Analyzing Spatiotemporal Patterns of Cultivated Land by Integrating Aggregation Degree and Omnidirectional Connectivity: A Case Study of Daqing City, China

Abstract

The spatial configuration of cultivated land is crucial for modern agricultural production; therefore, research on cultivated land aggregation and spatial connectivity holds significant importance for enhancing agricultural production efficiency and ensuring food security. This study selected Daqing City, China, as the research area and constructed a three-level nested framework of “patch–local–regional” scales. The aggregation degree was calculated through landscape pattern indices and the MSPA model, and connectivity was evaluated using the Omniscape algorithm based on circuit theory to explore the spatiotemporal evolution patterns of cultivated land configuration and analyze their spatial correlations, proposing classified optimization strategies. The results indicate the following: (1) the spatiotemporal distribution characteristics of cultivated land aggregation in Daqing City exhibit a spatial pattern of “high in the north and south, low in the middle,” with an overall declining trend from 2000 to 2020; (2) high-connectivity areas are primarily distributed in Lindian County in the north and Zhaozhou and Zhaoyuan Counties in the south, while low-connectivity areas are concentrated in the central urban area and surrounding regions; (3) the aggregation degree and connectivity demonstrate positive spatial correlation, with the Global Moran’s index increasing from 0.358 in 2000 to 0.413 in 2020; and (4) based on the aggregation degree and connectivity characteristics, the study area can be classified into four types: scattered imbalance–isolated dysfunction, regular imbalance–connected dysfunction, scattered improvement–connected optimization, and regular improvement–connected optimization. This study provides new research perspectives for cultivated land protection. The proposed multi-scale aggregation–connectivity research method and classification system offer important reference value for the efficient utilization and management optimization of cultivated land.

1. Introduction

With the advancement of agricultural modernization, the impact of cultivated land resource concentration and connectivity on agricultural production efficiency has become increasingly prominent [1]. The layout and morphology of cultivated land not only affect agricultural production efficiency and food security capacity but also influence the stability of agricultural ecosystems. Based on agricultural intensification theory, larger-scale contiguous cultivated land promotes agricultural mechanization and technological collaboration, improving cultivated land resource utilization efficiency [2]. Conversely, reduced cultivated land contiguity directly affects large-scale agricultural production, constrains the use of large agricultural machinery [3], increases production and transportation costs [4,5], and decreases cultivated land utilization efficiency [6]. Low connectivity of cultivated land affects farmland ecosystem functions while weakening agricultural production factor mobility [7]. Therefore, based on the principle of emphasizing both production and ecological functions, stable improvement in cultivated land quality can be achieved through cultivated land pattern optimization and the coordinated allocation of agricultural–ecological infrastructure. However, with accelerated industrialization and urbanization and the extensive management of cultivated land, the problem of the low aggregation and connectivity of cultivated land has become increasingly serious, affecting regional grain production capacity and weakening farmland ecosystem functions. Therefore, research on the evolutionary process of cultivated land spatial pattern morphology is of significant importance for improving agricultural production efficiency, ensuring food security, and enhancing agricultural ecosystem stability.

Previous research on cultivated land spatial characteristics primarily focused on topics such as farmland fragmentation [8]. Early studies mainly used single-dimensional evaluation indicators such as plot numbers for the basic characteristic analysis of cultivated land spatial distribution [9,10], providing static descriptions of cultivated land spatial patterns with limited capacity to characterize their complexity and change processes. After the introduction of landscape ecology theory, multidimensional landscape index systems for cultivated land aggregation characteristics were gradually constructed [11]. The use of landscape pattern index software enabled researchers to analyze cultivated land spatial heterogeneity from multiple perspectives including shape characteristics, area distribution, quantitative relationships, and spatial combinations [12]. Simultaneously, Morphological Spatial Pattern Analysis (MSPA) technology overcame the limitations of traditional evaluation methods in accurately identifying spatial structural types of and functional differences in cultivated land, studying cultivated land contiguity and fragmentation from the perspective of cultivated land structure [13]. Through mathematical morphological operations, the precise division of cultivated land landscape functional units was achieved. Combined with geographical distance decay law and ecological edge effects, pixel-scale cultivated land structural classification models based on the “patch–corridor–matrix” pattern were developed [14,15]. Early research on cultivated land connectivity characteristics was mainly limited to geometric distance and physical adjacency relationships, with less attention on the functional significance of connectivity [16]. With deepening research, the concept of functional connectivity based on biological migration characteristics and material energy flow patterns was introduced into research frameworks [17]. The application of methods such as graph theory [18], percolation theory [19], and resistance model theory [20] made landscape connectivity analysis networked, identifying key nodes and corridors that maintain overall network connectivity through establishing landscape node–connection systems. Circuit theory models further improved the scientific nature of connectivity evaluation technology by analogizing landscapes to conductor networks with different resistances, simulating current flow processes, and precisely identifying major connection channels and bottleneck locations in landscapes [21,22]. The Omniscape algorithm in circuit theory enables “omnidirectional” connectivity research, overcoming the dependency limitations of traditional methods on source and sink points, making it suitable for large-area landscape types [7,23]. As an emerging interdisciplinary application, omnidirectional connectivity models have also been used in urban open space permeability assessment [24], fire connectivity analysis [25], and urban green space system optimization [26]. In agricultural systems, this model, with its “species and process-independent” characteristics, can adapt to different agricultural regions and management mode requirements. Through multi-scale integration capabilities, it provides technical support for spatial pattern optimization in the transformation from traditional agricultural areas to modern agriculture. These connectivity theories are used to analyze relationships between landscape patterns and biodiversity maintenance and ecosystem service function performance, providing a scientific foundation for cultivated land protection strategy formulation.

In previous research, cultivated land aggregation mainly focused on analyzing cultivated land pattern characteristics within analysis units, while connectivity explored relational attributes between contiguous cultivated lands and agricultural ecosystem integrity [27,28]. Due to different emphases in aggregation degree and connectivity research on the main characteristics, pixel scale meets the refined analysis needs of the aggregation degree but fails to satisfy macro-perspective requirements in connectivity research. Therefore, most studies lack comprehensive evaluation frameworks integrating the aggregation degree and connectivity. Additionally, when constructing connectivity resistance surfaces, there is insufficient comprehensive consideration of the interaction mechanisms between human activities and natural conditions in plain areas, affecting the accuracy of connectivity evaluation. To address these issues, a multi-scale nested approach can be adopted to achieve the effective coupling of the aggregation degree and connectivity by nesting smaller-scale units within larger-scale units. This “bottom-up” nested analytical method breaks through the limitations of single-scale studies, leverages the advantages of remote sensing technology and high-resolution data, reveals cross-scale variation characteristics of the aggregation degree and connectivity, and resolves the contradiction of the difficulty in balancing microscopic details with macroscopic patterns. Meanwhile, this method identifies coupling relationships across different scales, reveals the cumulative effects of local fragmentation on regional connectivity, and the effects of human activities and natural conditions on local cultivated land integration through influencing connectivity, which helps elucidate the interaction mechanisms between agricultural production and ecological protection. Additionally, the multi-scale framework can better correspond to multi-level management systems, identify differential characteristics of different regions, and provide a scientific basis for decision-making at each administrative level. This study selected Daqing City as a case study, constructing a multi-scale nested framework that integrates landscape pattern indices and MSPA models to calculate the aggregation degree, and evaluates connectivity based on the Omniscape algorithm in circuit theory. Meanwhile, a dual-dimensional cultivated land classification system based on “aggregation–connectivity” changes and differentiated protection strategies are proposed, providing a scientific basis for the sustainable utilization of regional cultivated land resources.

Daqing City is located in the Northeast China Plain and is an important grain production area in China. Its terrain consists mainly of plains suitable for cultivation, representing typical characteristics of plain agricultural areas. As an oil field concentration area, its industrialization and urbanization levels are relatively high, with large contiguous cultivated land coexisting with fragmented cultivated land caused by urban expansion, facilitating the comparative analysis of the aggregation and connectivity of cultivated land in different locations. The study area also contains numerous wetlands, lakes, and other natural elements, making it suitable for studying cultivated land spatial pattern characteristics under the combined effects of natural and anthropogenic factors.

2. Materials and Methods

This study constructed a comprehensive evaluation framework for the cultivated land aggregation degree and connectivity based on a three-level nested perspective of “patch–local–regional” scales (Figure 1). The cultivated land aggregation degree (CLAD) and cultivated land connectivity (CLC) represent two core dimensions characterizing cultivated land spatial patterns. The CLAD primarily reflects the spatial concentration of cultivated land, focusing on the patch size, shape, and clustering degree, while CLC emphasizes physical connectivity between cultivated land units, characterizing the potential for material, energy, and information flow between cultivated lands, reflecting the integrity and coordination of cultivated land functions.

To comprehensively evaluate these two dimensions, this study adopted a multi-scale nested analytical approach. At the patch scale, the basic aggregation characteristics of cultivated land (CLADbasis) were calculated through landscape pattern indices. At the local scale, Morphological Spatial Pattern Analysis (MSPA) technology was used to identify the structural types of cultivated land (core cultivated land, edge cultivated land, perforated cultivated land, and island cultivated land), with different types of cultivated land weighted by effect values and superimposed with patch-scale basic aggregation characteristics (CLADbasis) to form the comprehensive aggregation degree (CLAD). At the regional scale, comprehensive resistance surface models were constructed, and source layers were built based on core cultivated land at the local scale to determine connectivity sources between cultivated lands. Based on this foundation, the Omniscape algorithm based on circuit theory was adopted to evaluate connectivity between cultivated lands in the study area (CLC). Through a “bottom-up” approach, small-scale aggregation analysis was combined with larger-scale connectivity assessment to further reveal the interactions between the cultivated land aggregation degree and connectivity. Based on this foundation, bivariate spatial autocorrelation analysis was employed to explore spatial coupling relationships between the aggregation degree and connectivity, and a two-level classification system was constructed according to the static conditions of and dynamic changes in these two dimensions to guide differentiated cultivated land protection and optimization strategies.

2.1. Cultivated Land Aggregation Degree Calculation Based on Landscape Pattern Indices and MSPA

2.1.1. Basic Aggregation Characteristic Identification of Cultivated Land

Calculating landscape pattern indices can precisely characterize landscape structural features and spatial distribution patterns, providing an important quantitative basis for analyzing the landscape aggregation degree and fragmentation levels. Based on existing research [29], number of patches (NP), patch density (PD), Mean Patch Size (MPS), Patch Size Coefficient of Variation (PSCOV), and Edge Density (ED) were selected as key indicators for CLADbasis identification. These indicators collectively evaluate cultivated land aggregation characteristics, comprehensively reflecting the cultivated land aggregation degree from three dimensions: quantity (NP, PD), scale (MPS, PSCOV), and shape (ED).

Using Fragstats 4.2 software, landscape pattern indices were calculated using the moving window method. Considering the significant impact of pixel granularity on calculation results, this study compared and analyzed the variation patterns and mutation point characteristics of various indicators under different granularities through multiple experiments. Combined with the actual conditions of cultivated land in Daqing City, land use data were resampled to 75 m resolution, with 450 m selected as the window edge length. Under this pixel granularity, plot details and computational efficiency can be well balanced, effectively reflecting the spatial distribution and structural characteristics of cultivated land while reducing redundant information and computational errors. After range normalization processing of the indicator results, Spatial Principal Component Analysis (SPCA) was utilized to screen redundant indicators through correlation analysis and extract main features, constructing selected landscape indices into a comprehensive index CLADbasis representing the basic aggregation characteristics.

2.1.2. Cultivated Land Aggregation Degree Calculation

Based on the study area land use data, MSPA methodology was employed, treating cultivated land as foreground data and non-cultivated land as background data. Through binary processing and mathematical morphological techniques (such as hole filling, opening and closing operations), spatial position differences between cultivated and non-cultivated landscapes were identified at the pixel scale. In Guidos Toolbox software (v2.8), cultivated land spatial types were identified based on eight-neighborhood analysis, dividing cultivated land into four categories: core cultivated land, edge cultivated land, perforated cultivated land, and island cultivated land. According to the different impacts of cultivated land types on the aggregation degree, different effect values, Ceffect, were assigned to reflect the contribution degrees of various cultivated land types in spatial aggregation.

Core cultivated land, located at the center of contiguous cultivated areas, possesses homogeneity and regularity, is isolated from surrounding non-cultivated landscapes, forms stable internal ecological environments, and serves as important carriers for cultivated land ecosystem function performance. It contributes most to the cultivated land aggregation degree and was assigned an effect value of 1. Edge cultivated land is distributed at the periphery of core cultivated land, serving as buffer zones or transition areas between core cultivated land and non-cultivated land, spatially inheriting partial functions of core cultivated land while facing interference from external non-agricultural activities. Perforated cultivated land is located in the internal gap areas of core cultivated land and, although adjacent to core cultivated land, contributes similarly to edge cultivated land due to fragmented internal spatial distribution. Therefore, both edge cultivated land and perforated cultivated land were assigned effect values of 0.7. Island cultivated land is completely surrounded by non-agricultural environments, exhibits strong isolation, has small internal areas, and is susceptible to external interference, making it difficult to maintain stable ecological functions. It contributes least to the cultivated land aggregation degree and was assigned an effect value of 0.5.

2.2. Cultivated Land Connectivity Calculation Based on Omnidirectional Connectivity

Cultivated land connectivity serves as an important indicator for measuring cultivated land spatial layout optimization and agricultural sustainable development capacity, effectively evaluating the flow smoothness of contiguous cultivated land in terms of material flow, energy flow, and information flow. Its quantitative evaluation holds important guiding significance for optimizing regional cultivated land spatial patterns and enhancing agricultural ecosystem functions. To accurately assess cultivated land connectivity in the study area, this research adopted an analytical framework based on circuit theory. Firstly, comprehensive resistance surface models were constructed from two dimensions: natural geographical conditions and human activity intensity. Based on this foundation, the Omniscape algorithm based on circuit theory was employed to calculate cultivated land omnidirectional connectivity and evaluate connectivity within the study area.

2.2.1. Resistance Surface Construction

Based on factors influencing cultivated land connectivity in the study area and following existing research [7,30], comprehensive resistance surface models were constructed. Considering natural geographical conditions (topography, water systems), artificial facilities (transportation networks), and land use intensity, the resistance characteristics of agricultural land (including cultivated land), water systems, transportation networks, and residential areas were analyzed. Resistance surface construction for agricultural land areas adopted a method combining static baselines and dynamic adjustments.

Firstly, baseline intensity values, L, for different agricultural land use types were established based on vegetation coverage characteristics [31]: 0.5 for cultivated land, 0.4 for grassland, and 0.2 for forest land. This assignment reflects the degrees of human intervention in different land use types, with forest land assigned lower intensity values due to the retention of more natural vegetation and less human influence. For cultivated land and grassland subject to greater agricultural management intensity, dynamic adjustments were made by introducing the Normalized Difference Vegetation Index (NDVI) coefficient of variation (cvNDVI) to reflect the actual intensity of agricultural management. Based on NDVI time series changes during growing seasons, the coefficient of variation (cvNDVI) was calculated to characterize the spatial differences in management intensity. Finally, considering the topographic factors’ impact on biological movement, the slope percentage was incorporated into the agricultural land resistance value calculation formula:

R = (L + 1)^10 + s/4,

(1)

where R represents the resistance value, L represents the land use intensity, and s represents the slope percentage. For water bodies exceeding 100 square meters in area, considering their significant obstruction to terrestrial biological movement [4], a unified value of 1000 was assigned.

Transportation road area resistance assignment was based on roads’ physical characteristics and usage intensity. Highways, due to their wide surfaces and high-speed traffic flow, constitute major obstacles to biological crossing and were assigned the highest resistance value of 900. Railways (including conventional railways and high-speed railways), due to their fixed pathways, high-speed train traffic, and line enclosure characteristics, constitute significant barriers and were assigned a value of 850. Secondary roads mainly connect towns and larger rural areas, with their lower traffic flow reducing the resistance values to 600. Notably, while the railway resistance values are slightly lower than highways, they remain significantly higher than secondary roads (difference of 250), reflecting their strong barrier effects on landscape connectivity.

Resistance assessment for urban and rural residential areas adopted a three-level classification system based on nighttime light intensity, with fine-tuning based on population density data. High-intensity areas such as commercial centers and industrial zones were assigned resistance values of 800–950, general urban areas 650–800, and low-intensity areas such as residential and suburban areas 500–650. Within areas of identical light intensity, resistance values were adjusted upward or downward based on the population density levels to reflect internal regional differences.

This multi-level resistance surface construction method, with resistance factors divided into three layers (land use type and intensity, natural geographical conditions, and human activity intensity) for superimposed assignment, resulted in final resistance values distributed between 1 (natural landscape) and 1032 (highly modified landscape) [5], comprehensively reflecting different landscape types within the study area, particularly the degree of human activity intensity obstruction to connectivity.

2.2.2. Source Intensity and Connectivity Calculation

To accurately assess landscape spatial connectivity, this study employed the Omniscape algorithm based on circuit theory [32] to calculate the omnidirectional connectivity between cultivated lands. This method, developed in Julia programming language, treats landscapes as conductor networks where each grid cell can serve as current source–sink points. Connectivity is evaluated by calculating the cumulative current flow in landscapes, considering not only optimal paths but also all possible movement paths. Omniscape calculates the connectivity for each area incrementally through moving windows, avoiding the discretization processing of source–sink points in traditional methods. This algorithm is particularly suitable for evaluating the overall connectivity of large-scale regions and can accurately identify connectivity bottleneck areas.

To calculate cultivated land connectivity, firstly, based on the core cultivated land determined by MSPA, minimum area thresholds for contiguous cultivated land were established by analyzing changes in patch numbers and area proportions with minimum area thresholds; 15 km² was determined as the minimum area threshold for contiguous cultivated land, which retains over 85% of the core cultivated land area while effectively filtering small fragmented patches. Secondly, the source intensity (ranging 0–1) for each pixel of contiguous cultivated land was calculated, using 1-L to represent the ecological integrity of each pixel. Finally, “coreless” connection models were constructed using the Omniscape algorithm, simulating the potential movement paths of each source point on resistance surfaces through moving window methods, with cumulative flow as connectivity measurement indicators. Based on the actual study area conditions and previous research [7], this study selected 3 km as the moving window radius, which adapts to the activity ranges of beneficial organisms such as pollinators and natural enemies in farmland ecosystems, facilitating agricultural ecosystem service function performance.

2.3. Spatial Coupling Relationship Analysis

To explore the spatial association characteristics and heterogeneity between the cultivated land aggregation degree (CLAD) and connectivity (CLC), bivariate spatial autocorrelation analysis methods were employed. Following similar studies [33], the study area was divided into 1200 m × 1200 m grids, and GeoDa spatial analysis software (v1.20) was used to conduct the spatial correlation analysis of the CLAD and CLC through the establishment of spatial weight matrices. This grid size balances computational efficiency and spatial analysis stability. Bivariate spatial autocorrelation analysis includes two levels: Global Spatial Autocorrelation (Global Moran’s I) and Local Spatial Autocorrelation (Local Indicators of Spatial Association, LISA). Spatial weight matrices were constructed based on Queen contiguity standards, bivariate LISA analysis was conducted for each period, grid type quantity proportions were calculated, and the evolutionary characteristics of spatial association patterns from 2000 to 2020 were analyzed.

2.4. Classification Integrating Cultivated Land Aggregation Degree and Connectivity Characteristics

County-level cities undertake core functions of implementing superior policy requirements and organizing protection actions in cultivated land protection systems. Based on the characteristics of the cultivated land aggregation degree (CLAD) and connectivity (CLC), this study constructed a two-level classification system to identify the protection and utilization states of county-level cultivated land within the study area. Firstly, according to the relative levels of the CLAD and CLC, cultivated land in the study area was divided into four primary types through the natural breaks method: high aggregation–high connectivity, high aggregation–low connectivity, low aggregation–high connectivity, and low aggregation–low connectivity. Classification results can intuitively reflect the aggregation–connectivity characteristics of regional cultivated land. Further, considering the dynamic change trends of both indicators, 16 secondary types (

Table 1. Two-level classification system integrating cultivated land aggregation degree and connectivity characteristics.

Primary Classification	Aggregation Change	Connectivity Change	Secondary Classification
High Aggregation–High Connectivity	Improvement	Optimization	Regular Improvement–Connected Optimization
	Improvement	Dysfunction	Regular Improvement–Connected Dysfunction
	Imbalance	Optimization	Regular Imbalance–Connected Optimization
	Imbalance	Dysfunction	Regular Imbalance–Connected Dysfunction
Low Aggregation–High Connectivity	Improvement	Optimization	Scattered Improvement–Connected Optimization
	Improvement	Dysfunction	Scattered Improvement–Connected Dysfunction
	Imbalance	Optimization	Scattered Imbalance–Connected Optimization
	Imbalance	Dysfunction	Scattered Imbalance–Connected Dysfunction
High Aggregation–Low Connectivity	Improvement	Optimization	Regular Improvement–Isolated Optimization
	Improvement	Dysfunction	Regular Improvement–Isolated Dysfunction
	Imbalance	Optimization	Regular Imbalance–Isolated Optimization
	Imbalance	Dysfunction	Regular Imbalance–Isolated Dysfunction
Low Aggregation–Low Connectivity	Improvement	Optimization	Scattered Improvement–Isolated Optimization
	Improvement	Dysfunction	Scattered Improvement–Isolated Dysfunction
	Imbalance	Optimization	Scattered Imbalance–Isolated Optimization
	Imbalance	Dysfunction	Scattered Imbalance–Isolated Dysfunction

) were subdivided to support differentiated county-level cultivated land protection strategy formulation. Each classification name adopts an “aggregation state–aggregation change, connectivity state–connectivity change” combination pattern, where aggregation states use “regular” and “scattered” to represent high and low aggregation degrees, respectively, aggregation changes use “improvement” and “imbalance” to represent increases and decreases in the aggregation degree, respectively, connectivity states use “connected” and “alienation” to represent high and low connectivity, respectively, and connectivity changes use “optimization” and “disorder” to represent increases and decreases in connectivity, respectively.

2.5. Study Area Overview and Data Sources

Daqing City (Figure 2) (45°46′ N–47°27′ N, 123°31′ E–125°42′ E) is located in the northern Songnen Plain in western Heilongjiang Province, China, with a total area of 21,000 km². The city administers 9 districts and counties, with approximately 2.78 million permanent residents in 2020 and an urbanization rate of 72.5%. As an important component of the Songnen Plain grain production area, Daqing City’s cultivated land area covers 10,653.64 square kilometers, accounting for 50.23% of the total area, primarily producing corn, soybeans, and rice, with annual grain production exceeding 12 million tons. Daqing City is located in a temperate continental monsoon climate zone with annual average precipitation of 644 mm, mostly concentrated during growing seasons. The terrain is flat with soils mainly consisting of highly fertile black soil and meadow soil. Abundant wetland resources and the Songhua River system provide excellent irrigation support for agriculture. However, with advancing urbanization and industrialization and the implementation of the Grain for Green Program, the cultivated land area decreased by approximately 107.7 km² from 2000 to 2018, mainly converting to construction land and forest land. The urban construction land area reached 947.35 km² in 2020, squeezing the agricultural production space and gradually fragmenting cultivated land spatial patterns.

This study employed CLCD land cover data as the fundamental data source for land use change analysis. CLCD data, provided by Wuhan University, span 1990–2020 with 30 m spatial resolution, effectively capturing land use/cover change characteristics within the study area. For connectivity resistance surface analysis, OpenStreetMap road data, NPP-VIIRS nighttime light data (500 m resolution) provided by the Chinese Academy of Sciences, NDVI data from NASA’s MOD13A3 dataset (1 km resolution, 2000–2020 timespan), and population density data from WorldPop (1 km resolution) were used. All data were resampled to 30 m spatial resolution and unified under WGS 84-based Albers equal-area conic projection. Through the integration of multi-source, multi-scale, multi-temporal spatial data (

Table 2. Data sources and specifications used in this study.

Data Type	Data Source	Spatial Resolution	Temporal Coverage	Purpose
CLCD Land Cover Data	Wuhan University	30 m	1990–2020	Land use change analysis
Road Network Data	OpenStreetMap https://www.openstreetmap.org (accessed on 15 March 2025)	Vector data	2020	Resistance surface construction
NPP-VIIRS Nighttime Light Data	Chinese Academy of Sciences https://www.resdc.cn (accessed on 20 March 2025)	500 m	2000–2020	Human activity intensity assessment
NDVI Data	NASA MOD13A3 https://www.earthdata.nasa.gov (accessed on 10 April 2025)	1 km	2000–2020	Vegetation coverage analysis
Population Density Data	WorldPop https://www.worldpop.org (accessed on 25 April 2025)	1 km	2000–2020	Population distribution analysis
Digital Elevation Model	SRTM	30 m	2000	Slope calculation for resistance surface
Administrative Boundaries	National Geomatics Center of China	Vector data	2020	Study area delineation
Socioeconomic Data	Daqing Statistical Yearbook	—	2000–2020	Study area overview and discussion

), this study comprehensively reflects the cultivated land pattern change characteristics within the study area.

3. Results

3.1. Basic Aggregation Characteristics of Cultivated Land

Landscape pattern indices for the entire study area were calculated and analyzed at 75 m pixel granularity. The results showed (

Table 3. Landscape pattern indices of cultivated land in Daqing City from 2000 to 2020.

Year	NP	PD	ED	AREA_MN	AREA_CV
2000	16,249	0.7667	33.4763	93.252	12,077.45
2010	26,312	1.2415	36.7322	49.8273	10,710.28
2020	17,670	0.8338	34.6346	81.3016	12,384.43

) that from the landscape quantity dimension, number of patches (NP) exhibited fluctuating changes with an initial increase followed by decline. The number of patches was 16,249 in 2000, significantly increasing to 26,312 in 2010, representing a 61.93% increase. From 2010 to 2020, the patch numbers decreased to 17,670, declining 32.84% from 2010 levels but remaining higher than 2000 levels, with an overall increase of 8.74%. The patch density (PD) change trends remained consistent with the patch numbers, rising from 0.7667 in 2000 to 1.2415 in 2010, then declining to 0.8338 in 2020. These changes indicated that cultivated land fragmentation in the study area experienced a process of initial increase followed by deceleration, but the overall trend remains fragmentation intensification.

From the landscape scale dimension, the mean patch area (AREA_MN) exhibited a “V-shaped” change trend opposite to patch numbers. The mean patch area was 93.252 in 2000, dramatically declining to 49.8273 in 2010, a 46.57% decrease. It recovered somewhat in 2020, reaching 81.3016, representing a 63.16% increase from 2010 but still not returning to 2000 levels. The Patch Area Coefficient of Variation (AREA_CV) showed fluctuating changes, rising from 12.1‰ in 2000 to 12.4‰ in 2020, with an interim decline to 10.7‰ in 2010. These changes reflect significant decline in cultivated land contiguity within the study area, with increased heterogeneity in patch scales.

From the landscape shape dimension, Edge Density (ED) changes were relatively moderate, rising from 33.4763 in 2000 to 36.7322 in 2010 (9.73% increase), then slightly declining to 34.6346 in 2020, still higher than 2000 levels. This indicated that the cultivated land landscape shape complexity showed an overall increasing trend during the study period.

To eliminate the impact of dimensional differences between indicators, range normalization methods were employed to normalize landscape indicators. Standardized indicator values were all within the [0,1] interval, maintaining relative change characteristics of the original data. Correlation analysis of the standardized landscape indices revealed a significant positive correlation between the number of patches (NP) and patch density (PD) (R² = 0.99, p < 0.01), indicating information redundancy between these two indicators. Considering that patch density better reflects cultivated land patch distribution within unit areas, patch density (PD) was retained while mean patch area (AREA_MN), Patch Area Coefficient of Variation (AREA_CV), and Edge Density (ED) were selected for subsequent analysis. This indicator screening ensured the precision and efficiency of subsequent analyses.

Spatial Principal Component Analysis was conducted on the four retained landscape pattern indices. The results indicate that the cumulative variance contribution rates of the first and second principal components reached 91.47%, 91.51%, and 91.02% for 2000, 2010, and 2020, respectively. The first principal component variance contribution rates all exceeded 80% (83.35% in 2000, 81.29% in 2010, 81.93% in 2020), while the second principal component variance contribution rates ranged between 8 and 10% (8.12% in 2000, 10.22% in 2010, 9.09% in 2020). CLADbasic constructed based on the principal component variance contribution rates (Figure 3) exhibits spatial distribution characteristics of “high in north and south, low in middle” with an overall declining trend from 2000 to 2020, with the most significant changes occurring from 2000 to 2010.

3.2. Cultivated Land Aggregation Degree Characteristics

According to the MSPA results (

Table 4. MSPA results of cultivated land types in Daqing City.

Year	Cultivated Land Type	Pixel Count	Area (km2)	Percentage (%)
2000	Core cultivated land	1,919,983	10,794.28	71.22
	Edge cultivated land	554,786	3120.67	20.6
	Perforated cultivated land	176,338	991.9	6.55
	Island cultivated land	42,875	241.85	1.59
2010	Core cultivated land	1,542,911	8678.87	67.4
	Edge cultivated land	594,631	3344.81	25.93
	Perforated cultivated land	122,720	690.3	5.35
	Island cultivated land	32,606	326.6	1.32
2020	Core cultivated land	1,740,051	9787.79	68.51
	Edge cultivated land	584,301	3286.69	22.97
	Perforated cultivated land	171,426	964.27	6.74
	Island cultivated land	47,876	269.3	1.88

), the core cultivated land proportion in the study area declined from 71.22% in 2000 to 68.51% in 2020, edge cultivated land increased from 20.60% to 22.97%, and the combined proportions of perforated and island cultivated land increased from 8.14% to 8.62%. Changes from 2000 to 2010 were most significant, with core cultivated land declining to 67.40% and edge cultivated land rising to 25.93%, with this trend moderating after 2010

Based on the CLADbasic spatial distribution patterns and combined with the effect values of different cultivated land structural types obtained from the MSPA, the cultivated land aggregation degree, CLAD, for the study area from 2000 to 2020 was calculated (Figure 4). The spatial characteristics of both indicators are similar, with high-value areas mainly distributed in the northern and southern parts of the study area, and the low-value areas concentrated along the central urban development axes. In 2000, large high-value areas formed in the northern and southern parts of the study area, while in 2010, the central low-value area ranges significantly expanded, with some original medium–high-value areas downgraded to medium–low-value areas, and the northern and southern high-value area coverage also decreased. By 2020, the central low-value area expansion trends were curbed, with the aggregation degree slightly recovering in some areas, though the overall levels remained lower than in 2000. Compared to CLADbasic, the CLAD more accurately identified the central low-value area ranges through the integration of cultivated land morphological characteristics, avoiding over-exaggeration, and exhibited more concentrated spatial distribution in high-value areas, more precisely reflecting the spatial heterogeneity of the cultivated land aggregation degree in the study area, particularly evident in urban–rural transition zones.

3.3. Cultivated Land Connectivity Analysis

Based on the Omniscape algorithm calculation results (Figure 5), areas with the strongest connectivity in the study area (connectivity values > 10.2) are primarily distributed in eastern Lindian County in the north and eastern Zhaozhou County and southern Zhaoyuan County in the south. Medium-connectivity areas (connectivity values 4.7–7.8) are distributed in bands around high-connectivity areas. Low-connectivity areas (connectivity values < 4.7) are mainly concentrated in central urban areas such as Saertu District and Ranghulu District and their surroundings. Although western Dorbod Mongol Autonomous County possesses large areas of cultivated land resources, the connectivity levels are relatively low (4.7–7.8).

From the temporal evolution characteristics, 2000–2010 was the most significant period for cultivated land connectivity changes in the study area. In the transition zones between Ranghulu District and Saertu District, the connectivity values declined from 4.7–7.8 to 1.8–4.7. From 2010 to 2020, extremely low-connectivity areas with values below 1.8 formed around Saertu District. The southern part of the study area exhibited obvious east–west connectivity gradient differentiation, with the connectivity significantly declining in the western areas of Zhaozhou and Zhaoyuan Counties near urban areas, while the eastern areas farther from urban centers maintained higher connectivity levels.

3.4. Spatial Coupling Relationship Analysis Results

The spatial autocorrelation results for the cultivated land connectivity (CLC) and aggregation degree (CLAD) (Figure 6) show the Global Moran’s Index rising from 0.358 in 2000 to 0.413 in 2020, demonstrating positive spatial correlation. In spatial distribution, high–high and low–low dominant patterns formed stable spatial configurations. High-connectivity–high aggregation areas are mainly distributed in eastern Lindian County and southern Zhaozhou and Zhaoyuan Counties, forming concentrated contiguous distribution patterns. Low-connectivity–low aggregation areas are concentrated in central urban areas such as Ranghulu and Saertu Districts and their surroundings. High-connectivity–low aggregation types are mainly scattered in the northern and southeastern marginal areas of the study area, while low-connectivity–high aggregation types are scattered in urban peripheral areas such as Ranghulu and Saertu Districts.

From quantitative composition perspectives, high-connectivity–high aggregation types account for the largest proportion, followed by low-connectivity–low aggregation types, with high-connectivity–low aggregation and low-connectivity–high aggregation types accounting for very small proportions. From 2000 to 2020, both high-connectivity–high aggregation and low-connectivity–low aggregation types exhibited trends of an initial increase followed by decrease, peaking in 2010. High-connectivity–low aggregation types showed fluctuating upward trends, while low-connectivity–high aggregation types demonstrated fluctuating downward trends.

3.5. Aggregation–Connectivity Classification

During the study period, the aggregation–connectivity characteristics of various counties and districts in Daqing City exhibited significant differences (Figure 7). Dorbod Mongol Autonomous County, Honggang District, Longfeng District, Saertu District, and Datong District belong to the scattered imbalance–isolated dysfunction category. Lindian County and Zhaoyuan County demonstrated regular imbalance–connected dysfunction characteristics. Ranghulu District exhibited scattered improvement–connected optimization characteristics. Zhaozhou County belongs to the regular improvement–connected optimization category.

From 2000 to 2020, the cultivated land aggregation degree and connectivity in most counties and districts showed declining trends, with only Ranghulu District and Zhaozhou County demonstrating improvement trends in some areas. From spatial distribution perspectives, scattered imbalance–isolated dysfunction cultivated land is most widely distributed, mainly concentrated in the northern and western parts of the municipal area. Regular imbalance–connected dysfunction types are mainly distributed in eastern and southern municipal areas, while improvement types mainly appear in central urban peripheral areas.

4. Discussion

4.1. Cultivated Land Connectivity Evaluation Based on Omnidirectional Connectivity Algorithm

Omnidirectional connectivity, as a relatively new modeling framework [34], implemented through the Omniscape algorithm based on Julia language, has been found through our research to be well-suited for application in farmland landscape analysis. This model does not require predefined source areas but allows researchers to use large-scale regions as source layers with established hierarchies, thereby focusing on potential connectivity across entire ranges [35]. This characteristic is reflected in this study’s method of determining core cultivated land based on MSPA at local scales and conducting source intensity grading. Simultaneously, compared to traditional circuit theory that primarily focuses on the point–line elements of network structures, omnidirectional connectivity models focus on potential connectivity at all locations within entire regions [36], making them more suitable for large-scale cultivated land connectivity evaluation in plain agricultural areas. This method demonstrates obvious application potential in large-scale agricultural systems and has been successfully applied to agricultural land connectivity evaluation in the United States [7,23]. Additionally, this method conducts universal analysis based on landscape resistance with species-independent characteristics, not relying on specific species’ ecological habit data, making it applicable to areas lacking species information [37,38]. In cultivated land protection research, this species-independent connectivity analysis method holds important practical value, providing feasible technical pathways for agricultural regions. Multi-scale integration capabilities enable it to obtain regional overall connectivity patterns while retaining local-scale detail information [39]. In spatial zoning aspects, general research typically divides the connectivity space into four types based on the normalized current density: channelized areas, aggregated areas, dispersed areas, and blocked areas [40]. This study, however, conducts analysis based on the cumulative current density, zoning according to static conditions, and dynamic changes in the aggregation degree and connectivity, employing county-level administrative units for classification. This county-level classification system better aligns with actual cultivated land protection management needs while leveraging the technical advantages of omnidirectional connectivity methods based on rasterized connectivity surface analysis [41,42].

4.2. Evolution Analysis of Cultivated Land Aggregation–Connectivity Characteristics

Whether through landscape pattern index analysis, morphological analysis, or Omniscape model analysis, all demonstrate that the cultivated land aggregation degree and connectivity in Daqing City exhibited overall declining trends during the study period. The MSPA results show the core cultivated land proportion declined by 2.71%, while the edge cultivated land proportion increased by 2.37 percentage points. The connectivity quantitative analysis also indicates the average connectivity levels declined from 6.138 in 2000 to 5.887 in 2020, reaching minimum values of 4.787 in 2010. Notably, this decline was not a continuous decreasing process but exhibited obvious staged characteristics, with dramatic deterioration from 2000 to 2010 and improvement beginning from 2010 to 2020 without recovering to the initial levels.

This change may be related to multiple anthropogenic factors. Both the aggregation degree and connectivity improvement depend on cultivated land protection, which often exists in land use competition relationships with urbanization. Daqing City’s permanent resident urbanization rate reached 72.50%, far exceeding the 2020 national average of 63.89% [6], while impervious surfaces increased 44.59% during the study period [7], particularly 29.25% growth from 2000 to 2010, highly coinciding with the cultivated land pattern deterioration period found in this study. Urbanization processes not only directly encroach upon contiguous cultivated land but also reduce landscape connectivity through road networks and construction land expansion, forming connectivity barrier effects, consistent with the findings in this study [43,44]. Distribution maps show that Ranghulu District, as an urban–rural transition zone, experienced dramatic declines in both the aggregation degree and connectivity in 2010, illustrating the negative impacts of urban development on cultivated land patterns. As a typical petroleum resource city, industrial land expansion significantly impacts cultivated land patterns, with petroleum development facilities generating substantial bare land around them, further fragmenting farmland landscapes. Related research shows that petroleum development activities in Daqing City significantly impact land cover pattern changes [45]. Notably, forest land coverage expanded dramatically during the study period, with area growth of 325.41%, mainly concentrated from 2000 to 2010, with the implementation of the Grain for Green Program significantly impacting the original cultivated land spatial patterns [46]. Spatial pattern improvement after 2010 may be related to strengthened cultivated land protection policies [8], such as cultivated land red lines and compensation-before-occupation policies proposed around 2010. The average connectivity levels recovered 23%, the cultivated land area rebounded 9.56% from 2010 to 2020, while the construction land expansion speed significantly decelerated, with growth rates declining from 29.25% to 9.67%. Simultaneously, the comprehensive implementation of land consolidation projects and black soil protection in Northeast China, with high-standard farmland construction through plot consolidation, road network optimization, and irrigation–drainage system improvement, not only enhanced the cultivated land aggregation degree but also significantly improved farmland ecological connectivity, providing favorable conditions for agricultural production factor flow and biological migration [47]. Significant improvement in the aggregation degree in areas such as Ranghulu District and connectivity recovery in areas such as eastern Lindian County indicate that policy regulation based on cultivated land protection orientation is effectively alleviating cultivated land fragmentation problems caused by early rapid development.

During the study period, high-value areas of the cultivated land aggregation degree and connectivity were mainly distributed in northern Lindian County and narrow zones in the southernmost Zhaozhou and Zhaoyuan Counties, while low-value areas concentrated in Daqing’s main urban area and Dorbod County. Ranghulu District, located between them, also formed east–west-oriented low-value connectivity corridors, with low-value zones also forming at boundaries between lake-abundant Zhaoyuan County and Datong District, more evident in the connectivity. Research by Wang et al. [48] found cultivated land was one reason for the increased soil organic carbon storage in Daqing City over the past decade, with the soil organic carbon mass fractions exhibiting high spatial distribution in the northeast, corresponding to the large high-value areas such as Lindian County found in this study, reflecting the relationships between cultivated land spatial patterns and ecological functions to some extent.

Daqing City combines the characteristics of petroleum resource cities, major grain production areas, and abundant lakes and water bodies. As a resource city, central urban areas form “multi-point–long-line” clustered spatial structures due to petroleum development activities [49], not traditional concentrated clustering patterns but several scattered small towns connected through linear elements in multi-point cluster forms. Linear infrastructure (roads, pipelines, etc.) connecting various town nodes creates strip-like division effects on surrounding cultivated land, explaining the formation of strip-like low-value areas in regions such as Ranghulu District to some extent. Daqing City is located in fertile black soil regions as an important grain production area, with cultivated land covering 50.23% of the total area. Peripheral agricultural areas benefit from the flat terrain conditions of Songnen Plain, with the natural conditions favorable for large-scale contiguous agricultural production [50], explaining why both sides of the study area exhibit high-value distribution characteristics. Abundant lakes and water bodies, with extensive water body distribution in water-rich regions, form natural barriers to cultivated land continuity [51], spatially fragmenting cultivated land and resulting in lower connectivity.

4.3. Spatial Coupling Relationship Between Aggregation Degree and Connectivity

Bivariate spatial autocorrelation analysis shows high–high and low–low clustering as dominant patterns with few mismatch types, indicating the aggregation degree and connectivity demonstrate significant positive coupling. Statistical characteristics verify that both do not change independently but exhibit intrinsic spatial synergistic relationships.

Higher aggregation degrees typically mean more regular patch morphology and larger contiguous cultivated land areas, providing foundational conditions for improving farmland connectivity. Conversely, good connectivity can promote the concentrated contiguous distribution of cultivated land, reducing the fragmentation degrees. However, urbanization and land use changes erode the edge areas of contiguous cultivated land, resulting in reduced biological habitat that destroys corresponding ecosystems, fragments populations, and reduces biodiversity [52], leading to decreased connectivity and entering vicious cycles. Liu’s research on the Min Triangle region also found significant positive coupling relationships between the structural connectivity and functional connectivity constructed from landscape pattern indices [33], consistent with the findings in this study. Our multi-scale analysis also aligns with these coupling relationships: at patch scales, the basic aggregation characteristics based on landscape pattern indices show spatial correspondence relationships with the connectivity levels; at local scales, the core cultivated land is mainly distributed in high–high clustering areas, while fragmented types such as edges and islands appear more in low-connectivity areas; at regional scales, the overall spatial patterns exhibit synergistic change characteristics in the aggregation degree and connectivity. From agricultural ecological function perspectives, agricultural landscapes provide multiple ecosystem services [53], while habitat integration can support beneficial insect populations, stimulating the provision of ecosystem services such as biological control and pollination [54]. Conservation agriculture practices show that conservation measures including contiguous management can significantly improve ecosystem service levels, with provisioning service values 16.2–23.7% higher than conventional scattered management [55]. This can explain the reasons for positive coupling to some extent.

This coupling relationship also has applicability for achieving sustainable agriculture. Intensive agriculture requires the transition toward greater sustainability, potentially requiring the redesign of agricultural landscapes [56,57]. Synergistic protection of the aggregation degree and connectivity provides a scientific basis for formulating similar regional agricultural landscape optimization strategies, holding important significance for maintaining agricultural ecosystem service functions and promoting agricultural sustainable development.

4.4. Differentiated Management Strategies for County-Level Cultivated Land

Based on the classification results of the aggregation degree and connectivity characteristics, the large-scale management of cultivated land in plain agricultural areas and the maintenance of agricultural ecological networks exhibit intrinsic consistency. Different types of regions should adopt differentiated protection strategies according to their aggregation–connectivity characteristics.

Four cultivated land types within the study area demonstrate typical aggregation–connectivity evolution patterns, holding important enlightening significance for exploring cultivated land spatial pattern optimization in plain agricultural areas. Daqing City, as an important component of Songnen Plain, provides beneficial experience for similar regions through its cultivated land protection practices.

Dorbod Mongol Autonomous County, Honggang District, Longfeng District, Saertu District, and Datong District belong to scattered imbalance–isolated dysfunction categories, representing the most problematic regional types. Dorbod Mongol Autonomous County has water body proportions reaching 13.78% and grassland landscape proportions of 10.43%. Extensive wetland resource distribution forms natural barriers to cultivated land connectivity, requiring the construction of coordinated development models between wetland protection and farmland contiguity. Through the construction of ecological transition zones between wetlands and farmlands, both wetland ecological functions and the relative integrity of farmland space can be maintained, utilizing natural water system networks to construct ecological connection channels between farmlands. Saertu and Honggang Districts, affected by industrial development, exhibit point-like fragmented cultivated land distribution, requiring cluster well technology and underground pipeline networks to reduce surface facility division effects on farmlands. Abandoned or inefficient industrial land should be reclaimed as farmland, establishing petroleum enterprise ecological compensation mechanisms specifically for farmland consolidation and connectivity improvement. Longfeng and Datong Districts, located at urban expansion frontiers, face continuous land use competition pressure for cultivated land. Permanent basic farmland protection systems should be established, agricultural buffer spaces constructed at urban edges, and fragmented suburban cultivated land converted to efficient facility agriculture to improve the economic output per unit area.

Lindian and Zhaoyuan Counties exhibit regular imbalance–connected dysfunction characteristics, maintaining relatively high spatial aggregation levels while the connectivity shows declining trends. Lindian County should fully utilize the flat terrain advantages of Songnen Plain, constructing thousand-acre contiguous operation units through further farmland spatial integration, supporting the construction of large agricultural machinery operation road networks, adopting grid layouts to create farmland protective forest belts, and forming modern farmland landscape structures with networked farmland infrastructure layouts. Zhaoyuan County should rely on Songhua River water system resources, constructing farmland connectivity networks with water systems as backbones, enhancing the hydraulic connections between different farmland units through the integrated design of farmland irrigation–drainage systems, and constructing ecological buffer zones along major rivers to fulfill the composite functions of flood control, storage, and biological corridors.

Ranghulu District belongs to the scattered improvement–connected optimization categories, with prominent cultivated land fragmentation problems but continuously improving connectivity. This region, affected by urban expansion, forms numerous scattered small plots, requiring focused improvement in the aggregation degree. Through small-to-large field consolidation and scattered plot integration, fragmented cultivated land should be reorganized into large-scale management units. Utilizing connectivity improvement trends, field road and farmland water system planning should be strengthened, establishing ecological corridors connecting scattered farmlands to form integrated urban–rural farmland connectivity networks. As convergence areas of petroleum development and urban expansion, petroleum pipeline, urban road, and farmland water system layouts should be coordinated to avoid repeated fragmentation. Combined with resource city transformation needs, “multi-point–long-line” cluster structures formed by petroleum development should be utilized, connecting scattered farmlands through green corridors and establishing benefit-sharing mechanisms for petroleum enterprise participation in farmland consolidation.

Zhaozhou County has both the aggregation degree and connectivity at optimal levels, with the core cultivated land proportions exceeding 70%, but faces threats from surrounding development pressure. Core tasks involve maintaining existing cultivated land patterns, establishing strict spatial control systems, and implementing key protection for contiguous cultivated land. Systematic maintenance of farmland connectivity networks should be strengthened, farmland protective forest systems improved, irrigation–drainage systems regularly dredged, and ecological corridor functions kept stable. Pattern monitoring and early warning mechanisms should be established to provide management experience for pattern improvement in other regions.

4.5. Policy Recommendations

Implementation of differentiated strategies requires systematic guarantee mechanisms. Since cultivated land pattern optimization involves multiple departments, municipal governments as the coordination level should establish joint conference systems with participation from natural resources, agriculture, and rural affairs departments to coordinate contradictions among urban expansion, industrial development, and improvement in the cultivated land aggregation degree and connectivity. In resource-based cities like Daqing, petroleum development enterprises should participate in farmland restoration through ecological compensation mechanisms, reducing the negative impacts of development activities on cultivated land protection.

At the technical level, the aggregation degree and connectivity evaluation can be incorporated into design standards for land consolidation and high-standard farmland construction. Before project implementation, the impacts on regional connectivity should be assessed to avoid further farmland fragmentation caused by engineering measures. Promote advanced extraction technologies such as cluster wells and underground pipelines to reduce farmland fragmentation by petroleum development facilities. In land consolidation, emphasize field consolidation and irrigation–drainage system optimization, constructing farmland protective forest belts along river systems to form ecological corridor networks. Establish dynamic monitoring platforms based on remote sensing, integrating multi-departmental data to provide decision-makers with change trends and visualization assessment tools for the aggregation degree and connectivity in various counties and districts. Funding arrangements should reflect classification characteristics. Beyond central and provincial special funds, county finances should provide matching investments and guide petroleum enterprise participation through ecological compensation. For improvement-type areas such as “regular improvement–connected optimization” and “scattered improvement–connected optimization,” funds focus on maintaining existing patterns and technology promotion. For dysfunction-type areas such as “scattered imbalance–isolated dysfunction” and “regular imbalance–connected dysfunction,” concentrate investment on fragmented farmland integration and connectivity restoration. Simultaneously establish incentive mechanisms, providing financial rewards to grassroots counties and districts maintaining stable or improving patterns while holding accountable those with continued deterioration. In supervision and assessment, incorporate the aggregation degree and connectivity indicators into county government cultivated land protection responsibility assessments. Improvement-type counties emphasize the assessment of maintenance effects; dysfunction-type counties emphasize the assessment of the improvement magnitude. Establish key protection lists for high-connectivity core areas such as eastern Lindian County, Zhaozhou County, and southern Zhaoyuan County. Through annual monitoring data, evaluate policy effectiveness and timely adjust fund allocation and technical measures based on pattern changes in various regional types, forming “monitoring–evaluation–adjustment” dynamic mechanisms.

4.6. Innovations, Limitations, and Future Research Directions

This study constructs a multi-scale nested evaluation framework, solving to some extent the problem of predominantly single-scale approaches in previous research, and achieves the integrated assessment of the cultivated land aggregation degree and connectivity. Compared with the existing literature, this study applies the omnidirectional connectivity algorithm to large-scale cultivated land pattern research in plain agricultural areas, overcoming the dependency limitations of traditional methods on source–sink points and providing feasible technical pathways for agricultural regions lacking detailed species data. Through bivariate spatial autocorrelation analysis, this study provides new research perspectives for the aggregation degree and connectivity, offering a reference basis for differentiated cultivated land protection. This evaluation framework has certain replicability and promotional value for cultivated land protection in other plain agricultural areas and resource-based cities.

Although this study constructed cultivated land aggregation degree calculation methods based on landscape pattern indices and MSPA, as well as connectivity evaluation models based on circuit theory, some limitations remain. Due to technical method limitations, this study has not yet established quantitative coupling relationship frameworks between the cultivated land aggregation degree and connectivity, making the evaluation results from both dimensions difficult to integrate into single-indicator systems, limiting the comprehensive evaluation capabilities for county-level cultivated land patterns. In resistance surface construction, this study adopted relatively simplified assignment schemes for certain landscape elements. For example, considering that high-speed railways account for a small proportion of the transportation network in the study area and that the bridge–tunnel ratios of high-speed railways in Northeast China are typically high, unified resistance values were adopted for both railway types without differentiating between high-speed railways and conventional railways. Additionally, selecting three temporal nodes for time series analysis may be insufficient for studying long-term changes in cultivated land patterns.

Future research can expand the geographical scope, verify the applicability and reliability of the cultivated land pattern evaluation models established in this study under different regional backgrounds, deeply explore the driving mechanisms of cultivated land pattern changes at multi-scales, and analyze the impact differences in natural and anthropogenic factors on cultivated land spatial patterns at different levels (national, provincial, municipal–county). Simultaneously, quantitative coupling mechanisms between the aggregation degree and connectivity should be deeply explored, mathematical relationship models between them constructed, and unified comprehensive evaluation indicator systems for cultivated land patterns formed. In resistance surface construction, more refined resistance assignment for different landscape elements such as various transportation facilities can be conducted by combining field survey data to improve connectivity evaluation accuracy. Additionally, practical pathways for applying the cultivated land aggregation degree and connectivity indicators to land consolidation planning, urban–rural land use conflict coordination, and regional agricultural collaborative development should be explored, providing a scientific basis for the sustainable utilization of cultivated land resources. Despite these limitations, this study’s results provide important insights for cultivated land spatial pattern evaluation, verify the application potential of multi-scale nested frameworks and multi-method integration in landscape ecology research, and provide new analytical perspectives for cultivated land protection and sustainable utilization research in plain agricultural areas.

5. Conclusions

Through the construction of a three-level nested framework of “patch–local–regional” scales, this study conducted an in-depth analysis of the aggregation degree and connectivity characteristics of cultivated land patterns in Daqing City, revealing interrelationships and change conditions between the cultivated land aggregation degree and connectivity in plain agricultural areas. The research results indicate the following: (1) the cultivated land aggregation degree in the study area exhibits spatial patterns of “high in north and south, low in middle,” with the core cultivated land proportions declining from 71.22% to 68.51% from 2000 to 2020, showing staged fluctuations of “dramatic deterioration–gradual improvement.” High-connectivity areas are mainly distributed in eastern Lindian County and southern Zhaozhou and Zhaoyuan Counties, while low-connectivity areas concentrate in the urban–rural transition zones around central urban areas and wetland peripheries. (2) The aggregation degree and connectivity demonstrate a positive spatial correlation, with this coupling relationship continuously strengthening over time, indicating the importance of protecting contiguous cultivated land for maintaining agricultural ecosystem integrity. (3) Based on the static levels of and dynamic changes in the aggregation degree and connectivity, the study area can be divided into four types, with the scattered imbalance–alienation disorder class accounting for the largest proportion. Differentiated strategies should be formulated for different types. The theoretical framework and evaluation methods of this study provide new perspectives for cultivated land protection under the combined effects of human activities and natural conditions. The established multi-scale nested evaluation framework demonstrates strong transferability. Its independence from specific species data and flexibly adjustable resistance surface construction method make it applicable to other grain-producing areas, providing effective references for cultivated land pattern optimization. Future research can deeply explore multi-scale driving mechanisms, apply evaluation results to regional agricultural collaborative development practices, and promote the sustainable utilization of cultivated land resources.