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Article

Impact of Spatial Evolution of Cropland Pattern on Cropland Suitability in Black Soil Region of Northeast China, 1990–2020

by
Long Kang
1 and
Kening Wu
1,2,*
1
School of Land Science and Technology, China University of Geosciences, Beijing 100083, China
2
Key Laboratory of Land Consolidation and Rehabilitation, Ministry of Nature Resources, Beijing 100035, China
*
Author to whom correspondence should be addressed.
Agronomy 2025, 15(1), 172; https://doi.org/10.3390/agronomy15010172
Submission received: 1 December 2024 / Revised: 26 December 2024 / Accepted: 8 January 2025 / Published: 12 January 2025
(This article belongs to the Special Issue Sustainable Crop Production for Mitigating Environmental Impact and Increasing Yield)
Browse Figures
Figure 1
<p>Location (<b>a</b>), administrative subdivisions (<b>b</b>), and soil types (<b>c</b>) in the black soil region of northeast China (BSRNC).</p> "> Figure 2
<p>Spatial changes in cropland gravity center.</p> "> Figure 3
<p>Hotspots for cropland change from 1990 to 2020.</p> "> Figure 4
<p>Spatial distribution of cultivable land in the BSRNC.</p> "> Figure 5
<p>Relationships between actual crop yields and simulated cultivability scores for different cities in the BSRNC.</p> "> Figure 6
<p>Suitability of cropland resources in the BSRNC (<b>a</b>) and changes in single-factor suitability (<b>b</b>).</p> "> Figure 7
<p>Cropland cultivation levels across years (<b>a</b>) and factors contributing to unsuitability for cultivation in the BSRNC (<b>b</b>).</p> "> Figure 8
<p>Spatial distribution of cropland reserves in the BSRNC (<b>a</b>) and coupling of reserves with current land use across various cultivability levels (<b>b</b>).</p> ">
Versions Notes

Abstract

:
Agricultural land resources are essential for food production, and thus it is vital to examine the spatiotemporal changes in these resources and their impacts on land suitability to optimize resource allocation. In this study, we investigated the spatial evolution of cropland resources through land use change analysis by utilizing four periods of land use data from 1990 to 2020 in the black soil region of northeast China (BSRNC). Employing niche theory, we developed a cultivability evaluation model tailored to the BSRNC, which was used to assess the impact of the spatial changes in cropland patterns over the past 30 years on land suitability. Our key findings are as follows: (1) Cropland resources have generally tended to expand in the BSRNC, with an increase of 7.16 × 103 km2 in the cultivated area and a northeastward shift in the cropland center by 52.94 km, indicating significant changes in the spatial configuration of the land. (2) The region’s cultivable land resources were substantial, covering 694.06 × 103 km2, or 55.78% of the total area, with notable spatial variability, influenced by the regional climate and topography. (3) The land cultivability has slightly improved, as shown by a 0.10 increase in the cultivability index, but a significant declining trend in the cultivability of cropland was observed after 2000. Our findings provide valuable insights to help accurately assess land productivity in the BSRNC and facilitate the sustainable use and conservation of black soil.

1. Introduction

Arable land is the fundamental basis for agricultural production and it is directly related to economic development, human well-being, and social progress [1]. Transforming the spatial configuration of arable land can lead to changes in site factors, such as climate, terrain, and soil, thereby influencing land suitability and introducing uncertainty into sustainable land use and food security [2,3]. The black soil region of northeast China (BSRNC) is among the three most favorable black soil belts for cultivation in the Northern Hemisphere and one of the key areas for food production in China [4]. Thus, it is crucial to understand the changes in the cropland resources within the BSRNC and investigate the variations in land suitability to facilitate the rational utilization and conservation of black soil, thereby safeguarding food security in China.
Due to urban sprawl, climate change, and shifts in production systems, arable land is continuously transformed into other land use types [5], thereby leading to significant changes in the spatial configuration of cultivated land. Most previous studies on changes in arable land were based on increases or decreases in the area of arable resources, particularly the spatial evolution of cultivated land patterns [6,7,8], such as overall trends and the characteristic transformations between arable land and other land types. These studies were predominantly conducted within the context of land use/land cover changes by employing established research methodologies and a standardized research paradigm. More recently, some studies have investigated how changes in the spatial configuration of cultivated land might affect its productivity and ecological conditions. Research has shown that over the past few decades, the arable land in China has tended to migrate toward the northwest, with significant conversion of arable land from plains to hilly regions, leading to varying degrees of decline in factors such as sunlight, temperature, water resources, and terrain within the arable land system [9,10]. Moreover, these studies mainly focused on whether changes occur in one or several components of the arable land system before and after migration, as well as the direction and degree of these changes. However, few studies have comprehensively considered the dynamic changes in land suitability related to various natural geographical factors, such as climate, terrain, and soil.
Recent national land survey findings indicate that new arable land in China is mainly distributed across the northeast region, especially in provinces such as Heilongjiang, Jilin, and Inner Mongolia. As the urbanization process continues to advance, the pressure to protect arable land in the BSRNC will increase [11]. Zhang et al. (2023) analyzed the spatiotemporal changes in the arable land resources in typical black soil regions of China [12] and showed that specific changes have occurred in the arable land pattern, as well as various changes in the components of the arable land system. However, the overall evolution of the spatial configuration of the arable land pattern in the BSRNC and its response in terms of land suitability to spatial changes remain unclear, thereby constraining the effective regulation of land policies by managers in this region at the macro level.
Arable land suitability is an important component of land suitability assessments and commonly understood as the sustainability of arable land use, with the aim of achieving sustainable and optimal land utilization while maximizing productivity [13]. The “Land Capability Classification System” proposed by the U.S. Department of Agriculture is an innovative assessment scheme for large agricultural land [14], which classifies land into several ordinal classes based on the strength of the natural factors limiting crop growth. In 1976, the Food and Agriculture Organization of the United Nations released the “Framework for Land Evaluation” [15] to propose a principle-based framework for land assessment that integrates data interpretation with land quality classification. Both systems have significantly influenced the development of global land evaluation frameworks and provided templates for land assessment in numerous countries, such as the national project by the Chinese Academy of Sciences to assess the land resource quality and potential, which led to the creation of the “1:1,000,000 Land Resource Map of China” [16]. These established land assessment systems share two main features: (i) the evaluation process relies heavily on expert judgment and it is often qualitative and (ii) the central evaluation principle is grounded in the contradictory unity of land limitations and suitability. Progressing from qualitative diagnostics to broader quantitative analyses in evaluations has long been a goal of the academic community. The key requirement of arable land suitability evaluation is establishing the relationships between suitability scores and the associated factors [17,18]. Thus, recent studies have employed various modeling and mathematical approaches to establish the mapping relationships between evaluation indicators and suitability, including fuzzy mathematics, multi-criteria decision models, and suitability functions [19,20]. However, the evaluation process continues to be affected by subjective elements, particularly during the assignment of weights to indicators in the analytic hierarchy process [21]. Recently, due to advances in artificial intelligence, machine learning techniques, including random forest, deep learning, and maximum entropy models, have increasingly been utilized for land evaluation [22,23,24], but these “black box” models can be significantly affected by noise and exhibit poor interpretability, which complicate the clear presentation of evaluation outcomes [25]. Consequently, there is an urgent need to develop a more straightforward, scientifically robust, and practical system for assessing land suitability. Niche models are based on the ecological niches of species or land use in the environment, they can predict land suitability through environmental variables that effectively capture complex ecological relationships, and they are also suitable for various habitat types [26]. This type of model has robust interpretability, requires minimal data, and exhibits excellent spatial adaptability, and it has been successfully utilized in land evaluation [27]. Furthermore, traditional research predominantly targeted current arable land or reserve arable resources, thereby hindering the accurate representation of the amount and spatial distribution of land available for cultivation within a region, which hinders studies of how changes in spatial arable land patterns affect suitability. Niche models can effectively combine current arable land with reserve arable land to view arable land as a dynamic system and identify ecologically suitable positions for cultivation within the area, and thus directly assess the appropriateness of the existing spatial arrangement of arable land and identify the distribution of potential cultivable land. This approach provides robust support to enhance land use decision making and management strategies.
Therefore, in the present study, we selected the BSRNC as the study area and conducted the following research: (i) Utilizing multi-temporal land use classification data, we systematically analyzed the changes in the quantity and spatial migration characteristics of the arable land resources in the area. (ii) Employing ecological niche models, we constructed a land suitability evaluation framework tailored for the BSRNC to assess the spatial distribution and quantity of arable land within the region. (iii) We investigated the effects of changes in arable land spatial patterns on land suitability. The findings obtained in this study may provide a scientific basis for the formulation and optimization of arable land protection policies in the BSRNC, as well as for the rational use and protection of black soil resources.

2. Materials

2.1. Study Area

The BSRNC (115°52′ E–135°09′ E, 38°72′ N–53°56′ N) is a relatively complete and independent natural geographical unit. Based on factors, such as the water and heat conditions, topography, soil types, and planting methods, the BSRNC can be divided into six types of regions: the Songnen Plain Region (SNP), Sanjiang Plain Region (SJP), Liaohe Plain Region (LHP), Changbaishan and Eastern Liaoning Region (CMEL), Western Sandy Region (WS), and Daxing’an and Xiaoxing’anling Region (DXXAL) (Figure 1a). The BSRNC encompasses the provinces of Liaoning, Jilin, Heilongjiang, and the eastern part of Inner Mongolia, including Hulunbuir City, Xingan League, Tongliao City, and Chifeng City (Figure 1b), with a total area of approximately 125 × 104 km2. This area is situated in the temperate monsoon climate zone, with annual active accumulated temperatures ranging from 1500 °C to 3800 °C and average annual precipitation between 450 and 850 mm. The region experiences concurrent rainfall and heat, with significant diurnal temperature variations, making it suitable for the growth of single-crop annual plants. The soil types include black soil, black calcareous soil, silt soil, meadow soil, brown soil, and dark brown soil (named according to the Chinese soil classification system) (Figure 1c). Due to the favorable resource conditions, the BSRNC has become an important grain production base in China, where the grain output accounts for about one-quarter of the national total, and the commodity grain output is about one-third of the national total [28].

2.2. Data Source and Processing

The core data sets required for this study were sourced from open websites or publicly available research results, and detailed information about the data employed is shown in Table 1. The ≥10 °C accumulated temperature data came from the National Ecosystem Science Data Center. The multi-year average precipitation data were provided by the Earth Resource Data Cloud. The DEM data were from the Geospatial Data Cloud, and the terrain slope was derived using the slope extraction tool in ArcMap 10.3. The potential annual soil erosion data were extracted from the study by Li et al. (2023) [29]. The four critical soil property data came from the China Soil Series Survey and the China Soil Series Compilation Project (2010–2018) [30]. The soil organic carbon content was converted into soil organic matter (SOM) content using the Van Bemmelen coefficient (constant 1.724). The land use and land cover (LUCC) data were sourced from the Resources and Environmental Science Data Platform. This study used the resampling tool in ArcMap 10.3 to standardize all geospatial data to a 1 km raster format. Furthermore, the multi-year food yield data for the BSRNC were taken from the “China Statistical Yearbook”.

3. Methodology

3.1. Cropland Gravity Center Model

The cropland gravity center model was used to explore the overall trends in the spatial shifts in cropland. The cropland gravity center refers to the point on the spatial plane where the cropland area within a region achieves moment equilibrium at a certain time. Its movement direction and speed effectively characterize the dynamic spatial changes in cropland resources [31]. In this study, the cropland area was used as the weight and the geometric center of cropland patches as the gravity center. The migration of the cropland gravity center was represented by combining the coordinates of the patch gravity center with weight factors, according to the following formulae:
X t = ∑ i = 1 N ( C t i · X i ) / ∑ i = 1 N C t i
Y t = ∑ i = 1 N ( C t i · Y i ) / ∑ i = 1 N C t i
where Xt and Yt represent the latitude and longitude coordinates of the cropland gravity center in year t, respectively; Cti represents the area of the i-th cropland patch in year t; and Xi and Yi represent the latitude and longitude coordinates of the cropland gravity center in the i-th year, respectively.

3.2. Ecological Niche Model

3.2.1. Ecological Niche Connotations of Cultivable Land

The niche model was applied in this study to assess land cultivability (i.e., cropland suitability). The niche concept was first applied in species biology to refer to the spatial position of an organism in its habitat and its functional role in the biological community [32], known as the “hypervolume niche”. This definition provides the theoretical foundation for systematically describing how organisms utilize environmental resources. In regional development, resources form a multidimensional demand space referred to as the “demand niche”. The degree of matching between the existing resource space in a region and the demand niche of arable land is the basis for evaluating land cultivability.
In the niche model, evaluation factors are classified into three categories. The first category includes positive factors (Equation (3)) that require meeting a minimum threshold, where higher values are advantageous until diminishing returns occur beyond a certain point, such as precipitation and accumulated temperature. The second category consists of intermediate factors (Equation (4)) with an optimal range that become limiting factors if values fall below or exceed certain thresholds, such as soil pH. The third category includes negative factors (Equation (5)) that must remain below a specified limit, where lower values are preferable, such as slope.
C i = 0             X i ≤ D i m i n X i D i m i n × 100      D i m i n ≤ X i ≤ D i o p t 100              X i ≥ D i o p t
C i = 0             X i ≤ D i m i n , X i ≥ D i o p t X i − D i m i n D i o p t − D i m i n × 100       D i m i n ≤ X i ≤ D i o p t D i m a x − X i D i m a x − D i o p t × 100       D i o p t ≤ X i ≤ D i m a x
C i = 100                     X i ≤ D i m i n ( 1 − X i − D i m i n D i m a x − D i m i n ) × 100      D i m i n ≤ X i ≤ D i m a x 0                    X i ≥ D i m a x
In Equations (3)–(5), Ci denotes the suitability index for evaluation factor i, Xi is the current value of evaluation factor i, Diopt denotes the optimal value of factor i, Dimin is the lower limit for factor i, and Dimax denotes the maximum threshold for factor i.
A vector composed of the current values of evaluation factors represents the actual niche of the land, and the indicators that influence land cultivability form an n-dimensional resource space. Land cultivability attains its optimal state when all evaluation factors reach their ideal values in a state defined as the “optimal niche” for land cultivability. The degree to which the actual niche approximates the optimal niche defines the niche suitability for land cultivability, where deviations in any factor impact the land’s overall cultivability. Consequently, the model for evaluating land cultivability is represented in Equation (6).
C = ∏ i = 1 n C i 1 / n
In Equation (6), C represents the land cultivability index, and Ci denotes the suitability index for each factor.

3.2.2. Selection of Factors for Cultivable Land Evaluation

The demand niche often spans multiple dimensions, and conducting a comprehensive analysis of all “resource dimensions” poses practical difficulties. Consequently, analyses typically prioritize key resources that are likely to serve as constraints and tailored to regional resource features. The BSRNC region mainly experiences problems such as low-temperature cold hazards, drought-induced sandstorms, soil erosion, thinning soil layers, acidification, and reduced organic matter content [33]. Based on the dialectical balance between land use limitations and suitability, we formulated an index system for land cultivability evaluation, where the model parameters were set according to pertinent national or industry standards and references (Table 2). Within the evaluation framework, soil texture functions as a discrete indicator, whereas pH is categorized as an intermediate indicator. To streamline data processing, pH was reclassified into a discrete indicator, and scores were assigned to discrete indicators to compute the cultivability index (Table 3).

3.2.3. Evaluation Results’ Verification

The correlation between actual crop yield and simulated suitability scores is frequently used to verify the effectiveness of land assessment [38,39]. Due to the absence of grid-level resolution in crop yield data, this study extended the validation scale to the municipal level. Specifically, we utilized the zonal extraction tool in ArcMap 10.3 (ESRI, Redlands, CA, USA, https://www.arcgis.com/, accessed on 14 May 2022) to obtain municipal-level land cultivability scores and conducted linear regression analysis between these scores and actual crop yields in Origin 2018 (OriginLab Corporation, Northampton, MA, USA, https://www.originlab.com/, accessed on 11 January 2018).

4. Results

4.1. Evolution of Spatial Pattern of Cropland in the BSRNC from 1990 to 2020

4.1.1. Changes in Cropland Quantity

The cropland area statistics at various time points (Table 4) indicated a non-symmetrical pattern in the BSRNC, where the cropland area increased initially, then decreasing and subsequently increasing again. In the last three decades, the overall cropland area in the BSRNC expanded from 446.62 × 103 km2 in 1990 to 453.78 × 103 km2 in 2020, with an average annual increase of 0.24 × 103 km2, corresponding to a yearly growth rate of 0.05%. In particular, the cropland area in the BSRNC declined between 2000 and 2010 but recovered from 2010 to 2020, although the growth rate was lower compared with that during 1990–2000.
Prior to 2000, China experienced considerable food supply pressure, but the cropland reserves were ample in the BSRNC region due to extensive land reclamation [12]. After 2000, the Chinese agricultural sector shifted toward sustainable intensification, resulting in lower agricultural profits. Combined with substantial population outmigration from the northeastern region and the implementation of policies such as “ecological land retirement”, this caused a decline in the cropland area [40,41]. Post-2010, previously untapped cropland reserves were reclaimed or repurposed due to the Chinese government’s heightened focus on development in the northeastern region and the introduction of policies such as the “Northeast Revitalization Strategy” [42], as well as land reclamation initiatives and the promotion of conservation tillage technologies [43], thereby facilitating a recovery in the cropland area.

4.1.2. Spatial Changes in Cropland Pattern

From 1990 to 2020, the cropland gravity center in the BSRNC consistently remained within the Songnen Plain, with a general northeastward shift, reflecting notable changes in spatial cropland patterns (Figure 2). In 1990, the cropland gravity center was situated in Nong’an County, Changchun City, Jilin Province (124°48′58″ E, 44°30′9″ N). By 2020, the cropland gravity center had moved to Fuyu City, Songyuan City, Jilin Province (125°14′8″ E, 44°52′9″ N), with a total northeastward migration of 52.94 km, averaging 1.76 km annually. There were notable differences in the direction and speed of the cropland gravity center’s migration during different phases. During 1990–2000 and 2010–2020, the migration speeds of the gravity center were relatively high, averaging 3.11 km and 2.78 km per year, respectively, with a northeastward trend during both periods. By contrast, the cropland gravity center exhibited a modest northwestward shift during 2000–2010, with a migration distance of just 7.42 km.
To detect cropland change hotspots, we applied the Create Fishnet tool in ArcMap 10.3 to generate a 10 km × 10 km grid for the BSRNC, which was overlaid with spatial cropland data to calculate the change rate within each grid cell. The hotspot regions for cropland change are illustrated in Figure 3. From 1990 to 2020, the hotspot areas for cropland expansion in the BSRNC were mainly concentrated around the arc-shaped zones of the SNP, DXXAL, and CMEL, as well as the eastern SJP and central WS, covering regions such as Heihe, Hulunbuir, Jiamusi, Harbin, and Xing’an. Conversely, cropland contraction hotspots were found in the southern WS and southwestern SNP, mainly in regions such as Tongliao, Chifeng, and Daxing.

4.2. Land Cultivability Evaluation Results of the BSRNC

4.2.1. Cultivable Land in the BSRNC

According to Shefold’s restrictive law, any deficiency in the quality of an ecological factor will lead to land being unsuitable for cultivation [44,45]. According to Equation (6), a score of 0 indicates that the land is unsuitable for farming. Conversely, regions with scores greater than 0 (C > 0) are categorized into land cultivability levels using the frequency distribution method. Specifically, the classification is as follows: uncultivable [0], marginally cultivable (0, 74.35], moderately cultivable (74.35, 84.15], and highly cultivable (84.15, 100]. The land cultivability classification results are presented in Figure 4. In terms of the spatial distribution, cultivable land was mainly located in a parallelogram-shaped region surrounded by the Greater Khingan, Lesser Khingan, and Changbai Mountains, as well as the Sanjiang Plain. The geomorphic structure was a key determinant of the spatial distribution pattern of cultivable land in the BSRNC. Highly cultivable land was primarily distributed in the central regions of the SNP, SJP, and LHP; moderately cultivable land in the border zone between the SNP and WS, western LHP, and the southern banks of the Heilong River; and marginally cultivable land in the south-central WS and southwestern LHP. In terms of quantity, the total area of cultivable land in the BSRNC was 694.06 × 103 km2, accounting for 55.78% of the region’s total area, where highly cultivable, moderately cultivable, and marginally cultivable land covered areas of 293.90 × 103 km2, 300.80 × 103 km2, and 99.36 × 103 km2, respectively, accounting for 23.62%, 24.17%, and 7.99% of the region’s total area.
The land cultivability varied significantly across different zones, and the factors that limited agricultural land use differed accordingly (Table 5). Based on their average cultivability scores, the zones were ranked as follows: SNP (83.06) > SJP (75.23) > LHP (58.24) > WS (43.29) > CMEL (36.97) > DXXAL (22.54). SNP had the highest land cultivability, with no apparent constraints on agricultural development and contiguous cropland. SJP has extensive bleached Beijiang soils, with a compact, grayish-white subsurface layer beneath the humus, where the relatively thin effective soil layers limit the agricultural potential [46]. LHP is located far from the core black soil region, and natural conditions are unfavorable for the formation of fertile black soil, resulting in low organic matter contents. The WS, CMEL, and DXXAL zones are characterized by poor land cultivability, and they accounted for 91.52% of the uncultivable land in the BSRNC. WS is characterized by arid conditions, sparse rainfall, sandy soil textures in some areas, thin soil layers, and overall poor fertility. The CMEL features high altitudes as well as steep slopes, and is situated in a humid area with ample rainfall, leading to significant soil erosion, thin arable soil layers, frequent frost events, and pronounced cold damage. The DXXAL is located at a high latitude and has the lowest effective accumulated temperature among the six zones, with a high gravel content in the soil and relatively thin soil layers.

4.2.2. Assessment of the Land Cultivability Model

Correlation analysis was conducted between the average grain yields over multiple years, and the land cultivability scores for these cities were calculated (Figure 5). In general, when the linear correlation coefficient between two parameters exceeds 0.6, the simulation results are considered to be highly accurate [47,48]. Good correlations were found between the actual crop yields and land cultivability scores (correlation coefficient = 0.659 > 0.6), thereby demonstrating that the BSRNC land cultivability model is both applicable and reliable for evaluations.

4.3. Changes in Cropland Suitability

The continuous evolution of cropland patterns indicated the imbalance in the dynamic changes in the cropland resources in the BSRNC, which affected the natural cropland resource endowments. In general, the cropland suitability has improved slightly in the BSRNC (Figure 6), where the cultivability score increased from 78.50 in 1990 to 78.60 in 2020, for a rise of 0.13%. However, the cropland suitability clearly declined after 2000. In the last three decades, the suitability indices for accumulated temperature and soil layer thickness have decreased continuously. The suitability index for accumulated temperature exhibited the largest decline, dropping from 88.44 in 1990 to 86.70 in 2020, a decrease of 1.97%. The soil layer thickness suitability index exhibited a minor reduction, decreasing by a total of 0.66 points. Conversely, the soil organic matter suitability index steadily increased from 61.51 in 1990 to 64.71 in 2020, with a gain of 3.20 points (5.20%). The precipitation suitability index also increased slightly by 0.71 points overall. The suitability indices for slope, erosion risk, pH, and soil texture largely remained stable.
The changes in the cropland areas of different cultivation levels in the BSRNC varied over time. The areas of highly cultivable and uncultivable lands tended to increase, whereas the areas of moderately cultivable and marginally cultivable lands decreased (Figure 7a). In particular, uncultivable land expanded from 33.58 × 103 km2 in 1990 to 35.19 × 103 km2 in 2020, with an average annual increase of 1.61 × 103 km2 and a yearly growth rate of 0.24%. Highly cultivable land expanded from 228.32 × 103 km2 in 1990 to 239.61 × 103 km2 in 2020, with an average annual growth of 0.56 × 103 km2 and a yearly increase of 0.25%. Conversely, the moderately cultivable and marginally cultivable land areas shrank from 148.98 × 103 km2 and 35.74 × 103 km2 in 1990, respectively, to 145.75 × 103 km2 and 33.11 × 103 km2 in 2020, with annual reductions of 0.16 × 103 km2 and 0.13 × 103 km2, and decreases of 0.10% and 0.37% per year.
The analysis of the main factors related to the unsuitability of current cropland for cultivation (Figure 7b) showed that the key factors associated with some cropland becoming unsuitable for farming were soil erosion, active accumulated temperature, and soil texture. In particular, the area of non-arable land attributable to inadequate active accumulated temperature increased yearly from 5.63 × 103 km2 in 1990 to 9.16 × 103 km2 in 2020, with an average annual increase of 0.12 × 103 km2 and a yearly growth rate of 0.07%. Conversely, the area of non-arable land attributable to poor soil texture and insufficient annual precipitation tended to decrease from 4.27 × 103 km2 and 0.53 × 103 km2 in 1990, respectively, to 2.51 × 103 km2 and 0.35 × 103 km2 in 2020, with annual reductions of 0.06 × 103 km2 and 0.006 × 103 km2 and annual declines of 1.37% and 1.13%. It should be noted that soil pH was not a major factor limiting cropland use at any time point, possibly due to the scale of this study. Previous studies of soil acidification in the BSRNC mostly focused on the plot scale [49], and localized small-scale changes may be partially masked at larger scales.

5. Discussion

5.1. Comparison with Existing Studies

The primary goals of this study were to reveal the evolution of croplands’ spatial patterns in the BSRNC, develop a model for evaluating land suitability for cultivation, and analyze changes in cropland suitability over the past thirty years. Regarding the quantity and spatial distribution of the farmland in the BSRNC, we observed an overall expansion trend (Table 4), which aligns with the findings of the National Land Survey of China [11]. Furthermore, this study quantified hotspot areas of cropland change at the grid scale and created a visual path map of cropland centroid migration (Figure 2 and Figure 3). As food security has become a global challenge, China has placed great emphasis on the protection of arable land. The BSRNC, supported by its rich natural resources, plays a strategic role in ensuring the stability of China’s food security. Therefore, the region has maintained high-intensity land use for a long time and continues to reclaim new land to support the growing demand for food production [50,51].
In cropland suitability assessments, gradient classification and the weighted summation method using land suitability functions with combined weights are commonly used [52,53]. However, the rigid classification of indicators can result in discontinuities in evaluation scores [54,55]. For instance, when the soil layer thickness increases from 49 cm to 50 cm, the score might suddenly differ by 20 points. Additionally, the weights of the evaluation indicators are generally determined by the analytic hierarchy process or expert scoring methods, which are susceptible to biases from the research context and the subjective judgments of the researchers [56,57]. The land cultivability evaluation method based on niche theory effectively minimizes the impact of excessive grading and avoids the errors introduced by subjective weighting. The evaluation results were effectively validated by comparison with actual crop yields (Figure 5). This study revealed that the highly arable land in the BSRNC is primarily found in areas like the Songnen Plain, Sanjiang Plain, and Liaohe Plain (Figure 4), where the terrain is flat, the climate is suitable, and the soil is of high quality. Other studies related to land quality or crop suitability have drawn similar conclusions [10,58]. At the same time, the BSRNC land cultivability map we created shows clear spatial differentiation. In contrast to Chen et al.’s (2023) evaluation of land suitability across China [27], our map is more practical and offers accurate references for land use adjustments in the region.
On this basis, we found that the natural background conditions of cropland in the BSRNC region have undergone complex changes. A study by He et al. (2024) pointed out that the cropland in this region is gradually expanding to higher altitudes [59], which supports our conclusion regarding the decline in temperature suitability and the rise in precipitation suitability. From the perspective of changes in cropland patterns, cultivated land expansion is primarily focused in areas like SNP, DXXAL, and CMEL, where the soil layers are relatively thin (Table 3), leading to a decrease in the suitability index for soil depth. The black soil in the BSRNC has attracted attention and is specially protected due to its high humus content. The central and northern regions of the area are characterized by long, cold winters, limited microbial activity, and plant residues that are slow to decompose, leading to the gradual accumulation of fertile black soil layers [60]. The dynamic migration of cultivated land to the northern region indicates that farmland is gradually expanding into areas with higher organic matter accumulation. Overall, the evolution of land use patterns has made the natural background conditions of farmland more complex, thus making changes in farmland suitability more intricate.

5.2. Suggestions for Protecting the Cropland in the BSRNC

During the rapid urbanization process in China, the cropland protection policy based on the cropland occupation and replenishment balance has served as a crucial regulator of land use/land cover changes [61,62]. This policy is governed by the principle that approved non-agricultural projects occupying cropland must compensate with cropland of equivalent quantity and quality to ensure the total balance. Over the last three decades, the BSRNC has achieved a near-dynamic equilibrium in cropland quantity and quality under the guidance of this policy (refer to Table 5; Figure 6a and Figure 7a). However, by 2020, considering the approved construction and ecological land, 65.38% of the cultivable land in the region had already been converted into cropland. According to the Ministry of Natural Resources’ notice on the national reserve cropland survey, we used the 2020 land cover map, excluding construction land, rivers, and other current land use types, to assess the stock of crop land reserves in the BSRNC (Figure 8). Our findings indicate that 90.19% of the cultivable land reserves consisted of forest land, medium- to high-coverage grassland, and wetlands, and 86.59% of the highly cultivable land was also categorized under these ecological land types. Converting this ecologically functional land into cropland [63] could substantially threaten the region’s ecological environment. In summary, the development of cropland in the BSRNC has reached its limits.
This paper proposes the following recommendations for farmland protection in BSRNC, considering changes in its spatial pattern and suitability: (i) Further large-scale cropland expansion should be prevented, and cropland protection should be expanded to cover all cultivable lands. For decades, the BSRNC has been one of the few regions in China to witness significant growth in cropland area, greatly contributing to national food security and social stability. The migration of the farmland center in this region has resulted in a marked decrease in natural suitability, emphasizing the need to curb excessive reclamation. Furthermore, farmland protection fundamentally aims at safeguarding land productivity; unexploited cultivable lands should be equally protected as existing farmland through spatial planning. Urban expansion should prioritize less-cultivable or marginal lands to achieve a balance between farmland development and urbanization. (ii) Tailored farmland protection policies can be adopted to improve suitability effectively. Farmland with high cultivability and superior resource conditions should receive priority protection, strictly preventing its non-grain or non-agricultural use. Farmland with moderate cultivability, while facing minor constraints, should be prioritized for development, focusing on projects like land leveling, organic matter improvement, and agricultural infrastructure. Farmland with marginal cultivability, constrained by significant limitations and low productivity, should be used cautiously with controlled intensity and appropriate utilization methods to ensure its protection. Land that is unsuitable for farming has significant limitations and high transformation costs, and improper utilization may lead to ecological problems. Such lands should be transitioned out of cultivation following the principle of “forest where suitable, grass where appropriate, and wetland where necessary”.

5.3. Uncertainty Analysis

Although this study yielded valuable results, it still has some limitations, especially with the land suitability evaluation method based on ecological niche theory. First, the farmland system is one of the most complex systems in human–environment interactions. This study mainly focused on assessing how natural land attributes support agricultural use, somewhat overlooking the influence of human activities on land suitability [64,65,66]. Due to data availability limitations, this study only obtained soil attribute data for specific time points and did not include key land quality indicators such as surface water, groundwater, and drainage capacity. Future studies should further investigate the impact of these potential factors on farmland suitability and their thresholds to optimize the performance of land evaluation models. Secondly, the data used in this study came from various sources and had some temporal inconsistencies, which may have introduced certain errors. However, topographic conditions in large areas tend to be stable over long time scales, and climate data were averaged over several years to reflect a more stable climate background, which is a common and reasonable approach in land evaluation studies [67]. More importantly, these thresholds were established for the BSRNC and may not be applicable at different research scales or data resolutions.
This study explored changes in farmland suitability from the perspective of land use spatial evolution, but it did not perform the annual monitoring of specific plots. Consequently, the changes in climate and soil conditions in the BSRNC region over the past thirty years were not fully captured. Future studies should integrate higher-precision and longer time-scale data to better capture local variations, thus enhancing the timeliness and accuracy of the evaluation.

6. Conclusions

In this study, we quantitatively assessed the dynamic changes in cropland suitability using the niche model based on the evolution of cropland spatial patterns in the BSRNC over the past 30 years. Our main conclusions are summarized as follows: (1) Over the past 30 years, the cropland area has generally increased in the BSRNC, where the center has shifted toward the northeast, indicating the gradual expansion of cropland use into cold and humid regions. (2) We developed a land cultivability evaluation model that is suitable for the BSRNC. The region has abundant cultivable land resources, where highly cultivable land is concentrated in flat areas, such as the Songnen Plain and Sanjiang Plain, and medium- and low-grade cultivable land is found in arid regions with thinner soil layers. (3) Under changes in the cropland spatial patterns, the overall suitability of cropland improved slightly in the BSRNC, but declines in suitability occurred in some areas due to limiting factors such as the temperature and soil thickness, especially in expansion areas. Moreover, the increased soil organic matter contents and improved suitability of precipitation partially mitigated some adverse factors. The findings obtained in this study provide scientific support to facilitate the enhancement in farmland management efficiency and refinement of land use policy frameworks.

Author Contributions

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

Funding

This work was supported by the National Key Technology R&D Program of the Ministry of Science and Technology of China (grant No. 2023YFD1500104), the National Natural Science Foundation of China (grant No. 42430705), and 2023 Graduate Innovation Fund Project of the China University of Geosciences, Beijing (grant No. ZD2024YC020).

Data Availability Statement

The original contributions presented in this study are included in this article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Location (a), administrative subdivisions (b), and soil types (c) in the black soil region of northeast China (BSRNC).
Figure 1. Location (a), administrative subdivisions (b), and soil types (c) in the black soil region of northeast China (BSRNC).
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Figure 2. Spatial changes in cropland gravity center.
Figure 2. Spatial changes in cropland gravity center.
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Figure 3. Hotspots for cropland change from 1990 to 2020.
Figure 3. Hotspots for cropland change from 1990 to 2020.
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Figure 4. Spatial distribution of cultivable land in the BSRNC.
Figure 4. Spatial distribution of cultivable land in the BSRNC.
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Figure 5. Relationships between actual crop yields and simulated cultivability scores for different cities in the BSRNC.
Figure 5. Relationships between actual crop yields and simulated cultivability scores for different cities in the BSRNC.
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Figure 6. Suitability of cropland resources in the BSRNC (a) and changes in single-factor suitability (b).
Figure 6. Suitability of cropland resources in the BSRNC (a) and changes in single-factor suitability (b).
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Figure 7. Cropland cultivation levels across years (a) and factors contributing to unsuitability for cultivation in the BSRNC (b).
Figure 7. Cropland cultivation levels across years (a) and factors contributing to unsuitability for cultivation in the BSRNC (b).
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Figure 8. Spatial distribution of cropland reserves in the BSRNC (a) and coupling of reserves with current land use across various cultivability levels (b).
Figure 8. Spatial distribution of cropland reserves in the BSRNC (a) and coupling of reserves with current land use across various cultivability levels (b).
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Table 1. Data applied in the present study and data sources.
Table 1. Data applied in the present study and data sources.
DataTime ResolutionSpace ResolutionFormatData Source
≥10 °C active accumulated temperature (AT)1971–20001 kmRasterhttp://www.nesdc.org.cn (accessed on 16 March 2020)
Annual precipitation (AP)1980–20181 kmRasterwww.gis5g.com (accessed on 16 June 2024)
ASTER GDEM\30 mRasterhttp://www.gscloud.cn (accessed on 16 June 2024)
Potential annual soil erosion (SEp)1999–20191 kmRasterLi et al. (2023) [29]
Thickness2010–20181 kmRasterhttp://www.geodata.cn (accessed on 22 June 2024)
Soil organic carbon2010–20181 kmRaster
Soil pH2010–20181 kmRaster
Soil texture2010–20181 kmRaster
Land use1990, 2000, 2010, 202030 mRasterwww.resdc.cn (accessed on 29 december 2023)
Table 2. Land cultivability evaluation system and ecological niche model parameters.
Table 2. Land cultivability evaluation system and ecological niche model parameters.
IndicatorsIndicator TypesOptimum ValueLimit ValueBasis for Parameterization
≥10 °C active accumulated temperature (AT)I≥3200 °C≤1800 °CStandards of surveying and evaluating reserved land resource for cultivation [34]
Annual precipitation (AP)I≥650 mm≤350 mm
Potential annual soil erosion (SEp)III50 t·ha−1·a−1300 t·ha−1·a−1Standards for classification and gradation of soil erosion [35]
SlopeIII≤2°>25°Regulation for gradation on agriculture land quality (standard No. GB/T 28405-2012); [36]
Cultivated land quality grade (standard No. GB/T 33469-2016) [37]
Soil thicknessI≥150 cm≤60 cm
Soil textureISlit, sandy loam, loam, silt loam, sandy clay loam, clay loam, silty clay loamSand
Soil pHII6.0 ≤ pH < 7.9pH < 4.5, pH ≥ 9.5
Soil organic matter (SOM)I≥4%≤0.6%
Table 3. Discrete indicator scores for land cultivability.
Table 3. Discrete indicator scores for land cultivability.
IndicatorsIndicator classificationScore
Soil textureSlit, sandy loam, loam, silt loam, sandy clay loam, clay loam, silty clay loam100
Clay, silty clay, sandy clay80
Loamy sand60
sand0
Soil pH6.0 ≤ pH < 7.9100
5.5 ≤ pH < 6.0, 7.9 ≤ pH < 8.590
5.0 ≤ pH < 5.5, 8.5 ≤ pH < 9.080
4.5 ≤ pH < 5.0, 9.0 ≤ pH < 9.560
pH < 4.5, pH ≥ 9.50
Table 4. Changes in cropland area in the BSRNC during 1990–2020.
Table 4. Changes in cropland area in the BSRNC during 1990–2020.
19902000201020201990–2020
Cropland area/103 km2446.62456.60444.81453.78—
Area proportion/%35.9336.7435.7936.51—
Amount of area change/103 km2—9.97−11.788.977.16
Rate of area change/%—2.23−2.582.021.60
Table 5. Land cultivability and effects of constraints in different zones in the BSRNC.
Table 5. Land cultivability and effects of constraints in different zones in the BSRNC.
Type RegionsCultivability ScoreAT10APSEpSlopeTextureThicknesspHSOM
SNP83.0683.4479.5894.5590.8899.7990.3796.8271.24
SJP75.2380.4392.0979.5085.8794.2467.8599.4394.18
LHP58.2499.5986.9061.7583.2499.2781.2999.8033.80
WS43.2975.1257.0978.3384.6389.8571.9295.6244.63
CMEL36.9775.3297.7937.0267.4399.5754.4895.9982.33
DXXAL22.5423.5481.1571.2076.2299.7645.3593.8296.60
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Kang, L.; Wu, K. Impact of Spatial Evolution of Cropland Pattern on Cropland Suitability in Black Soil Region of Northeast China, 1990–2020. Agronomy 2025, 15, 172. https://doi.org/10.3390/agronomy15010172

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Kang L, Wu K. Impact of Spatial Evolution of Cropland Pattern on Cropland Suitability in Black Soil Region of Northeast China, 1990–2020. Agronomy. 2025; 15(1):172. https://doi.org/10.3390/agronomy15010172

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Kang, Long, and Kening Wu. 2025. "Impact of Spatial Evolution of Cropland Pattern on Cropland Suitability in Black Soil Region of Northeast China, 1990–2020" Agronomy 15, no. 1: 172. https://doi.org/10.3390/agronomy15010172

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Kang, L., & Wu, K. (2025). Impact of Spatial Evolution of Cropland Pattern on Cropland Suitability in Black Soil Region of Northeast China, 1990–2020. Agronomy, 15(1), 172. https://doi.org/10.3390/agronomy15010172

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