.11 from 2010 to 2020, presenting a spatial pattern of high in the north and low in the south.

(4)

The GTCL in Henan Province is gradually promoted and achieved through the interweaving and joint effects of multiple factors such as social change, economic development, environmental protection, and agricultural modernization. The population density, urbanization rate, per capita GDP, and irrigation index are key influencing factors on the GTCL.