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Article

Study on the Impact of Spatiotemporal Changes in the Ecological Environment on Grain Crops in the Subtropical Monsoon Climate Zone

by
Xiaokang Zuo
1,
Rui Zhi
1,
Ruiqian Tang
1,
Hanxi Wang
1,2,3,* and
Shuying Zang
1,2
1
Heilongjiang Province Key Laboratory of Geographical Environment Monitoring and Spatial Information Service in Cold Regions, School of Geographical Sciences, Harbin Normal University, Harbin 150025, China
2
Heilongjiang Province Collaborative Innovation Center of Cold Region Ecological Safety, Harbin Normal University, Harbin 150025, China
3
Jilin Shuangqi Environmental Control Co., Ltd., Changchun 130021, China
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(23), 10301; https://doi.org/10.3390/su162310301
Submission received: 8 September 2024 / Revised: 5 November 2024 / Accepted: 9 November 2024 / Published: 25 November 2024
(This article belongs to the Section Environmental Sustainability and Applications)
Browse Figures
Figure 1
<p>Flowchart of the proposed method.</p> "> Figure 2
<p>Simulation results for China’s subtropical monsoon climate zone (ND, Natural development scenario; UD, Urban development scenario; CP, Cultivated protection scenario; EP, Ecological protection scenario).</p> "> Figure 3
<p>Simulation results of HQ in the subtropical monsoon climate zones of China in 2030 (ND, Natural development scenario; UD, Urban development scenario; CP, Cultivated protection scenario; EP, Ecological protection scenario).</p> "> Figure 4
<p>Accuracy evaluation and contribution rate of environmental variables in Maxent.</p> "> Figure 5
<p>PHS for food crops under different scenarios in 2030 (Note: The data in each sector represents the percentage of area of each type in the corresponding scenario).</p> "> Figure 6
<p>Spatial distribution and cultivation area distribution of food crops under different scenarios. (Note: The data in each sector represents the percentage of area of each type in the corresponding scenario).</p> "> Figure 7
<p>HQCI values for land use transfers in 2030 under different scenarios (ND, Natural development; UD, Urban development; CP, Cultivated protection; EP, Ecological protection).</p> "> Figure 8
<p>PHSCI for land use transfers for different land uses (Note: A. SSP1-2.6, B. SSP2-4.5, C. SSP3-7.0, D. SSP5-8.5, I. ND, II. UD, III. CP, IV. EP, 1. cultivated lands, 2. forests, 3. grass, 4. water, 5. construction lands, 6. bare lands).</p> ">
Versions Notes

Abstract

:
Global warming and land-use type shifting lead to the degradation of natural habitats. The research on the ecological and environmental impact of the subtropical monsoon climate zone on food crop cultivation is not systematic enough. An Integrated Valuation of Ecosystem Services and Trade-offs (InVEST)–Patch-generating Land Use Simulation (PLUS)–Maximum Entropy (MaxEnt) model was created to provide a comprehensive assessment of the spatiotemporal variations for food crop habitat quality (HQ) in China’s subtropical monsoon climate zone from 2010 to 2030. The HQ degradation trend was obvious during 2010–2030 under the influence of land-use change. The expansion of lower habitat areas was larger than that of medium and higher habitat areas. The shared socio-economic pathways SSP-CP and SSP-EP increased the mean total area of suitable areas compared with the SSP-ND scenario for food crops by 9% and 17.8%, respectively. Land-use shifts increased the suitable range of food crops and mitigated the negative impacts of urban expansion on food growth. This research has theoretical guidance for land-use planning for food crop production in subtropical monsoon climate zones.

1. Introduction

Global warming and human activities change the land-use layout and the distribution pattern of plant habitats, which lead to increases in habitat fragmentation for food crops [1]. This causes habitat quality (HQ) degradation [2]. Food crop yield reductions have become more common because of global warming and land-use pattern changes [3,4]. The subtropical monsoon climate region is the main production area of food crops globally. The frequent occurrence of meteorological disasters and extreme weather lead to a decline in the quality of crop habitats due to large inter-annual variations in climatic conditions and a large reduction in the yields of major food crops (wheat, rice, and maize) [5,6,7]. Food security in China as well as the world is threatened. Land-use change also has an important influence on altering crop HQ [8,9] and changing species diversity [2,10,11]. Land-use change can alter regional habitat suitability, which leads to environment changes to plants’ suitable habitats [12]. There is a scarcity of comprehensive research on the integrated assessment of the effects of global climate change and land-use change on the regional distribution of food crops. The subtropical monsoon climate zone is densely populated and economically developed, and the ecological environment is greatly affected by external conditions [13,14]. The ecological environment in the subtropical monsoon climate zone affects the cultivation of grain crops [15]. A systematic assessment of future trends in the ecological environment of subtropical monsoon climate zones is important for regional habitat optimization and food crop cultivation, among other things.
Land-use change has a significant impact on regional climate and habitat-quality change [16]. The occupation of ecological land (forest, grassland, watershed, etc.) by construction land causes landscape pattern fragmentation and ecosystem imbalance, which has an impact on regional headquarters [17,18]. The majority of prior studies have been limited to qualitatively examining the influence of land-use variation on the biological environment at the provincial/county level [8,19], while in-depth studies on quantitatively assessing the effect of land-use change for the ecological environment at the cross-regional scale are lacking. Cellular automata (CA)–Markov [20], CLUE-S [21], FLUS [22], and others are widely used to simulate future land-use patterns. The foregoing prediction methods make it difficult to identify probable land-use change causes and dynamically capture the evolution of land-use patches. The study of the Patch-level Land-use Simulation (PLUS) model, which generates land-use change simulations based on raster patch data, has integrated the Land Expansion Analysis Strategy (LEAS) and the CA Model based on Random Seeds of Multiple Classes (CARS), significantly improving the accuracy of the model’s spatial distribution simulation [23]. The PLUS model can simulate land use under various development scenarios, which compensates for the inadequacies of prior models that cannot repeat predictions [24].
The traditional method of HQ evaluation was obtaining HQ parameters through field measurements to construct HQ evaluation indexes [25], but it was not easy to obtain HQ parameters using this method at large-scale range, so domestic and foreign scholars often use models such as Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) [26], Social Values for Ecosystem Services (SoLVES) [27], and species distribution modeling (SDM) [28] to evaluate HQ. Compared with other models, InVEST is widely used in regional HQ evaluation due to its advantages of low data requirement, fast operation speed, and high evaluation accuracy [26,29]. The model evaluates HQ by analyzing the different land-use development scenarios and the threat degree to biodiversity from different land uses. Meanwhile, domestic and foreign scholars have also investigated the impacts of land-use changes on HQ in different development scenarios by means of InVEST model scenarios and the impact of land-use changes on HQ with the help of InVEST [30]. Fewer studies have investigated the relationship between HQ and prospective habitat suitability (PHS) with particular attention dedicated to simulating the effects of climate change and land-use change on food crops. Currently, the most effective method for predicting crop PHS is SDM, which uses numerical methods to estimate the possible distribution of species based on the relationship between species distribution information and environmental features [31]. Geographic information technology has led to the creation of various species distribution models. The Generalized Additive model and maximum entropy (Maxent) are used to predict crop PHS [32]. The Maxent model can obtain higher fitting results with a smaller sample capacity, which has a significant advantage in prediction accuracy over other models. The prediction accuracy of Maxent has been evaluated and found to be highly applicable in the assessment of cropland suitability. Therefore, the Maxent model has been frequently used to anticipate the prospective distribution of important food crops (wheat, rice, and maize) [33,34,35].
This study suggests coupling the InVEST, PLUS, and Maxent models to comprehensively evaluate the ecological environment of food crops, in order to supplement the lack of research on the impacts of climate change and land-use change on the future HQ–PHS of food crops in subtropical monsoon climate zones. Combining the constraints under China’s national policy and SSP, four development scenarios and four climate scenarios were designed to evaluate the core cultivation areas suitable for food crop cultivation and the impacts of climate and land-use change on HQ–PHS in subtropical monsoon climate zones from 2010 to 2030. The main objectives were to (1) determine the coupled model reliability in land-use prediction, (2) analyze the future trends of Land Use/Cover Change (LUCC)–HQ–PHS under different development and climate scenarios, (3) identify and quantify the impacts of climate change and land-use change on HQ–PHS, and (4) explore the dynamic equilibrium between climate-land-use change and food crop habitat suitability to achieve the dynamic optimization of food crop returns.

2. Materials and Methods

2.1. Research Area

China’s subtropical monsoon climate zone covers a large area south of the Qinling-Huaihe River and east of the Tibetan Plateau (97°40′~121°51′ E, 21°28′~34°7′ N) (Figure 1). This region’s landscape is both high (west) and low (east), with complex and diversified topography that includes the Yunnan–Guizhou Plateau, the Sichuan Basin, the middle-lower Yangtze River lowlands, and the southeastern hills. The land-use type is dominated by forest, followed by farmland and grassland [36]. The research region is located in low latitude and affected by the topography and differences in the thermal properties of the land and sea generating a subtropical monsoon climate, and its hot summer and warm winter climatic features are suitable to the growth of food crops [37]. There are five major commercial grain bases (Chengdu Plain, Han River Plain, Dongting Lake Plain, Pearl River Delta, and Yangtze River Delta) [38].

2.2. Research Framework

The technical route and model method were divided into three parts. They were Land use, HQ, and PHS simulation process, spatial overlay analysis, and spatial cluster analysis (Figure 1). Part 1: Firstly, land-use, social factor, and natural factor data in 2010–2020 were imported into the PLUS model to obtain land-use data predicted in 2030 under different scenarios. Then, land-use and threat source data in 2030 under different scenarios were imported into the InVEST model to calculate HQ. Finally, the data for land use, natural factors, and climate consistent with P > 0.8 and q > 0.2 were screened by Pearson correlation analysis and Geo-detector and imported into the Maxent model to calculate PHS. Part 2: Regions P H S > 0.5 and H Q > 0.5 were obtained by using the Raster Calculator of ArcGIS10.8, and then the priority cultivation area P H S > 0.5 - H Q > 0.5 was obtained by grid overlay analysis. Part 3: High and low clustering results were obtained through local bivariate autocorrelation analysis by the GeoDa Space v1.14.0.0 module, and then priority cultivation areas and key cultivation areas were overlaid to obtain core cultivation areas.

2.3. Data Collection and Pre-Processing

2.3.1. Land-Use Simulation Data for PLUS Model

The land-use simulation data included land-use data, restricted conversion area data, and natural and social factor data for the PLUS model, of which the land-use data were the remote sensing monitoring data of 2010 and 2020 with six natural factor data and five social factor data (Table S1). The land-use type data were classified into six land-use types (cultivated land, forest land, grassland, water, construction land, and bare land). The land-use simulation data were re-sampled to a resolution of 30 m.

2.3.2. Maxent Model Food Crop Distribution Points and Environmental Variables

Distribution point data for grain crops were obtained from the Global Biodiversity Information Facility (http://www.gbif.org/). To avoid the spatial autocorrelation of distribution points leading to overfitting and a reduction in the accuracy of the prediction results, the R language 4.2.0 was used to screen out the duplicated and incorrectly located point data, and only the data with accurate location information on grain crops in the 10-year period were retained; finally, 54 distribution sample points were retained.
Complete and accurate environmental factors were the basic requirements for the construction models in species distribution [39]. The five most widely used environmental variables, climatic variables (the data series of Bioclimatic variables includes 19 climatic variables with ecological significance) [40], land-use type variables, topographic variables, hydrological variables, and anthropogenic variables, were chosen to construct the Maxent species distribution model (Table S2). Data for climate variables were obtained from the WorldClim database (http://www.worldclim.org/) at a spatial resolution of 2.5 arcmin using the Beijing Climate Center Climate System Model 1.1 (BCC-CSM1.1), which consisted of four SSP scenarios for the years 2021–2040 (2030s) (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) (Table S3). Future land use-type variables are PLUS model simulation projections. Topographic variables (elevation, slope, slope aspect) were obtained from the digital elevation model provided by Geospatial Data Cloud (https://www.gscloud.cn/). Hydrological and anthropogenic variables were obtained by a buffer analysis of river and road vector data using ArcGIS 10.8 (Originate in National Catalogue Service for Geographic Information, https://www.webmap.cn/). The research indicated that the topographic, hydrological, and anthropogenic factors did not change easily within 10 years, so a total of 26 environmental factors (topographic, hydrological, and anthropogenic factors in 2020 and climatic and land-use type factors in 2030) were used to simulate the PHS.
To avoid overfitting the model due to mitigating excessive correlation among environmental variables [41] and ensure that the selected environmental variables used in modeling possessed stronger explanatory power regarding the impact on the PHS of food crops, ArcGIS 10.8 and SPSS 19.0 were employed for conducting Pearson correlation analysis. Multiple groups of environmental variables with a correlation coefficient (>0.8) were screened. Only one variable closely associated with the habitat characteristics of food crops was chosen from each group to be included in the modeling process, and the selected environmental variables had a correlation coefficient (r < 0.8). Simultaneously, considering the factor detector within the geographical detector model, eight environmental variables—Bio 1, Bio 2, Bio 5, Bio 8, Bio 12, Bio 15, Bio 17, and LUCC—were selected as participants in constructing the food crop distribution model (Table S2).

2.4. Multi-Scenario Future Land-Use Simulation Projections

2.4.1. Multi-Scenario Design

The purpose of the multi-scenario design was to simulate the land-use variation trend under different development scenarios to analyze the spatial–temporal evolution of land-use and the HQ change in different development scenarios in China. Four scenarios (natural development, city development, cultivated land protection, and ecological protection) were set in accordance with China’s actual situation [42,43].
The natural development (ND) scenario was the continuation of the land-use change trend in 2010–2020. Without setting the conversion probability between various land-use types and considering the requirements of development policies, the Markov chain of the PLUS model was used to predict land-use in 2030 for the natural development scenario at an interval of 10 years. The model parameters (land-use expansion capacity, transfer matrix, domain factor weight, and HQ index) were kept consistent with those of 2010–2020.
The urban development (UD) scenario increased the probability of transferring cultivated land, forest land, and grassland to construction land by 20%. The probability of transferring construction land to land types other than cultivated land was decreased by 30% [44].
Cultivated land security was the foundation for ensuring food security. The cultivated land protection scenario (CP), by controlling the conversion probability of transferring cultivated land to other land-use types, slowed the expansion rate of construction land, which reduced the conversion probability of transferring cultivated land to construction land by 60% [45,46].
Ecological protection scenario (EP). In order to protect ecosystems and biodiversity, the probability of transferring forest and grassland to construction land was reduced by 50% in the ecological protection scenario setting. The probability of transferring cultivated land to construction land was reduced by 30%, and the probability of transferring construction land to forest land was increased by 10%. Habitat degradation was strictly prohibited, and the regional watershed was used as a constraint to limit its arbitrary conversion [46,47].

2.4.2. Multi-Scenario Future Land Use Simulation by PLUS Model

The PLUS model mainly consisted of two modules (LEAS and CARS) [23]. This study was based on the PLUS model simulation process. Firstly, combining the actual situation of the Chinese region, different driving factors from both environmental and social factors were selected, so the data had a consistent projected coordinate system and spatial resolution. Second, the LEAS module was used to assess land-use expansion in 2010–2020 to determine the development probability of each land-use type in China. The CA model simulation, which incorporated the future land-use demand of each land-use type under various scenarios, domain weights, transfer matrices, and other relevant characteristics, was then used to estimate land-use changes in the China region in 2030. Finally, the data of land-use types in 2020 were simulated by the PLUS model. The actual data were compared and verified. The overall accuracy of the PLUS model was found to be 0.91. The Kappa coefficient was 0.87. The FOM (figure of merit) value for the PLUS model was 0.29 [48,49]. The relevant model parameters were set as follows:
Land-use demand indicated the number of grids for different land-use types. The Markov chain method was applied to predict the number of grids for each land-use type in 2030 (Table S4).
Neighborhood weights. Neighborhood weights indicated the expansion capacity of different land-use types. The amount of change in the area (TA) of each land-use type on the same-time scale was more reflective of its expansion capacity [50]. The dimensionless value of ΔTA from 2010 to 2020 was used to determine the neighborhood weights (Table S5), and the calculation was as in Equation (1).
w i = Δ T A i − Δ T A m i n Δ T A m a x − Δ T A m i n
where Δ T A i is the expansion area change of each land-use type and Δ T A m i n and Δ T A m a x are the minimum and maximum values of the expansion area of changein land-use type, respectively.
Transfer matrix. When a land-use type was not converted to another type, the corresponding value in its matrix was 0, and vice versa was 1. The land-use change rules for the multi-scenario design are shown in Table S6.

2.5. HQ Assessment Methods

2.5.1. InVEST Model HQ Assessment

Based on the HQ module of the InVEST model, the HQ index was calculated by combining the land cover, habitat threat factors, and sensitivity of different land covered to the threat factors in 2010 and 2030, and the calculation was as in Equation (2).
Q x j = H j 1 − D x j z D x j z + K z
where Q x j is HQ index in raster x and land-use type j, ranging from 0–1, with larger values indicating better HQ; H j is habitat suitability; D x j is habitat degradation; K is the half-saturation parameter; and z is the model default constant.
The degree of habitat degradation was calculated as in Equation (3).
D x j = ∑ r = 1 R ∑ y = 1 Y r w r / ∑ r = 1 R w r r y i r x y β x S j r
where R is habitat threat source, Y r is the grid number of r threat source in the land type layer, w r is the weight of threat factor r, r y is the intensity of threat factor, β x is the legal protection degree, which is set as 1, and S j r is the sensitivity of habitat types to threat sources. i r x y is the influence of threat factor r on the habitats of grid x on grid y, and its variation with distance satisfies the following Equations (4) and (5).
i r x y = 1 − d x y / d r m a x
i r x y = e x p − 2.99 / d r m a x d x y
where d x y is straight-line distance between grid x and y and d r m a x is the maximum influence distance for the threat factor r.
Relevant research results [51,52] were combined with the actual situation of the Chinese region. The habitat threat factors, threat factor weights, maximum impact distance, attenuation type (Table S7), habitat suitability of land-use types, and sensitivity to the threat factors (Table S8) were determined for cultivated, constructed, and unutilized land. HQ distribution maps were obtained based on the above methods and categorized into three levels (low, medium, and high) by applying the equal interval method.

2.5.2. Land-Use Change on HQ

ArcGIS 10.8 partition statistics and the geographic information mapping method were used to extract the HQ amount under land-use type change, which constructed the HQ change index ( H Q C I i j ) to explore the HQ change caused by land-use change per unit area [53], which was calculated as in Equation (6).
H Q C I i j = Δ Q i j Δ S i j
where Δ Q i j is the change amount in HQ when land-use type i is converted to j, Δ S i j is the change amount in the area when land-use type i is converted to j, and H Q C I i j positive or negative represents the positive or negative effect of land-use change on HQ.

2.6. PHS Assessment Methods

2.6.1. Environment Variable Filtering

Pearson correlation analysis. The Pearson correlation test was performed on environmental data using ArcGIS 10.8 and SPSS. Environmental factors were examined using the Maxent model’s Jackknife approach to quantify their importance in influencing the distribution of food crops with the regularized gain value and to determine the model contribution rate of the environmental variables. When |r| > 0.8 between the two variables indicated a covariance relationship, the environmental variable with the higher importance and contribution rate was retained.
Geo-detector method. Environmental variables with minimal impact on PHS were screened through factor detection using geographic detectors. Factor detection was employed to determine the degree to which distinct independent factors X explained the geographical dissimilarity of a dependent variable Y [54]; the calculation was conducted as in Equation (7).
q = 1 − S S W S S T = 1 − ∑ h = 1 L N h σ h 2 N σ 2
where q represents the factor of detector; h = 1, 2, …, L is the number of partitions of independent variable X or dependent variable Y; N and N h are the number of units in the whole district and district h, respectively; σ 2 and σ h 2 are the variance of the Y value in the whole district and district h, respectively; and SST and SSW are the sum of the variances in the whole district and within the district, respectively. The q value is in the range of [0, 1], and the stronger the explanatory power, the larger the value.

2.6.2. Maxent Model Construction and Accuracy Evaluation

The Maxent model was built by combining data from food crop points and eight environmental parameters. The samples used in the modeling were randomly divided into 75% training samples and 25% verification samples, and this was then repeated 50 times to reduce sample mistakes. The Bootstrap enumeration method was used as the iterative accuracy test method; the number of loop iterations was 5000 times. The receiver operation characteristic (ROC) curve was used to assess accuracy. The area under the curve (AUC) was employed as an indicator of fitting accuracy. Finally, the mean AUC of 17 simulation results of 16 combined scenarios in 2020 and 2030 was obtained as a quantitative evaluation index of the overall model prediction accuracy.
ArcGIS 10.8 was used to divide potentially suitable areas into four levels for suitability index P (non-suitable zones, ≤0.25; low suitability zones, 0.25–0.5; medium suitability zones, 0.5–0.75; high suitability zones, >0.75).

2.6.3. Land-Use Change on PHS

ArcGIS 10.8 partition statistics were used until the fitness and land-use change rasters corresponded to each other. The amount of fitness change under land-use type change was calculated to create the fitness change index, which was then used to investigate the PHS change due to changes in land use per unit area. The calculation is as Equation (8).
P H S C I i j = Δ P H S i j Δ S i j
where Δ P H S i j is the amount of change in fitness when land-use type i is converted to j; Δ S i j is the amount of change in the area when land-use type i is converted to j; and the positive or negative of P H S C I i j represents the positive or negative impacts of land-use change on PHS.

2.7. Calculation of Food Crop Cultivation Areas

2.7.1. Spatial Overlay Analysis

To investigate the combined effects of HQ and PHS on future food crop cultivation areas under four land-use and four climate scenarios in 2030, Maxent model predictions and InVEST model predictions at a threshold of 0.5 were binarized, and the binarized results were overlaid using ArcGIS 10.8; the areas of P H S > 0.5 and - H Q > 0.5 were set as food priority grain cultivation area, the areas of P H S > 0.5 and - H Q < 0.5 were set as high-PHS area, the areas of P H S < 0.5 and - H Q > 0.5 were set as high-HQ area, and the areas of P H S < 0.5 and - H Q < 0.5 were set as unsuitable cultivation area (Table S9).

2.7.2. Spatial Cluster Analysis

Cluster analysis [55] was used to further understand PHS–HQ spatial correlation, and PHS–HQ values were assigned to the county-level vector data, imported into the GeoDa v1.14.0.0 software. It used the Space module’s bivariate and regional bivariate autocorrelation analysis to generate the Moran’s index as well as the high and low clustering maps, with the high and high clustering areas indicating major food cultivation areas.

3. Results

3.1. Multi-Scenario Land-Use Simulated Predictions

Based on the 2000 and 2010 land-use data in the research area, the PLUS model was used to simulate and predict the 2020 land-use data, which was then verified by comparing it with the actual data from 2020. The PLUS model’s overall accuracy was 0.91, with a Kappa coefficient of 0.87, indicating that it was very dependable. The results of the multi-scenario land-use distribution simulation prediction in 2030 (Figure 2A) showed that, compared with 2010, the cultivated land area in the four different scenarios was encroached upon by forest land to varying degrees. The ecological protection scenario, the urban development scenario, the natural development scenario, and the cultivated land protection scenario all resulted in a reduction in cultivated land area. In the cultivated land protection scenario, cultivated land area proportion was the highest (30.38%). However, it was still lower than in 2010 (30.53%). Environmental changes have had an impact on food crop growth. Forest land was effectively protected under the ecological protection scenario, with an area share of 52.34% up from 2010 (51.54%). Urban construction led to a general expansion of construction land, with the area share reaching 4.34%, 4.93%, 4.14%, and 5.04% under different scenarios (the area shares in 2010 were 3.40%). It was the most obvious expansion trend in urban development and the ecological protection scenario. The expansion of forest land resulted in a drop in grassland area, which showed a downward trend under all four scenarios, but the decrease was less. Under the natural development scenario, the proportion of grassland area was 10.92%, which was the highest among the four scenarios, but it was still lower than that in 2010 (11.35%). Trends in unused land and water were not significant under any of the four scenarios (both 2010 and 2030), and the unused land in all four scenarios would have expanded in 2030, which resulted in a decrease in water compared to 2010. Overall, the trends in unused land and water were more stable.
Combined with the chord diagram of land-use transfer in 2010–2030 under different scenarios (Figure 2B), there was minimal variation in the alteration of land-use patterns between 2010 and 2030 under the four scenarios. In general, cultivated land was primarily converted into forest, grassland, and construction land. Forest land was primarily converted to cultivated land and grassland. Grassland was transferred more to forest and cultivated land. Among them, construction land, water, and unused land accounted for less area and less change. Land-use structure types were projected to stay generally constant between 2020 and 2030, allowing food crops to adjust to environmental changes.

3.2. Multi-Scenario HQ Simulated Predictions

InVEST was used to anticipate HQ in various future scenarios (Figure 3). The overall spatial distribution pattern of HQ was similar. The HQ classes’ distribution was consistent with the pattern of land-use change. The land-use type of low-grade habitats was dominated by constructing land, which was primarily dispersed in economically developed urban development clusters, particularly the Yangtze River Delta and Pearl River Delta urban agglomerations. Medium habitats were dominated by cultivated land and grassland cover, which had a better ecological environment and were mainly distributed in the Yunnan–Guizhou Plateau, Sichuan Basin, and middle and Yangtze River. Higher habitats were mainly composed of woodlands, which were widely distributed in the hilly areas south of the Yangtze River and the mountainous and plateau areas at higher altitudes.
The results of HQ projections for 2010 and 2030 were compared and analyzed (Table S10). Under the four land-use scenarios, the study area’s overall trend of HQ changes was similar, showing a consistent increase in low and high habitat areas and a decline in medium habitat areas. Under the natural scenario, the low and high habitat areas will expand significantly in 2030 with an area of 2.01 × 104 and 1.68 × 104 km2 respectively compared with 2010, while the medium habitat area will shrink significantly with an area of 6.79 × 104 km2. Overall, the degradation rate of HQ was slightly higher than the enhancement rate. Under the town development scenario, low habitat area expanded significantly, with a degraded HQ area of 2.58 × 105 km2 (degradation rate of 11.28%) and the lowest mean HQ value compared to the other scenarios.
Under the cultivated land protection scenario, medium habitat areas shrank less. HQ had the biggest stable area among the scenarios, measuring 1.81 × 106 km2. The rate of degradation of HQ was the smallest compared to the other scenarios (10.53% degradation), and the mean value of HQ improved to 0.61%. Under the ecological protection scenario, the expansion of higher habitat areas was the largest with an area of 2.45 × 104 km2. The area of HQ enhancement was the largest (2.43 × 105 km2) (an enhancement rate of 10.61%). In general, the overall trend of HQ degradation under different scenarios in the subtropical monsoon climate region of China from 2010 to 2030 is obvious, with lower habitat areas expanding more than medium and higher habitat areas. The rate of HQ degradation is higher than the rate of enhancement.

3.3. Multi-Scenario Potentially Suitable Habitat Simulated Predictions

3.3.1. Environment Variable Filtering for the Maxent Model

The extent of the influence of the 27 environmental variables on PHS for food crops was analyzed by factor detection with Geo-detector (Table S11). Pearson correlation analyses were performed on the 27 environmental variables using ArcGIS 10.8 and SPSS (Figure S1). The Maxent model was used to iteratively screen 26 variables to ensure that the regularized training gain value of the taken variables was greater than or equal to 0.1. Finally, Bio 1, Bio 2, Bio 5, Bio 8, Bio 12, Bio 15, Bio 17, and LUCC were selected to participate in the construction of the model.

3.3.2. Model Accuracy Assessment of the Maxent Model

The model was evaluated by ROC, and the AUC curve value was used as the model accuracy evaluation index, which combined four climate scenarios with four land-use scenarios and a total of 17 combinations of land-use and climate data in 2020 to carry out 50 iterations of the model; the mean value of the AUC of the results of all the runs was 0.869 > 0.85 (Figure 4a), which made the prediction results reliable.

3.3.3. Environmental Contribution Assessment for Maxent Model

The Maxent model was utilized to determine the significance of replacement and the contribution of environmental factors to the model’s design (Figure 4b). The environmental variables with a percentage contribution greater than 10% included LUCC (39.7%), Bio 17 (38.2%), Bio 15 (12.7%), and the environmental variables with a permutation importance greater than 20% included Bio 17 (53.4%) and Bio 15 (30%). Jackknife was used to obtain the importance test map on food crop distribution (Figure 4c). The training value of the LUCC, Bio 17, Bio 15, Bio 8, and Bio 5 variables was more than 0.1, and the sum of the contribution reached 98.2%. The complete research revealed that the primary environmental variables influencing food crop distribution were LUCC, Bio 17, and Bio 15.

3.3.4. Simulations of Potential Future Habitat Distribution Patterns for Food Crops

Climate factor indicators for the four SSP scenarios under the BCC-CSM1.1 GCM for the years 2021–2040 (2030s) (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) were used in combination with land-use data for the four future scenarios obtained from the PLUS model run. The simulation projected the potential suitable habitats for food crops corresponding to the four climate scenarios under the four future land-use scenarios (Figure S2).
Natural development scenario. Highly suitable areas for grain crops were mainly distributed in the Yangtze River and Pearl River Delta. The SSP2-4.5 scenario showed a more concentrated distribution of highly suitable areas at the spatial scale compared with the SSP3-7.0 scenario. The highly suitable areas showed a tendency to expand from Zhejiang and Guangdong provinces and the Guangxi Zhuang Autonomous Region to Hunan and Jiangxi in the SSP5-8.5 scenario. The SSP1.2-6 scenarios showed a more dispersed distribution of suitable areas compared to the other scenarios. Overall, the SSP5-8.5 scenario had the widest coverage of highly suitable areas (7.0%). The SSP1-2.6 scenario had the highest total coverage of suitable areas (52.5%).
Urban development scenario. The distribution of highly suitable areas was relatively fragmented. In the SSP1-2.6 scenario, highly suitable areas were mainly distributed in Anhui, Jiangsu, Zhejiang, Hunan, Hubei, and Guangdong provinces. The distribution of highly suitable areas in Hunan and Hubei in the SSP2-4.5 and SSP3-7.0 scenarios was significantly reduced. In the SSP5-8.5 scenario, the distribution of suitable areas was scattered, and the distribution of highly suitable areas was concentrated only in the Pearl River Delta region. Overall, the SSP1-2.6 scenario had the largest area coverage of highly suitable areas (7.6%), and the SSP3-7.0 scenario had the largest area coverage of suitable areas (42.3%).
Cultivated land protection scenario. The high-fitness areas for grain crops were mainly located in Anhui, Guangdong, Yunnan, Jiangsu, and Zhejiang Provinces and the Guangxi Zhuang Autonomous Region. The highly suitable areas decreased slightly in Zhejiang Province under the SSP1-2.6 scenario. In the SSP2-4.5 scenario, there was a significant expansion trend of highly suitable areas in the Guangxi Zhuang Autonomous Region. Under the SSP3-7.0 scenario, the highly suitable areas showed an expansion trend in the Fujian, Hunan, and Jiangxi Provinces. Under the SSP5-8.5 scenario, the high aptitude areas slightly decreased in Yunnan and Zhejiang Provinces, while the number of its low aptitude areas greatly increased. Overall, under the cultivated land protection scenario, the SSP1-2.6 scenario had the largest proportion for high-aptitude areas (9.3%) and the highest coverage of aptitude areas (69.2%).
Ecological protection scenario. Highly suitable areas for food crops were widely distributed. Highly suitable areas were distributed in Guangdong Province in the SSP1-2.6 scenario. In the SSP2-4.5 scenario, the distribution of highly suitable areas was particularly scattered, but they covered the largest area (7.4%). In the SSP3-7.0 scenario, highly suitable areas were mainly distributed in Guangdong Province and the Guangxi Zhuang Autonomous Region. Under the SSP5-8.5 scenario, the largest proportion of highly adapted areas was in the Guangxi Zhuang Autonomous Region. Overall, under the ecological protection scenario, the area covered by highly suitable zones was the largest under the SSP2-4.5 scenario (7.4%), and the area covered by suitable zones was the largest under the SSP5-8.5 scenario (67%).
The simulation prediction of the 16 combinations showed that future climate change and land-use types had a greater impact on the distribution of potentially suitable habitats for food crops (Figure 5). The suitable areas for food crops were centered on the Yangtze River Delta and the Pearl River Delta as the center of the highly suitable areas. Compared with the natural development scenario, the mean percentage of total suitable areas increased by 9% and 17.8% for the cultivated land protection scenario and the ecological protection scenario, respectively. However, it decreased by 10.65% for the urban development scenario. The simulation of SSP1-2.6 under the ecological protection scenario showed the largest coverage of highly suitable areas (9.3%) and the largest coverage of total suitable areas (69.2%).

3.4. Comprehensive Analysis of Food Crop Cultivation Areas

This study synthesized the results of PHS and HQ projections based on four land-use scenarios and four climate scenarios in 2030 through spatial overlay and cluster analysis. The results showed (Figure 6) that the key cultivation area and priority cultivation area under the 16 combination scenarios were roughly the same, the intersection area was designated as the core cultivation area, and the cultivation zone buffer was designated around the core cultivation area as the center, which was to dynamically regulate the layout of food crop cultivation and effectively guarantee global food security.
Natural development scenario. Core cultivation areas were mainly distributed in the Pearl River and the Yangtze River Delta and the central region of the southeastern hills, among which the distribution area of the core cultivation area was the largest (9.458%) under the ND-SSP5-8.0 scenario, and it was the largest under the 16 combination scenarios. Urban development scenario. Compared with other scenarios, the area of grain core cultivation area was the least distributed, patchily scattered in the Pearl River Delta, the Yangtze River Delta, and the central and eastern parts of the southeastern hills, in which the UD-SSP5-8.0 scenario accounted for the highest area, only 0.531%. Under the urban development scenario, the ecological environment was disturbed by human activities, and the area of grain core cultivation area was greatly reduced, which seriously threatened global food security.
Cultivated land protection scenario. Under the CP-SSP1-2.6 and CP-SSP3-7.0 scenarios, the core cultivation area expanded relatively significantly, and its area accounted for 6.279% and 3.361%, respectively. Under the CP-SSP2-4.5 and CP-SSP5-8.5 scenarios, the core cultivation area was less distributed, accounting for 0.788% and 1.613%, respectively. Climate change has had a positive and negative impact on the core cultivation area. Part of unsuitable areas for crop growth were transformed into suitable habitats under climate change, which resulted in a large expansion of the core cultivation area, while an unfavorable climate environment led to the shrinking of the core cultivation area. Ecological protection scenario. Measures returned farmland to forests and grasslands and protected grassy hills and grassy slopes. The management of rocky desertification had changed the original land-use distribution. The core cultivation areas for grain were scattered in the Pearl River Delta and part of the Yangtze River Delta. The percentages of the core cultivation areas for grain under the four climate scenarios were 1.222%, 1.135%, 1.470%, and 1.535%, respectively.
The simulation prediction results of 16 combination scenarios showed that climate change and land-use changes had caused the loss and fragmentation of some food crop habitats to a certain extent, which resulted in a significant reduction in core cultivation area. In some regions, land-use shifts have also increased the suitable range of food crops and mitigated the negative impacts of urban expansion and the return of farmland to forests and grasslands, while climate change had facilitated the conversion of some unsuitable areas to suitable habitats; thus, this enlarged the core cultivation area.

4. Discussion

4.1. Multi-Scenario Land-Use on Food Crop HQ

The analysis of HQCI values of land-use transfer in China’s subtropical monsoon climate zone under different scenarios from 2010 to 2030 showed that the transfer of forests to other types negatively affected HQ. Those were the most significant effects of converting to construction and bare lands, whose HQCI mean values were −0.3992 and −0.4851, respectively (Figure 7). The transfer of construction lands to other types of land positively affected HQ, with the conversion of construction lands to forests having the largest HQCI value (mean value of 0.5901). The transfer of bare lands to cultivated lands, forests, grass, and water positively affected HQ, with HQCI means of 0.2753, 0.5957, 0.3701, and 0.4454, respectively, and the transfer of construction lands negatively affected HQ (−0.1661). Cultivated lands were converted to forests, grass, and water. The conversion of grass to forests and water positively affected HQ.
When comparing different land-use scenarios, the urban development scenario had the highest absolute value of HQCI for converting forests and bare lands to construction lands, with HQCI values of −0.4444 and −0.1676, respectively. Thus, the conversion of forests and bare lands to development lands has the greatest harmful influence on HQ. The reason for this was that the construction lands encroached on part of the forests and the bare lands, which posed a threat to the HQ [56,57]. The conversion of construction lands to forests had the largest HQCI (0.5958); this was the most significant positive impact on HQ, which indicated that the conversion of construction lands to forests during urbanization and development had a significant positive impact on HQ. Under the cultivated land protection scenario, the HQCI value (−0.4111) for the conversion of cultivated lands to construction lands was the smallest in absolute value among the four scenarios, which indicated that the conversion of cultivated lands to construction lands had a weaker negative impact on HQ than the other three scenarios. The reason for this was that cultivated protection measures weakened the negative impacts of the encroachment of construction lands on cultivated lands, which attenuated the degradation of HQ [58]. Under the ecological protection scenario, the HQCI values of forests and grass converted to construction lands were −0.3791 and −0.3424, respectively. These were the smallest in absolute value compared with the other three scenarios, and their negative impacts on HQ were weaker than those of the other three scenarios. This might be because the artificial protection of the earth’s ecosystems and the preservation of biodiversity in the scenario restricted the encroachment of construction lands on forests and grass, so the degree of degradation of HQ was weakened [59].

4.2. Multi-Scenario Land-Use Change for Food Crop PHS

Based on the PHS of 16 SSP-LUCC combination scenarios, an index named PHSCI was constructed (Figure 8). The direction of land-use change from cultivated lands to construction lands had a generally negative impact on PHS, with PHSCI generally negative under the 16 SSP-LUCC combination scenarios and PHSCI values only positive under the three combination scenarios of SSP1-2.6-UD, SSP1-2.6-EP, and SSP5-8.5-EP (PHSCI value 0.01, 0.01, and 0.03, respectively), which indicated that although urbanization occupied cultivated land, the balance of cultivated land occupation and compensation [60] offset the negative impacts of urbanization. The role of cultivated land compensation was limited, and most of the compensated cultivated lands originate from ecological land [61,62], so the increased ecological service value of the same area of compensated cultivated lands tends to be lower than that of the cultivated lands occupied by construction lands [63,64]. The conversion of cultivated lands into construction lands in some areas still showed negative impacts on the PHS. The direction of land-use changes from forests to construction lands had a generally negative effect on PHS, which was due to the higher ecological contribution rate of forests when urbanization occupied many ecological land (forests) [65,66], which resulted in a decrease in PHS. While the direction of land-use changes from water to forests and grass had a greater degree of positive influence on PHS, PHSCI was positive in all 16 SSP-LUCC combination scenarios, and the direction of land-use change with PHSCI was higher than 0.20, accounting for 65.6%. The ecological environment index of water was smaller than grass and forests [67], and the land-use types that played a dominant role in the maintenance and improvement of the regional ecological environment were grass and forests [49,68]. Developing bare lands regulated the ecological environment [69], and the direction of land-use change in which bare lands were converted to forests, grass, and construction lands had a generally positive effect on PHS, and the percentage of positive PHSCI under the 16 SSP-LUCC combination scenarios was as high as 81.25%.
Analyzed from the perspective of different land-use scenarios, the direction of land-use changes from construction lands to forests had a positive effect on PHS under the natural development scenario (PHSCI values: 0.05, 0.13, 0.11, and 0.17, respectively). Conversion from construction lands to forests increased biodiversity and strengthened the homeostasis of the food crop-growing environment in the study area [70,71]. The PHSCI of converting from forests to construction lands was negative, and forest conservation plays an important role in maintaining ecological sustainability. The PHSCI of this scenario was generally lower than the natural development scenario due to the concentrated development of bare lands because of economic development and urban expansion [72], which threatened the PHS of the subtropical monsoon climate zone in China. The conversion of bare lands into forests and grass to achieve regional ecological optimization resulted in positive PHSCI under all four climate scenarios, while the conversion of water and construction lands to bare lands resulted in negative PHSCI, which indicated that the expansion of bare lands reduced the PHS for food crops. The ecological structure of bare lands was more vulnerable to destruction under the urban development scenario, which led to a reduction in overall ecological quality [73]. In the protection scenario, the quality of cultivated lands was basically guaranteed, which led to the enhancement of ecological diversity [74], and thus the PHSCI was generally higher in this scenario than in the natural development scenario. In the ecological protection scenario, the PHSCI of conversion from water and cultivated lands to forests and grass was generally positive, but the mean value was smaller than that of the cultivated land protection scenario. This was mainly because some pits and ponds in water bodies and some rain-fed dryland and pastureland in cultivated land were converted to ecological land by measures such as returning farmland to forests, grasslands, and reforestation, thus vigorously developing potential ecological space. The expansion of ecological space reduced the area of cultivated lands, which had an impact on the potentially suitable habitats for food crops [75]. Compared with the natural development scenario and the urban development scenario, the PHSCI under the ecological protection scenario increased, and the ecological protection measures led to the enhancement of the PHS for food crops [76].

4.3. Food Crop Habitat Optimization Strategies

In order to scientifically identify potentially suitable areas for food crops in China’s subtropical monsoon climate zone, this study applied the PLUS–InVEST–Maxent coupling model and spatial cluster analysis to integrate the core cultivation areas of food crops and the HQ–PHS high-high clustering areas. This was carried out to fully consider the potential distribution of food crops in the synergistic effect of natural conditions and anthropogenic disturbances, which improved the identification of potential food crop habitat areas and the accuracy of identification. Based on the results of the current analysis, this study proposed three habitat optimization strategies from the perspective of different land-use scenarios as follows:
(1)
Habitat optimization strategies for the urban development scenario. In the context of urban development, significant urban expansion had threatened the stability of ecosystems, and the conversion of land-use types with high habitat suitability such as forests and grass to land-use types with low habitat suitability such as construction lands had resulted in serious degradation of HQ [77,78]. Based on the HQCI–PHSCI analysis, optimizing the conversion of bare lands to forests, grass, and construction lands further improved food benefits and ecological benefits while ensuring economic and urban development. Ecological restoration was carried out through the land-use types in the direction of land-use conversion types that have adverse effects on the HQ of food crops to optimize the HQ and construct the ecological security pattern of the urban ecosystem.
(2)
Habitat optimization strategies for the cultivated land protection scenario. In the context of food security as the core, the conflict between urban development and cultivated land protection was mitigated by reducing the conversion rate of cultivated lands to other land-use types [79]. The stable state of highly suitable habitat was maintained by increasing the conversion rate of construction lands and bare lands to forests and grass. HQ was optimized through measures such as increasing urban vegetation cover and building ecological corridors.
(3)
Habitat optimization strategies for the ecological conservation scenario. In the context of ecological protection, ecological environmental protection and HQ optimization need to be considered, and the HQ of food crops needed to be improved while expanding ecological land [80]. This scenario considered food security and the carrying capacity of resources and the environment, which reduced the conversion rate of forests, grass, and cultivated lands to construction lands while increasing the conversion rate of construction lands to forests. Later, the conversion rate of other land-use types to bare lands was reduced to improve land utilization while considering ecological protection and habitat optimization. Through the grading of food crop cultivation zones, the habitat optimization needed for the core cultivation area was given priority, and then the cultivation zone buffer for food crop cultivation in highly suitable habitats was constructed. While taking ecological protection into account, potentially highly suitable habitats will be gradually transformed into core cultivation areas to enhance the cultivation advantages of grain crops.

4.4. Inadequate and Prospects

When designing future land use development scenarios in this study, there were several subjective factors due to limited data resources. In the future, based on this study, more comprehensive development scenarios will be designed with field survey data to improve the credibility of the model prediction results. Due to the dominant impact of land-use change on HQ [12], the HQ predicted by the InVEST model simulation in this study will lack the consideration of the influence of different climate scenarios on HQ in the future, and the combined scenario design system will be further improved in the subsequent study to enhance the accuracy of the prediction results.
Species distribution was affected by various factors such as environment, geography, and ecosystems, and the prediction of potential distribution areas (PHAs) of species using only a single model was highly biased. Considering the effects of different land-use changes and climate change on habitat suitability, and to ensure smoothness and improve the efficiency of model operation, the Maxent model was used to predict the PHS, and its simulation accuracy was in line with expectations. In the follow-up study, to further improve the accuracy of the prediction results, it will be recommended to use the BioMod2 combination model to reduce the error of misjudging the interrelationships between environmental variables and habitats [81]. It is also recommended to introduce new indicators to describe the effects of human and animal activities as well as the intensity of interspecific competition [82] on the HQ and PHS to monitor the ecological changes of food crops more accurately and dynamically.
The response of crops to climate change had a certain lag [83], and since the topography, hydrology, and human factors had little fluctuation within 10 years, the study has not considered the changes in topography, hydrology, and anthropogenic factors in the next 10, and the prediction results based on future climate conditions may be different from the reality on the time scale. Subsequent studies extend the time scale to improve the reliability of the ecological prediction results for food crops in different future periods (2050s/2070s/2100s).

5. Conclusions

InVEST–PLUS–Maxent was used to assess the spatiotemporal changes of food crop ecosystems in China’s subtropical monsoon climate zone from 2010 to 2030. The main conclusions were summarized below:
(1)
The PLUS model simulation predicted high reliability (accuracy, 0.91; Kappa coefficient, 0.87), so this model can be reliably used for land-use prediction. The composition of the landscape matrix in 2010–2030 was dominated by forests. The land-use structure will be relatively stable in 2020–2030, which is conducive to the adaptation of food crops to environmental changes.
(2)
The overall trend of HQ degradation under different scenarios in China’s subtropical monsoon climate zone from 2010 to 2030 will be obvious, with the expansion of lower habitat areas being greater than that of medium-level and higher-level habitats, and the rate of HQ degradation was higher than the rate of enhancement.
(3)
LUCC, Bio 15, and Bio 17 will be the main environmental factors effecting the food crop PHS in China’s subtropical monsoon climate zone from 2020 to 2030. Under the 16 SSP-LUCC combination scenarios, the total area suitable for grain crops increased by 9% (SSP-CP) and 17% (SSP-EP), which decreased by 10.65% in SSP-UD compared with SSP-ND.
(4)
The outward transfer of ecological land such as forests and grass as well as bare lands triggered a negative response, and the outward transfer of construction lands triggered a positive response. Although climate change and land-use change will cause some of the habitats for food crops to be lost and fragmented to a certain extent, reasonable land-use shifts in different regions will expand the suitable range of food crops and alleviate the negative impacts of urban expansion on food growth, which will achieve the dynamic balance necessary to maintain the ecosystem.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su162310301/s1, Figure S1: Pearson correlation coefficient analysis of environmental variables (Note: 1 is LUCC, 2 is ASE, 3 is Dislake, 4 is ASL, 5 is Discity, 6 is Disriver, 7 is Disroad, 8 is SLOP, 9 is bio 19, 10 is bio 18, 11 is bio 17, 12 is bio 16, 13 is bio 15, 14 is bio 14, 15 is bio 13, 16 is bio 12, 17 is bio 11, 18 is bio 10, 19 is bio 9, 20 is bio 8, 21 is bio 7, 22 is bio 6, 23 is bio 5, 24 is bio 4, 25 is bio 3, 26 is bio 2, 27 is bio 1); Figure S2: Future distribution patterns of potentially suitable habitats for food crops in 2030; Table S1: PLUS Land-use simulation data; Table S2: Geographical environment variables required by the MaxEnt model; Table S3: Four emission scenarios; Table S4: Land Demands; Table S5: Neighborhood weight of each variety according to Δ T A ; Table S6: Transfer matrix; Table S7: Influence distance and weight of threat factors; Table S8: Habitat suitability of land use types and sensitivity to different threat sources; Table S9: Partitioning of grain cultivation regions after HQ and PHS spatial superposition; Table S10: Area statistics and changes of each HQ type in the subtropical monsoon climate zones of China in 2030 under different scenarios; Table S11: The detection results of the factor detector for 27 environmental variables.

Author Contributions

Conceptualization, H.W.; methodology, X.Z., H.W. and S.Z.; software, X.Z., R.Z. and R.T.; validation, R.Z., R.T. and S.Z.; formal analysis, X.Z., R.Z. and R.T.; investigation, X.Z., R.Z. and R.T.; resources, X.Z. and S.Z.; data curation, X.Z., R.Z. and R.T.; writing—original draft preparation, X.Z.; writing—review and editing, H.W. and S.Z.; visualization, X.Z. and R.T.; supervision, X.Z. and H.W.; project administration, H.W.; funding acquisition, H.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the High-level Talent Foundation Project of Harbin Normal University, grant number 1305124219.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data used in this research are available upon request from the corresponding author.

Acknowledgments

The authors thank the reviewers for their valuable comments, and the authors thank the editor for his efforts in this paper.

Conflicts of Interest

Author Hanxi Wang was employed by the company Jilin Shuangqi Environmental Control Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Figure 1. Flowchart of the proposed method.
Figure 1. Flowchart of the proposed method.
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Figure 2. Simulation results for China’s subtropical monsoon climate zone (ND, Natural development scenario; UD, Urban development scenario; CP, Cultivated protection scenario; EP, Ecological protection scenario).
Figure 2. Simulation results for China’s subtropical monsoon climate zone (ND, Natural development scenario; UD, Urban development scenario; CP, Cultivated protection scenario; EP, Ecological protection scenario).
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Figure 3. Simulation results of HQ in the subtropical monsoon climate zones of China in 2030 (ND, Natural development scenario; UD, Urban development scenario; CP, Cultivated protection scenario; EP, Ecological protection scenario).
Figure 3. Simulation results of HQ in the subtropical monsoon climate zones of China in 2030 (ND, Natural development scenario; UD, Urban development scenario; CP, Cultivated protection scenario; EP, Ecological protection scenario).
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Figure 4. Accuracy evaluation and contribution rate of environmental variables in Maxent.
Figure 4. Accuracy evaluation and contribution rate of environmental variables in Maxent.
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Figure 5. PHS for food crops under different scenarios in 2030 (Note: The data in each sector represents the percentage of area of each type in the corresponding scenario).
Figure 5. PHS for food crops under different scenarios in 2030 (Note: The data in each sector represents the percentage of area of each type in the corresponding scenario).
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Figure 6. Spatial distribution and cultivation area distribution of food crops under different scenarios. (Note: The data in each sector represents the percentage of area of each type in the corresponding scenario).
Figure 6. Spatial distribution and cultivation area distribution of food crops under different scenarios. (Note: The data in each sector represents the percentage of area of each type in the corresponding scenario).
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Figure 7. HQCI values for land use transfers in 2030 under different scenarios (ND, Natural development; UD, Urban development; CP, Cultivated protection; EP, Ecological protection).
Figure 7. HQCI values for land use transfers in 2030 under different scenarios (ND, Natural development; UD, Urban development; CP, Cultivated protection; EP, Ecological protection).
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Figure 8. PHSCI for land use transfers for different land uses (Note: A. SSP1-2.6, B. SSP2-4.5, C. SSP3-7.0, D. SSP5-8.5, I. ND, II. UD, III. CP, IV. EP, 1. cultivated lands, 2. forests, 3. grass, 4. water, 5. construction lands, 6. bare lands).
Figure 8. PHSCI for land use transfers for different land uses (Note: A. SSP1-2.6, B. SSP2-4.5, C. SSP3-7.0, D. SSP5-8.5, I. ND, II. UD, III. CP, IV. EP, 1. cultivated lands, 2. forests, 3. grass, 4. water, 5. construction lands, 6. bare lands).
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Zuo, X.; Zhi, R.; Tang, R.; Wang, H.; Zang, S. Study on the Impact of Spatiotemporal Changes in the Ecological Environment on Grain Crops in the Subtropical Monsoon Climate Zone. Sustainability 2024, 16, 10301. https://doi.org/10.3390/su162310301

AMA Style

Zuo X, Zhi R, Tang R, Wang H, Zang S. Study on the Impact of Spatiotemporal Changes in the Ecological Environment on Grain Crops in the Subtropical Monsoon Climate Zone. Sustainability. 2024; 16(23):10301. https://doi.org/10.3390/su162310301

Chicago/Turabian Style

Zuo, Xiaokang, Rui Zhi, Ruiqian Tang, Hanxi Wang, and Shuying Zang. 2024. "Study on the Impact of Spatiotemporal Changes in the Ecological Environment on Grain Crops in the Subtropical Monsoon Climate Zone" Sustainability 16, no. 23: 10301. https://doi.org/10.3390/su162310301

APA Style

Zuo, X., Zhi, R., Tang, R., Wang, H., & Zang, S. (2024). Study on the Impact of Spatiotemporal Changes in the Ecological Environment on Grain Crops in the Subtropical Monsoon Climate Zone. Sustainability, 16(23), 10301. https://doi.org/10.3390/su162310301

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