Historical Trajectories of the Evolved Cropland Features and Their Reshaped Influences on Agricultural Landscapes and Ecosystem Services in China’s Sanjiang Commodity Grain Base

Abstract

Drastic cropland expansion and its internal structural changes have had an obvious impact on agricultural landscapes and ecosystem services. However, a prolonged investigation of this effect is still lacking in China’s grain-producing bases, such as Sanjiang Plain. To address this issue, half a century of study on the ‘land trajectory migration–landscape evolution–ecological effect,’ covering the period 1970–2020, was elucidated using the synergistic methodology of spatial analysis technology, the reclamation rate algorithm, the landscape indicator, and the newly established ecosystem service improvement model. Satellite observation results indicate that the cropland area exhibited a substantial expansion trend from 23,672.69 km² to 42,856.17 km² from 1970 to 2020, representing a net change of +19,183.48 km² and a huge growth rate of 81.04%, which led to an obvious improvement in the level of agricultural cultivation. Concurrently, the internal structure of the cropland underwent dramatic restructuring, with rice fields increasing from 6.46% to 53.54%, while upland fields decreased from 93.54% to 46.46%. In different regions, spatially heterogeneous improvements of 2.64–52.47% in agricultural cultivation levels across all cities were observed. From 1970 to 2020, the tracked cropland center of gravity trajectories exhibited a distinct biphasic pattern, initially shifting westward and then followed by a southward transition, accumulating a displacement of 19.39 km². As for the evolved agricultural landscapes, their integrity has improved (SHDI = −0.08%), accompanied by increased connectivity (CON = +8.82%) and patch edge integrity (LSI = −15.71%) but also by reduced fragmentation (PD = −48.14%). Another important discovery was that the evaluated ecosystem services continuously decreased from 2337.84 × 10⁸ CNY in 1970 to 1654.01 × 10⁸ CNY in 2020, a net loss of −683.84 × 10⁸ CNY and a huge loss rate of 33.65%, accompanied by a center–periphery gradient pattern whereby degradation propagated from the low-value central croplands to the high-value surrounding natural covers. These discoveries will play a significant role in guiding farmland structure reformation, landscape optimization, and ecosystem service improvement.

1. Introduction

Tracking agricultural land evolution over a long time period and investigating its impact on landscape patterns and processes [1,2] and the surrounding ecological environment are valuable academic projects [3], with critical implications for global food security, landscape aesthetics, and ecosystem service resilience in coupled human–natural systems [4,5]. In particular, in the first 20 years of the 21st century, the global population has displayed a sustained and rapid increase, growing from 6.11 to 7.94 billion—a growth rate of 29.95% [6]. This means that cropland has been essential for the increased production of key grains—including soybean, corn, wheat, and rice, as well as vital agricultural commodities such as cucumbers, tomatoes, potatoes, and beans—to meet the escalating demand for global staple food security. In light of multiple challenges, such as the rising frequency of extreme weather events (e.g., rainstorms and droughts), soil salinization, land degradation, and intensified human interference [7], sustaining the global cropland area has become increasingly difficult, particularly in rapidly developing economic regions such as China. Meanwhile, the evolution of cropland and its internal crops not only alters the agricultural landscape itself but also transforms the surrounding natural landscapes into artificial agricultural systems during cultivated land expansion [8], which brings about drastic changes in the landscape configuration characteristics. Further, the cultivated land changes reshape vegetation communities in neighboring land categories such as the grasslands and wetlands, consequently modifying ecosystem services [9,10]. Given these complex processes, involving multiple variables, the systematic investigation of land–landscape–ecosystem service dynamics has emerged as a prominent research focus, drawing substantial scholarly interest worldwide.

China’s total population has exhibited a rapid growth trajectory in recent decades, leading to a sustained increase in food demand [11]. Although the total population of China has declined slightly since 2022, the demand for food rations and agricultural products is still significant due to the country’s large population base [12,13]. Ensuring national food security necessitates extensive cultivated land and agricultural products. Expanding the cultivated land area has become one of the essential approaches to enhancing agricultural production. Extensive spatial agricultural land conversion has occurred along China’s southeast coast and in its northeastern and northwestern regions in recent decades (i.e., from 1980 to 2020) [14,15]. This inevitably triggered obvious changes in agricultural landscapes, including alterations in their structural advantages, connectivity levels, fragmentation patterns, and overall complexity. Landscape ecological indicators have typically been considered an efficient method for characterizing cultivated land and other cover types [16,17,18], and they have been adopted in this study. Simultaneously, intense agricultural land expansion and other examples of large-scale land conversion have altered ecosystem structures, functions, and spatial patterns. Given the critical role of ecological environments in sustaining human well-being, investigating the spatiotemporal patterns and subtypes of ecosystem services influenced by long-term land use changes has remained a persistent research priority [19,20]. In ecosystem service assessment methodologies, Costanza pioneered the foundational logical framework for quantifying ecosystem service value [21]. Subsequent research has expanded on this work, with scholars improving spatiotemporal modeling approaches. Chinese researchers, in particular, have adapted these methods by integrating experimental and simulation techniques to address China’s unique spatiotemporal distribution characteristics [22]. Among these methods, the equal factor method has been widely adopted in China due to its computational simplicity and consistently reliable simulation results [23,24]. Despite its widespread use, the model has been found to systematically undervalue settlement ecosystem services. With the rapid advancement of remote sensing technology and big data analytics, the classification of land use types can be carried out within the settlement ecosystem, such as in China’s grain-producing regions, to precisely evaluate the influence of agricultural practices on regional ecosystem service.

China has a globally recognized commodity grain production base known as the Northeast Plain, which contains the three provinces, Jilin, Heilongjiang, and Liaoning, and four cities, Chifeng, Hulunbuir, Tongliao, and Xing’an League, in the northeastern part Inner Mongolia, with a total area of 1.52 million km². This region is renowned as the national granary due to its fertile soil, optimal sunlight hours, ample rainfall, and an extensive river network [25], which are ideal conditions for agricultural reclamation. In the Northeast Plain, there is a core region that used to produce national grain commodities called Sanjiang Plain, which is a low-lying and fertile area formed by the sink, flowing mainly from three rivers, namely, Heilongjiang, Wusuli, and Songhua. Due to factors such as agricultural reclamation policies, infrastructure development, and favorable farming conditions, Sanjiang Plain has undergone substantial land use alterations in recent decades, primarily driven by agricultural land expansion [25]. Monitoring land changes and their environmental effects, such as the impact of land use and land cover changes on net primary productivity [26], land surface albedo variations [27], vegetation carbon storage [28], habitat quality [29], and groundwater drought vulnerability [30], has become a research hotspot in this region. Across the whole Sanjiang Plain, the most severe land use change zone is mainly found in its northern region. This is primarily because this region hosts the largest state-owned farm in Northeast China, named ‘build Sanjiang,’ which benefits from the flat terrain and convenient conditions such as local agricultural water conservancy policies, national land consolidation projects, and standard farmland construction projects [31,32]. Agricultural development in this region has surpassed the average level of Sanjiang Plain, and it also serves as a benchmark for China’s Northeast Plain. Therefore, conducting agricultural research in the northern part of Sanjiang Plain over a long time period has important scientific value for both agricultural development and environmental assessment.

To explore the agricultural land evolution process and the relevant landscape characteristics, as well as the corresponding environmental effects in China’s grain-producing region, we analyzed the long-term spatiotemporal cropland changes and their role in reshaping the structure of the landscape and ecosystem services, employing a synergistic methodology involving spatial analysis technology, the reclamation rate algorithm, the ecological indicator, and the newly established ecosystem service improvement model. Our research goals are as follows: (1) to assess the current situation and its long history of regional agricultural development and cultivated land reclamation using time-series of land use products and remote sensing satellite imagery; (2) to track the pattern, quantity, and corresponding process differences in cultivated land structures (i.e., rice fields and upland fields) in the periods 1970–1980, 1980–1990, 1990–2000, 2000–2010, and 2010–2020 across the entire region and its subdivisions, as well as the corresponding distance–direction migration trajectories over a series of 10-year intervals from 1970 to 2020; (3) to understand the different landscape-scale effects brought about by changes in the cultivated planting structure, including at the landscape comprehensive scale and different spatial type configuration scales; and (4) to reveal the trends, characteristics, and spatial hierarchy of ecosystem services under land use evolution, particularly those driven by agricultural development and internal crop changes. In the process of ecosystem modeling, we considered the ecological services in settlements to improve measurement precision. This study is expected to advance the theoretical foundations of land system science, human–land interactions, landscape aesthetics, and agricultural ecological services while simultaneously providing actionable insights for the structural reform of cropland, the optimization of land-based livelihoods and production systems, the strategic design of agricultural landscapes, and the enhancement of ecological functions, thereby fostering the synergistic development of land system science and environmental science.

2. Materials and Methods

2.1. Study Area

Sanjiang Plain, a globally recognized commodity grain production base in China, is situated in Northeast China, spanning latitudes 45°01′–48°27′ N and longitudes 130°13′–135°05′ E (Figure 1a,b). It is a plain formed by the convergence of three rivers; thus, it is named Sanjiang (three) Plain. Decades ago, Sanjiang Plain was a wilderness region known as the ‘Bei da huang’. Due to suitable crop planting conditions and decades of agricultural development, it has now become China’s granary (i.e., Bei da cang). As the agricultural land expands dramatically in the region, various other land types are being lost, which clearly impacts the local ecosystem. The northern Sanjiang Plain, spanning 7.41 × 10⁴ km² (i.e., 68.04% of the total plain area), encompasses 15 administrative cities/counties. The northern region exhibits the most dramatic agricultural land use changes. Characterized by flat terrain (~60 m elevation, Figure 1c), it has a temperate monsoon climate with 500–650 mm annual rainfall and a 1–4 °C annual mean temperature (summer > 22 °C). The heat–rain coincidence and dense river network (Figure 1b) support intensive agricultural reclamation in this region.

2.2. Research Data and Methods

2.2.1. Technical Flowchart of This Study

This study addressed the issue of the cropland changes and their influences in reshaping landscape structure and ecosystem services in China’s commodity grain-producing base by analyzing spatiotemporal land–landscape–ecosystem dynamics. Using historical land use data (i.e., 1970–2015) from the Key Laboratory of Land Surface Patterns and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, and 2020 satellite imagery that adjusted for regional crops’ phenology, we obtained the land use data in 2020 through human–computer interpretation and stratified sampling, thus establishing a 50-year land dataset. Then, the agricultural cultivation intensity was classified into high, medium, and low levels, with spatiotemporal patterns analyzed using spatial statistics and gravity-based migration models. After that, landscape indices quantified the heterogeneity in terms of connectivity, fragmentation, and complexity, while a modified ecosystem service model corrected the construction land value underestimation issue to accurately calculate the ecosystem services of the entire region. This study compared ecosystem service evolution across the region and its subsystems. Key technical procedures are displayed in Figure 2.

2.2.2. Data Source and Preprocessing

The data primarily encompassed three categories: land use data and satellite imagery, socioeconomic development data, and additional auxiliary data. Among which, the land use data, spanning 45 years from 1970 to 2015 with time nodes at 1970, 1980, 1990, 2000, 2010, and 2015, was obtained from the Chinese Academy of Sciences in vector format, which is generally recognized to have higher accuracy in area statistics compared to raster data. The 1970 dataset represented the earliest available land map from this source, which was selected to maximize the temporal coverage for land evolution analysis in this paper. Based on prior scholarly evaluations, all datasets demonstrated robust accuracy, with comprehensive assessment scores consistently exceeding 91% in each time node.

According to land use data in 2015 and images in 2020, we digitized the land use map to 2020 to extend the research period from 45 years to half a century (i.e., 50 years). By referencing crop phenology in the high-altitude and cold regions of Northeast China [33], satellite images covering the research area from July to September in 2020 were downloaded as July to September was the summer season in Northeast China and also the period during which the crops were easily distinguishable. All downloaded images were filtered to eliminate bad pixels, cloud and rain obstruction, strong wind influence, and low quality. The high-resolution images were initially generated through false-color processing techniques on the ENVI 5.6 platform and subsequently imported into ArcGIS 10.7 as digitized background images. The 2015 land use map was overlaid onto the 2020 remote sensing image, with the original attribute table containing a ‘land 2015’ column representing the 2015 classification system. A new ‘land 2020’ column was added to record the 2020 land status through human–computer visual interpretation, where professionals identified land use types based on remote sensing imagery and assigned corresponding codes. By comparing the differences between the 2015 and 2020 land codes, dynamic change patches were extracted and subsequently verified by multiple personnel for accuracy. The final stratified random sampling validation confirmed that 2020 land use classification achieved over 93% comprehensive accuracy.

The study also utilized socioeconomic development data and other types of data, including the research area’s socioeconomic development statistical yearbooks, state-owned farms’ statistical yearbooks, municipal statistical data, archived materials from universities and research institutes, and field survey data.

2.2.3. Classification of Reclamation Rate Levels

In this study, the cropland reclamation rate was defined as the numerical ratio of the regional cropland area to its corresponding administrative division area, which was commonly employed to quantify agricultural reclamation intensity within a given region. To quantitatively analyze the agricultural reclamation dynamics in Sanjiang Plain, we classified the reclamation rate of cropland into three parts using the certainty-equivalent method to break the value from 0 to 100% (i.e., the low, medium, and high levels), due to there being no official criteria for different reclamation levels in China. Then, the low, medium, and high levels of cultivation reclamation were assigned to enable the systematic characterization of regional agricultural development disparities during 1970–2020. The corresponding classification thresholds for low, medium, and high reclamation levels are presented in

Table 1. Low, medium, and high reclamation levels in China’s national commodity grain base.

Reclamation Rate of Cropland	Classification of Levels
(0–33.33%]	Low reclamation level
[33.33–66.66)	Medium reclamation level
[66.66–100]	High reclamation level

.

2.2.4. The Selection of Landscape Indices and Their Ecological Significance

The landscape index, as a universal and effective method, was regularly applied to characterize the spatiotemporal heterogeneity and evolution process of various landscapes [34]. In this study, we used the landscape index to analyze the current situation pattern and dynamic evolution in agricultural landscapes, including cropland, rice field, and upland field over the past 50 years. According to the landscape guidebook, each landscape index had its own ecological meaning. The use of many landscape indicators could easily lead to redundancy or confusion, but a single indicator was insufficient to express the required scientific issue at multiple scales [35]. Here, by investigating the changing characteristics of cropland and its internal structure, the analysis perspectives of landscape comprehensiveness, advantages, connectivity, fragmentation, and complexity were established. To comprehensively and clearly express these landscape features, we applied the landscape indicators that are frequently used in the literature. Specifically, SHDI is often used to describe the landscape features in an entire area, while LPI and PD are always applied to represent the degree of dominance and fragmentation. Similarly, CON is used for connectivity and LSI for complexity [36]. The nomenclature, abbreviations, mathematical formulations, and explanations for these ecological metrics are displayed in

Table 2. Landscape table employed in this study.

Names	Abbreviations	Formulas	Range of Value	Explanations
Shannon’s Diversity Index	SHDI	$SHDI=-{\displaystyle \sum _{i=1}^n}[P_{i}In(P_i)]$	SHDI ≥ 0	This serves as an indicator for assessing species diversity within ecosystems
Patch Density	PD	$PD={\displaystyle \frac{n_i}{A}}(\mathrm{10,000})(100)$	0 < PD ≤ 1	Expresses the number of patches per unit area and facilitates comparisons among landscapes of various sizes
Connectivity Index	CON	$Connect=[{\displaystyle \frac{{\displaystyle \sum _{j\ne K}^n}{c}_{ijk}}{\frac{n_i(n_i-1)}{2}}}](100)$	CON ≥ 0	Connectivity is reported as a percentage of the maximum possible connectivity, given the number of patches
Largest Patch Index	LPI	$LPI={\displaystyle \frac{max(a_1,\dots ,a_n)}{A}}\ast 100\%$	0 < LPI ≤ 100	Quantifies the percentage of the total landscape area represented by the largest patch: a simple measure of dominance
Landscape Shape Index	LSI	$LSI={\displaystyle \frac{0.25{\displaystyle \sum _{k=1}^m}{e}_{ik}^{*}}{\sqrt{A}}}or={\displaystyle \frac{0.25E^{*}}{\sqrt{A}}}$	LSI ≥ 1	Serves as a quantitative measure for assessing the complexity of landscape forms, where higher values correspond to greater shape irregularity

.

2.2.5. Migration Center and Direction Model

Traditional methods can characterize the temporal evolution and spatial succession of cropland changes, but these approaches are insufficient to comprehensively describe the migration processes. As a renowned grain base in China, Sanjiang Plain is located in Northeastern China’s cold temperate climate zone. Due to the influence of total sunshine and the accumulated temperature, the crop maturity in this cold, high-latitude region was once a year. The migration trajectory of crops, especially rice fields, was more susceptible to terrain and temperature constraints. Rice fields were more likely to migrate to areas with flat terrain and abundant water sources. And for upland fields, although the terrain imposes fewer constraints on its growth, the accumulated temperature imposed a greater limitation. If crops migrated northward, the accumulated temperature they received could decrease, leading to a decrease in grain yield and fruit plumpness. However, technological advancements have introduced cold-resistant and low-temperature-tolerant crop varieties, enabling cultivation in previously unsuitable regions. This means that crops can grow in colder regions than before. This progress can be captured through migration distance and direction. Therefore, conducting research on the migration distance and direction of the crops in the region can not only track the spatiotemporal changes in crops; it can also reflect the technological progress in agriculture at that time. The core equations include Equation (1) for geographic coordinates and Equations (2) and (3) for trajectory direction and distance, respectively, where R is a constant.

$$\overline{x}={\displaystyle \frac{{\sum }_i^n{m}_i{x}_i}{{\sum }_i^n{m}_i}},\overline{\mathrm{y}}={\displaystyle \frac{{\sum }_i^n{m}_i{y}_i}{{\sum }_i^n{m}_i}}$$

(1)

$${\theta }_{i-j}={\displaystyle \frac{n\pi }{2}}+arctg({\displaystyle \frac{y_i-y_j}{x_i-x_j}})$$

(2)

$${\mathrm{D}}_{i-j}=R\times \sqrt{{(y_i-y_j)}^2+{(x_i-x_j)}^2}$$

(3)

In Equation (1), n represents the number of centroids, which is an integer; i is a count index; and $\bar{\mathrm{x}}$ and $\bar{\mathrm{y}}$ are the gravity migration coordinates over a certain period of time. In Equation (2), ${\theta }_{i-j}$ represents the directional angle from the j_th location to the i_th location; n is represented as an integer; and i and j represent the index of different locations. In Equation (3), ${\mathrm{D}}_{i-j}$ is the distance between point i and point j; R is the coefficient; (x_i, y_i) denotes the coordinates of point i; and (x_j, y_j) denotes the coordinates of point j.

2.2.6. Improvement of Ecosystem Service Algorithm

Numerous ecosystem service evaluation models have been developed [37]. Among these, China’s widely adopted equivalent factor evaluation model, pioneered by Xie [23,38], stands out for its comprehensive consideration of land use patterns and vegetation cover types and its incorporation of nationwide observational experiments to derive region-specific parameters. It has been validated through extensive applications across Chinese regions, with robust simulation results in prior studies. This model was selected for our research to assess ecosystem services and analyze spatiotemporal patterns. The simulation framework comprised three core components, namely, ecosystem service system tables, equivalent factor values, and integrated computational procedures.

The first core component of the model was established through the construction of an ecosystem coordination table (i.e.,

Table 3. Matching different ecosystems from this study with the equivalent factor evaluation model.

Ecosystem Types in the Equivalent Factor Evaluation Model	First Level Type of Ecosystem	Ecosystem Types of Chinese Academy of Sciences
Rice field	Cropland	Rice field
Upland field		Upland field
Average value of coniferous forest, mixed coniferous, and broad-leaved forest	Forestland	Woodland
Shrub wood		Shrub wood
Average value of forest and bare land		Sparse woods
The average value of forest		Other forestland
Average value of grassland	Grassland	High- and medium-coverage grassland
Average value of grassland and bare land		Low-coverage grassland
River system	Water bodies	Reservoirs, ponds, tidal flats, beaches, rivers, and lakes
Glacier and snow		Permanent glacier and snow
Wetland	Wetland	Wetland
Desert	Construction land	Impermeable surface area (62.84%), forestlands (16.93%), grasslands (16.92%), and bare soils (3.31%)
	Other lands	Bare land, alkali land, sandy land, gobi, and saline bare rock

), which facilitated the transfer of ecological factors via equivalent factor matrices by enforcing a one-to-one correspondence between disparate ecosystem classification systems and their respective subsystems. The first and second columns of Table 3 are derived from the land ecosystem classification system proposed by Xie [23,38], while the second and third columns are grounded in the classification system developed by the Chinese Academy of Sciences [39,40]. A comparison revealed the substantial congruence between the first-level categories of these two systems. Leveraging the shared first-level categories (represented in the second column) as an intermediary mapping layer, we systematically aligned the second-level categories from the Xie classification (i.e., the first column) with those from the Chinese Academy of Sciences classification (i.e., third column), thereby enabling consistent transmission of these factors. This hierarchical correspondence ensured methodological coherence in the integration of divergent land ecosystem typologies within the model. Meanwhile, in the original table, urban, rural, and other construction land types were classified as desert ecosystems. This means that their ecosystem services were very low. However, real-world construction areas contained extensive green park spaces, green block spaces, green road spaces, and other examples of urban greenery due to initiatives like ecological city development and rural revitalization, suggesting that these lands likely provided higher ecosystem value than desert systems. This also indicates that the ecosystem assessment method has an underestimation issue with respected to construction land. To address this, our study developed new construction land parameters for our assessment of Sanjiang Plain, thereby enhancing the ecosystem service algorithm for China’s key grain production regions.

Then, to determine the parameters of different land types within the broader category of construction land, we acquired 0.5 m spatial resolution remote sensing satellite imagery covering the entire construction area. Through human–computer interaction digital interpretation, we classified the land cover types within the construction zone. The internal land use classification system consolidated various cover types into impermeable surface area, forestland, grassland, and bare soil, with water body excluded due to its negligible area. Impermeable surface area and bare soil, both devoid of vegetation, were merged into bare soil. By applying mathematical statistics and spatial analysis techniques, we derived the land use types and their proportional areas within the construction land. Finally, the parameters within the construction land were impermeable surface area (62.84%), forestlands (16.93%), grasslands (16.92%), and bare soils (3.31%).

The second core process of this model involved determining equivalent factor values. The mathematical conversion results are presented in

Table 4. Equivalent factors of ecosystem types in Sanjiang Plain.

Land Types		Paddy Fields	Rain-Fed Farmland	Wood Land	Shrub Wood	Sparse Woods	Other Forest Land	High–Medium-Density Grassland	Low-Density Grassland	Rivers, Lakes, Reservoirs, Ponds, Tidal Flats, and Beaches	Permanent Glacier and Snow	Wet Land	Urban Land, Rural Land, Industrial, and Mining Land	Bare Rock land Sandy Land, Bare Land, Gobi, Saline Alkali Land, and Others
Supply	FP	1.36	0.85	0.27	0.19	0.25	0.25	0.23	0.18	0.80	0.00	0.51	0.29	0.01
	MP	0.09	0.40	0.63	0.43	0.58	0.58	0.34	0.26	0.23	0.00	0.50	0.58	0.03
	WRS	−2.63	0.02	0.33	0.22	0.30	0.30	0.19	0.14	8.29	2.16	2.59	0.31	0.02
Regulation	GR	1.11	0.67	2.07	1.41	1.91	1.91	1.21	0.91	0.77	0.18	1.90	1.95	0.13
	CR	0.57	0.36	6.20	4.23	5.71	5.71	3.19	2.39	2.29	0.54	3.60	5.47	0.10
	PE	0.17	0.10	1.80	1.28	1.70	1.67	1.05	0.82	5.55	0.16	3.60	1.85	0.41
	HR	2.72	0.27	3.86	3.35	3.74	3.74	2.34	1.76	102.24	7.13	24.23	3.80	0.24
Support	SC	0.01	1.03	2.52	1.72	2.33	2.32	1.47	1.11	0.93	0.00	2.31	2.37	0.15
	MNC	0.19	0.12	0.19	0.13	0.18	0.18	0.11	0.09	0.07	0.00	0.18	0.18	0.01
	BD	0.21	0.13	2.30	1.57	2.12	2.12	1.34	1.01	2.55	0.01	7.87	2.16	0.14
Culture	AL	0.09	0.06	1.01	0.69	0.93	0.93	0.59	0.45	1.89	0.09	4.73	0.95	0.06

Note: The land types in this table were taken from the land classification system of the Chinese Academy of Sciences, which was established under the Chinese land use code [39].

based on Table 3. In this table, each type of ecosystem service has a corresponding numerical parameter. As for the source of each parameter, they were taken from the equivalent factor evaluation model proposed by Xie, in which all the parameters were obtained through site observations, field measurements, etc. In Table 4, the transmission of parameters for different ecosystem types is addressed. For example, the parameter value of the upland fields in the equivalent factor evaluation model was directly assigned to the upland fields in the Chinese Academy of Sciences model. Similarly, all average data values for grassland and bare land were transmitted to low-coverage grassland. In this process, to provide a more detailed description of the subtype changes in different ecosystem services, the ecological services among the functions of culture, support, regulation, and supply were calculated. Around these four functions, more detailed element factors were transmitted according to the following values: aesthetic landscape (AL), purification environment (PE), biodiversity (BD), raw material production (MP), climate regulation (CR), soil conservation (SC), food production (FP), maintaining nutrient cycle (MNC), water resources supply (WRS), gas regulation (GR), etc.

The central issue entailed the quantification of the economic worth of ecosystem services. To guarantee the precision of the computational data sources, all information was sourced from statistical yearbooks and archival records, with subsequent processing employing Equations (4) and (5). This methodology produced the ecosystem services alongside their corresponding subcategories.

$${\mathrm{E}}_{\mathrm{a}}={\displaystyle \frac{1}{7}}{\sum }_{i=1}^n{\displaystyle \frac{m_i{p}_i{q}_i}{M}}$$

(4)

$$\mathrm{E}\mathrm{S}\mathrm{V}=\sum {\mathrm{A}}_{\mathrm{i}}\mathrm{\times }\mathrm{V}{\mathrm{C}}_{\mathrm{i}}$$

(5)

For Equations (4) and (5), ${\mathrm{E}}_{\mathrm{a}}$ is the economic value (CNY/ha); i is the crop type; M is the total planting area (ha); $m_i$ is planting area of i crop; $p_i$ is the price of i crop (CNY/ton); $q_i$ is the yield (ton/ha); ESV is the total ecosystem service; $A_i$ is the i land area; and $V{C}_i$ is the coefficient of land i.

3. Results

3.1. Analysis of the Evolutionary Cropland Structure Features from 1970 to 2020

3.1.1. Overall Characteristics of the Succession Law of Cropland

In 1970, the cropland area totaled 23,672.69 km², comprising 31.91% of the study region. According to the land use statistics from that year, cropland dominated the area (31.91%), surpassing forestland (31.32%). The spatial distribution map showed that cropland was exclusively concentrated in the central–western zone, displaying no scattered pattern (Figure 3a). From 1970 to 2020, cropland coverage increased from 30,499.95 km² to 42,856.18 km², with recorded areas of 31,376.87 km² in 1980, 37,145.15 km² in 1990, 40,635.52 km² in 2000, and 42,856.18 km² in 2010 (Figure 3g). Over the 50-year period, cropland expanded by 19,183.48 km², showing an 81.04% growth rate. The cropland proportion within the study area rose from 31.91% in 1970 to 57.77% in 2020, exceeding half the region (Figure 3g). Its expansion quantity varied by period, namely, +6827.26 km² in 1970–1980, +876.91 km² in 1980–1990, +5768.28 km² in 1990–2000, +3490.37 km² in 2000–2010, and +2220.66 km² in 2010–2020, reflecting sustained growth over five decades.

With the significant cropland expansion, its spatial distribution also underwent changes. Referring to the 1970 cropland scope, the primary expansion directions were east, south, and north (Figure 3b–f). Other land types in these areas were consistently encroached upon, leading to alterations in their land functions. In contrast, the western region was designated as a series of forest protection zones, where land use conversion was strictly prohibited.

3.1.2. Differences in the Reclamation Level with 10-Year Intervals in Different Administrative Regions from 1970 to 2020

In 1970, there were seven cities with a low reclamation level, including FuCNY, Hegang, Hulin, Raohe, Baoqing, Tongjiang, and Luobei. The reclamation level of FuCNY City was the lowest (only 9.85%), despite having abundant cropland resources in its jurisdiction. The highest cultivation level happened in Luobei (28.76%), mainly concentrated in its eastern part. There were only three medium-reclamation-level cities, namely, Shuangyashan, Fujin, and TangCNY, with agricultural reclamation rates of 32.00–47.35%. Five cities exhibited high reclamation levels, located in the central region and among the earliest regions for farmland cultivation, with reclamation rates between 62.34% and 73.58%.

From 1970 to 2020, all cities in the study area showed rising cultivation rates over the 50-year period (

Table 5. Reclamation levels of cropland in various regions from 1970 to 2020.

City Names	Year 1970	Year 1980	Year 1990	Year 2000	Year 2010	Year 2020	1970–2020 Changes	Administrative Area
FuCNY	9.85	15.60	16.42	28.78	47.07	62.32	+52.47	623,274.04
Hegang	17.47	18.51	20.27	21.38	21.72	20.25	+2.78	456,635.43
Hulin	17.97	29.65	32.24	47.93	51.07	53.06	+35.09	930,830.67
Raohe	20.76	28.18	28.26	36.02	39.36	42.17	+21.41	657,963.40
Baoqing	23.74	44.91	45.02	49.77	52.17	54.27	+30.53	999,345.51
Tongjiang	24.68	42.40	42.42	49.44	58.21	60.66	+35.97	616,047.83
Luobei	28.76	31.44	37.71	42.89	43.74	46.42	+17.66	675,222.35
Shuangyashan	32.00	32.96	32.97	38.66	37.72	35.36	+3.36	152,802.80
Fujin	41.81	56.17	56.20	69.13	79.77	82.90	+41.10	822,803.50
TangCNY	47.35	47.47	48.29	50.59	51.67	52.33	+4.98	343,773.62
Youyi	62.34	74.96	75.04	87.17	88.27	89.76	+27.43	168,318.29
Jiamusi	64.05	63.35	64.09	66.21	66.57	66.69	+2.64	190,454.37
Suibin	68.07	69.71	70.06	72.51	73.06	76.27	+8.20	333,301.69
Jixian	73.56	74.32	74.37	77.13	77.07	78.81	+5.25	224,756.17
Huachuan	73.58	73.11	73.19	77.84	79.78	80.10	+6.53	222,373.82

), reflecting an overall improvement in agricultural development across the region. The highest growth rate happened in FuCNY, exceeding half (52.47%), due to its abundance of suitable arable land resources. There are seven cities with growth rates between 15 and 50%, namely, Fujin (41.10%), Tongjiang (35.97%), Hulin (35.09%), Baoqing (30.53%), Youyi (27.43%), Raohe (21.41%), and Luobe (17.66). These cities had flat terrain and abundant water resources in non-cultivated areas in 1970, making them suitable for large-scale cultivation during 1970–2020. There were also seven cities with a cultivation rate increase of less than 10%, including Suibin (8.20%), Huachuan (6.53%), Jixian (5.25%), TangCNY (4.98%), Shuangyashan (3.36%), Hegang (2.78), and Jiamusi (2.64). The reasons for the low increase in reclamation rate were either that most of the cropland in these cities had already been reclaimed in 1970 or their jurisdictions contained restricted soil areas such as nature and forestland reserves. In 2020, the cities with low, medium, and high reclamation levels were one, nine, and five (vs., five, three, five in 1970), indicating that agricultural cultivation in all cities improved during this period.

3.1.3. Different Cropland Structure Evolution Processes from 1970 to 2020

The cropland planting structure (i.e., rice fields and upland fields) exhibited significant differences during the expansion from 1970 to 2020 (Figure 4a–f). In 1970, the proportion of rice and upland fields in cropland was 6.46% and 93.54%, respectively, with the latter being the primary type of cropland structure. For the rice field, it was initially small (1528.69 km²) and concentrated in the southeast in 1970. Afterwards, the rice fields continued to expand towards the surrounding areas, with their original distribution forming the center, with increments of +3.79 km², +98.14 km², +5944.23 km², +8608.85 km², and +6763.00 km² in 1970–1980, 1980–1990, 1990–2000, 2000–2010, and 2010–2020, respectively. By 2020, the rice field area reached 22,946.71 km² and formed a large number of concentrated distribution patterns (Figure 4a–f). This area was 1501.07% bigger than that of the initial year (1970) (Figure 4g2,g3). As for the upland field area, it covered 22,144.00 km² in 1970 and expanded by +6823.47 km² and +778.77 km² in 1970–1980 and 1980–1990, peaking at 29,746.24 km² in 1990. Then, the area changes were −175.95 km², −5118.48 km², −4542.34 km² in the periods 1990–2000, 2000–2010, and 2010–2020, respectively. By 2020, the cover was 19,909.47 km², which was only 89.91% of that in 1970. Meanwhile, the proportion of rice and upland fields shifted from 6.46%/93.54% in 1970 to 53.54%/46.46% in 2020 (Figure 4g1–g3), indicating that rice fields have surpassed upland fields in the last 50 years. Furthermore, the regional cropland data showed increasing rice field proportions and declining upland field shares across all cities (Figure 4h1,h2).

3.2. Cropland Structure Migration Process with 10-Year Intervals in the Study Area from 1970 to 2020

Analyzing the trajectory and direction of gravity migration revealed the comprehensive spatial features of the land changes. Over a period of 50 years, we mapped the migration distance, the direction of the cropland, and its internal structure (Figure 5). In 1970, the cropland was concentrated in the central part, within the territory of Fujin city. From 1970 to 1980, the center of gravity expanded northeast by 3.04 km², with the direction changing to the flat areas of Fujin and FuCNY due to the rapid agricultural development of cropland in the Jian Sanjiang state-owned farm, located in the northeast of the study area. The Jian Sanjiang state-owned farm was able to quickly carry out land reclamation on the basis of their being insufficient food supply for the entire population of China during this period. Then, during 1980–1990, we saw a 6.48 km² southwest migration. This was mainly caused by the expansion of cropland in the southeastern region near rivers due to the convenience of agricultural irrigation. From 1990 to 2000, the cropland area returned northeast by 4.04 km² because lots of wetlands and unused land were being cultivated for agricultural purposes in the central–northern parts of the study area. Since 2000, the cropland has mainly shifted in a north–south direction. Specifically, in 2000–2010, the cropland migrated 2.81 km² towards the north. This migration was due to seed cold resistance improvements and the advancement of modern agriculture technology, which enabled crops to be planted in the north region at low temperatures. After all the suitable land in the north region was cultivated as cropland, the agricultural reclamation began to shift to the remaining areas in the south region from 2010 to 2020; the center of gravity moved 3.04 km² in a manner almost perpendicular to the south direction. And in 2020, almost all of the suitable land had been reclaimed as cropland. Over 50 years, the center of gravity first shifted east–west and then north–south, with a total distance of 19.39 km². The ultimate distance between gravity points in 1970 and in 2020 culminated in a 1.64 km² southeast displacement.

Regarding the internal structure of the cropland, rice fields initially migrated southward before shifting northward, covering a total distance of 11.88 km² from 1970 to 2020. The main reasons for this can be summarized as follows: Specifically, the rice fields’ center of gravity was initially situated in the central–eastern region of the study area, near the major riverine systems, to maximize reliance on the use of surface water for irrigation. Over the subsequent five decades, a progressive northeastward shift in this centroid was observed, primarily driven by the reclamation of wetlands for paddy agriculture in the central–northern region and the large-scale conversion of upland fields into rice fields in the northeastern region, with an enhanced river network density and flat terrain. The concentrated presence of state-owned agricultural enterprises in these two regions collectively enabled the efficient execution of national land use policies, particularly those targeting the transition from upland fields to irrigated rice cultivation. For upland fields, the center of gravity mainly moved to the north region, which was due to a combination of various factors, including national agricultural policies, state-owned farms, and flat terrain. When the upland fields in the north were cultivated, the center of gravity shifted to the south in the late period. The total gravity shift was 18.01 km² over 50 years.

3.3. Analysis of Spatial Landscape Characteristics During Cropland Structure Changes

Under the land use space control policy, the scope of the state-owned forest farms, wetland conservation areas, and other special lands in the study area remained largely unchanged. Consequently, the expansion direction of cropland and the types of land use encroached upon were predominantly determined by manual planning, akin to the spatial configuration of the land landscape. From 1970 to 2020 (Figure 6), against the background of a rapid expansion in cropland and the evolution of its internal planting structure, the comprehensive landscape integrity in the northern Sanjiang Plain improved (SHDI = −0.08%), indicating reduced patch fragmentation (e.g., patch density decreased by 48.14% over 50 years). Simultaneously, as other land use types were converted to cropland, the expansion of cropland enhanced landscape connectivity (CON = +8.82%). Higher connectivity further suggested an enhancement in plaque index; for example, the LPI increased by 16.73% from 1970 to 2020. The cropland expansion process was primarily a result of human intervention. To boost agricultural cultivation efficiency, more mechanized methods were used to level the land. In this process, various measures transformed the irregular land types into regular agricultural plots and standardized regional land use boundaries (LSI = −15.71%).

At the land-type scale, against a background of rice expansion and the initial increase—followed by a decline—in upland fields, all land types displayed reduced fragmentation, i.e., PD changes of −73.35% (rice field), −62.94% (upland field), −13.41% (forestland), −46.33% (grassland), −58.03% (wet land), etc. (Figure 7). The data revealed that the reduction in the fragmentation of rice and upland field landscapes ranked among the top two. This also means that the integrity of these two landscape types greatly improved. Meanwhile, spatial pattern of the rapid expansion and continuous agglomeration rice fields for 50 consecutive years led to a continuous increase in its largest patch index, reaching as high as 3679.79%, implying a very strong landscape advantage. The extensive aggregation of rice field patterns also enhanced its connectivity (CON = +113.13%). The regular development of spatial scales in rice fields has further increased the ornamental value of rice field landscape aesthetics, and the patch edges were more regular (LSI = −19.25%). Similar landscape spatial configuration patterns also appeared in the upland field type (i.e., LPI = 18.16%; CON = +50.51%; LSI = −7.76%), as well as in construction land (i.e., LPI = 114.77%; CON = −61.58%; LSI = −9.64%). Their common features were those human activities interfered strongly and the area was constantly increasing in a regular manner. On the contrary, the reduction in the area of other land use types led to a decrease in the dominance of patches and a weakening in connectivity (i.e., for forestland, LPI = −4.28% and CON = −8.48%; for grassland, LPI = −90.06% and CON = −20.50%). From a numerical perspective concerning landscape changes, the decline in grasslands’ advantage was more pronounced, primarily due to greater grassland loss. Furthermore, the forestland was mainly lost in non-state-owned forest farms, displaying a relatively small area. Due to the disappearance of small patches, the edges of other patches in forestland and grasslands became more regular (i.e., for forestland, LPI = −4.28%; for grassland, LPI = −90.06% and CON = −20.50%).

3.4. Analysis of Spatiotemporal Features in Ecosystem Services in the Whole Region During 1970–2020

3.4.1. Assessment of the Quantity and Evolutionary Law of Ecosystem Services and Their Different Subfunctions

The total amount of ecosystem services in 1970 was 2337.84 × 10⁸ CNY. Since 1970, the ecosystem services in the entire region have shown a decreasing trend, with evaluated values of 2060.78 × 10⁸ CNY, 2032.26 × 10⁸ CNY, 1813.77 × 10⁸ CNY, 1667.94 × 10⁸ CNY, and 1654.01 × 10⁸ CNY in 1970, 1980, 1990, 2000, 2010, and 2020, respectively. The data revealed stage-wise losses of 277.06 × 10⁸ CNY, 28.52 × 10⁸ CNY, 218.49 × 10⁸ CNY, 145.83 × 10⁸ CNY, and 13.93 × 10⁸ CNY in each decade. This decreasing trend correlated with reduced land development intensity. Therefore, the calculated ecosystem service value decreased from 2337.84 × 10⁸ CNY in 1970 to 1654.01 × 10⁸ CNY in 2020, with a net loss of −683.84 × 10⁸ CNY throughout the entire period of study. This indicated a 33.65% decline in ecosystem services over the past 50 years, representing substantial loss. We further investigated the reasons for the significant ecosystem service loss in this region and found that low-ecosystem-service cropland expansion primarily encroached upon high-ecosystem-service forest and wetland areas throughout the period.

The 50-year changes in ecosystem services according to the functions of supply, adjust, support, and culture are presented statistically in

Table 6. Fifty-year changes in the functions of supply, adjust, support, and culture in ecosystem services.

Service Types	Subfunctions	1970	1980	1990	2000	2010	2020	Changes in 1970–2020	Change Rate
Supply	FP	482,502.07	518,839.89	524,953.18	595,213.41	668,635.37	731,257.49	248,755.42	51.56%
	MP	419,441.49	413,869.33	411,907.99	386,076.87	350,765.52	331,301.74	−88,139.75	−21.01%
	WR	910,543.21	775,133.42	760,460.90	460,282.16	108,969.96	−117,133.89	−1,027,677.10	−112.86%
Regulate	GR	1,275,793.26	1,185,311.91	1,172,866.33	1,137,628.25	1,141,324.54	1,183,022.52	−92,770.75	−7.27%
	CR	2,894,952.01	2,628,966.54	2,579,242.41	2,357,453.36	2,231,209.93	2,192,539.33	−702,412.68	−24.26%
	PE	1,542,989.17	1,319,542.48	1,296,300.59	1,135,486.89	1,034,637.73	1,036,959.58	−506,029.60	−32.80%
	HR	10,463,426.63	9,290,465.98	9,175,418.36	8,285,631.24	7,821,957.01	7,787,183.44	−2,676,243.20	−25.58%
Support	SC	1,585,106.47	1,493,009.14	1,478,296.22	1,337,596.96	1,187,470.20	1,115,266.02	−469,840.45	−29.64%
	MC	135,572.40	131,829.31	131,330.00	134,223.34	140,036.66	147,980.78	12,408.38	9.15%
	BD	2,362,137.81	1,848,628.24	1,810,310.66	1,504,586.73	1,307,503.36	1,391,197.66	−970,940.15	−41.10%
Culture	AL	1,305,981.64	1,002,252.75	981,515.21	803,494.08	686,844.59	740,520.27	−565,461.36	−43.30%

Abbreviation: gas regulation (GR); hydrological regulation (HR); soil conservation (SC); maintain nutrient cycling (MC); biodiversity (BD); climate regulation (CR); purify environment (PE); food production (FP); material production (MP); water resource (WR); aesthetic landscape (AL).

. In this table, it can be seen that, in terms of supply function from 1970 to 2020, material production and water resource decreased by 21.01% and 112.86%, respectively. By contrast, food production increased by 51.56%. This was due to extensive cropland expansion, which has led to an improvement in food supply services. In terms of supply function, the evaluation results indicate that all functions exhibited a downward trend, with changing rates ranging from 7.27% to 32.80%. Similarly, landscape aesthetics in culture function also showed a declining trend, with a value of 43.30%. On the other hand, the various indicators in support service function displayed different changing trends; i.e., nutrient cycling maintenance increased by 9.15%, while soil conservation and biodiversity decreased by 29.64% and 41.10% in the past 50 years, respectively.

3.4.2. Spatial Variation Characteristics of the Different Ecosystem Service Gradings

The spatial features of the ecosystem services in the study area were visualized through a graded distribution map that was created via the equivalence method (Figure 8). According to this method, the ecosystem services were classified into five levels, with values ranging from low to high (i.e., from level I (low) to level V (high)). In the initial year of the research, the lowest level (I) was located in the central part, and level II was concentrated in the eastern part, especially in the northeast part. This northeastern region was spatial concentration distribution region for level III. As for level IV, it gathered along the study area’s periphery, encompassing the western, eastern, and southern edges. The highest concentration (level V) was predominantly observed in the central–eastern and central–northern regions. Starting from 1970, the spatial evolution characteristics of expansion at level I and contraction at levels II, III, IV, and V were observed. This means that the persistent encroachment of level I upon higher-level ecosystem service areas led to the near-total disappearance of levels II and III. By the research endpoint (i.e., the year of 2020), levels II and III were only sporadically distributed. Meanwhile, only a small portion of the highest level (V) had been retained. Level I was predominantly distributed across the study area, while level IV exhibited a relatively stable spatial distribution, particularly in forest reserve zones.

4. Discussion

4.1. Sanjiang Plain Was a Special Commodity Grain Base Where Cropland Continuously Increased over the Past 50 Years in China

Our investigation showed that Sanjiang Plain of Northeast China exhibited persistent cropland area expansion over five decades, with an expanded area of 19,183.48 km² and a total growth rate of 81.04%. During the period of cropland expansion, its internal structure shifted acutely. This can be attributed to Sanjiang Plain’s status as the national commodity grain base [41], where numerous policies and projects were implemented between 1970 and 2020 to enhance cropland area and agricultural development.

Differently from Sanjiang Plain, China’s cropland has been subject to varied spatial migrations and quantity changes nationwide over the last 50 years [42]. Specifically, China’s cropland area expanded from 1970 to 2000 but contracted from 2000 to 2020 [43], displaying a different quantity trend than that of Sanjiang Plain, where continuous cropland growth occurred. Sanjiang Plain’s sustained cropland expansion contrasted with China’s biphasic trend, i.e., from the increasing to the decreasing, highlighting its unique agricultural trajectory. This also means that Sanjiang Plain’s monotonic cropland expansion provided a countervailing force with respect to China’s nationwide decrease, alleviating the food security challenges exacerbated by restricted land resources. In addition to the temporal trends in cropland quantity, the spatial redistribution of cropland exhibited pronounced regional migration patterns, reflecting underlying geographic processes [39]. In China’s southeastern coastal regions, the multi-cropping system, which was defined as an annual yield of two harvests or two years of three harvests, exhibited an obvious decline attributable to urbanization, industrialization, and socioeconomic development. Subsequently, the expansion of cropland shifted toward Northeast China, driven by the region’s favorable agroecological conditions, including water availability, soil fertility, and topographic suitability. Following the nearly full reclamation of suitable cropland in Northeast China [15,44], the expansion of cropland subsequently shifted toward Northwest China. Therefore, China’s cropland has exhibited a distinct spatiotemporal pattern of changes across the Southeast, Northeast, and Northwest regions. However, Sanjiang Plain exhibited a northward shift in cropland over the 50-year period. This phenomenon is attributed to enhance the seed cold tolerance and technological advancements in this cold temperate zone. A persistent increase in cropland extent was observed in Sanjiang Plain during this process, accompanied by drastic internal structural changes, emphasizing its importance to China’s food supply.

4.2. Human Activities Promote Agricultural Land Integrity in the Evolving Process of Natural to Artificial Landscapes

The comprehensive landscape integrity in Sanjiang Plain has improved (SHDI = −0.08%) from 1970 to 2020. This means that the landscape indicator value of patch fragmentation became lower [45], i.e., that the patch density decreased by 48.14% over the 50-year period. In this process, the study area has transformed from a natural landscape to an artificial landscape due to human-induced effects, thereby enhancing the integrity and ornamental value of the landscape. In 1970, Sanjiang Plain was called ‘Beida Huang’ due to its sparse population, lots of non-cropland, and overgrown weeds [29,46]. The disorderly combination of various natural landscapes brought about the chaotic condition of the comprehensive landscape. Subsequently, extensive land use transformations were initiated in Sanjiang Plain, marked by significant agricultural land expansion concomitant with substantial wetland depletion and reductions in other land cover types [28,47]. The regional landscape has also undergone extensive changes. Statistically, almost all landscape conditions, such as connectivity and aggregation, became favorable for cropland. Concurrently, within cropland areas, substantial rice field expansion coupled with progressive upland field contraction resulted in landscape modifications that predominantly favored rice cultivation. Such alterations in agricultural land use patterns typically indicate anthropogenic influences on natural landscape configurations.

As we all know, Sanjiang Plain was China’s grain base, holding a strategic position in the structure of the Chinese grain supply market [48]. Thus, a series of agricultural projects and water conservancy projects have been implemented in Sanjiang Plain to modernize agricultural production [49], causing obvious changes to the landscape, such as the ongoing transition from natural landscapes to artificial landscapes. During the period under study, in the initial period, to address the issue of insufficient food supply for the large Chinese population, a large portion of the unused land was cultivated. Due to low agricultural productivity and a lack of systematic agricultural landscapes, the process of cropland reclamation led to the appearance of the randomness in the landscape [40,50]. Then, after introducing and integrating a variety of world-leading agricultural machinery and equipment, the agricultural productivity in Sanjiang Plain greatly improved. Mechanical operations have been extensively applied in rice field and upland field production; rice cultivation machinery, seedling factory equipment, standardized seedling greenhouse facilities, rapid transplanting machines, and harvesting and processing facilities have greatly improved rice field productivity [51,52]. Grain ration shortages were subsequently relieved, and the Chinese people continued to enjoy a moderately prosperous existence. During this process, agricultural development paid more attention to the planning and designing of farmland patches, internal structure types, and comprehensive landscapes. Land leveling and the large-scale planting of different crops were widely implemented in this region. Thus, the cropland not only served the basic function of supplying grains but also satisfied the appreciation function by improving landscape aesthetics in conjunction with the influence of human activities.

4.3. One-Third of the Original Ecosystem Services Have Been Lost in Sanjiang Plain in the Last Fifty Years

The important finding of this study was that 33.65% of the original ecosystem services were lost from 1970 to 2020 in the study area. An improved ecosystem service assessment based on the equal factor model was established in this study, wherein land use types within construction areas were systematically classified through remote sensing and digital technologies, thereby improving assessment accuracy. Quantitative analysis revealed a decline in ecosystem service value from 233.784 to 165.401 billion CNY, with a cumulative loss of 68.384 billion CNY (i.e., −33.65% reduction rate) over the 50-year study period. Thus, Sanjiang Plain, a critical wetland region in Northeast China, experienced the loss of one-third of its ecosystem during this interval. By consulting the relevant materials and literature [53], the large loss of ecosystems was found to mainly be caused by the expansion of low-ecological-value croplands and the disproportionate loss of high-ecological-value forestlands and wetlands, resulting in many natural ecosystems being replaced by artificial ecosystems.

During the early development period, Sanjiang Plain functioned as an underutilized territory with abundant natural vegetation, such as weeds and forests, and scarce human habitation, a condition that earned it the historical appellation of the ‘Great Northern Wilderness’ [54]. Hydrologically abundant conditions historically supported extensive wetland ecosystems in the region [55], characterized by high ecological service provision. In response to the national food security challenges posed by population growth and limited cropland availability [31,56], Sanjiang Plain implemented large-scale agricultural expansion initiatives, leading to substantial reductions in alternative land use types. The implementation of grain production policies has significantly contributed to the marked expansion in cultivated land area. Concurrently, the agricultural mechanization process has facilitated the introduction of advanced farming machinery in Sanjiang Plain, resulting in measurable improvements in operational efficiency [57]. Furthermore, through systematic land consolidation measures and the construction of premium-grade farmland infrastructure, grain output efficiency has demonstrably improved [58]. Recently, the government department arranged funds to support the construction of field-supporting facilities in the Sanjiang Plain irrigation area, to promote groundwater improvement, and to protect the ecological environment. In addition, tax and fee preferential policies were implemented for agricultural enterprises, encouraging them to invest in modern agricultural equipment and technology to improve agricultural production efficiency [59]. Over the past five decades, Sanjiang Plain has experienced an approximately 33% reduction in the extent of its original ecosystem. The current land use configuration in this region has reached a state of equilibrium. With the application of advanced agricultural technologies and strengthened ecological conservation measures, there may be a potential way for ecosystem services to transition from decreasing to improving per unit area.

4.4. Limitations of This Study and Directions for Future Research

This study found that the cropland area in China’s grain production bases expanded by 81.04% from 1970 to 2020, driving the reclamation of low- to medium-productivity lands. Concurrently, the cropland centroid exhibited a biphasic southeastward shift, culminating in a 19.39 km² displacement over five decades. During this process, landscape integrity improved through reduced fragmentation and enhanced ecological connectivity, yet such land changes led to the loss of one-third of the native ecosystems. These findings will play an important role in managerial decision-making in the agriculture and land use domains. First, in terms of farmland development and protection, the priority should be to protect the existing high-yield cropland, while regionally tailored strategies such as soil amelioration and irrigation infrastructure development should also be implemented to optimize productivity and ensure spatial coherence in land use planning. Second, in response to the two-stage southeast cropland migration pattern from 1970 to 2020, it is imperative to recalibrate agricultural production systems by coordinating land allocation with regional resource endowments and ecological carrying capacities through targeted policy guidance and economic incentives in areas of cropland migration. Finally, faced with the issue of ‘one-third loss of native ecosystems,’ it is necessary to strictly define ecological protection policies and implement systematic land ecological restoration plans to gradually restore the integrity and stability of the ecosystem.

Furthermore, managerial decision-making in the agriculture and land use domains should be combined with the latest land data and ecological environment data; although this study encompasses a five-decade span from 1970 to 2020, it does not capture the latest land changes, particularly those occurring in 2025. This will require the integration of remote sensing datasets with digital classification technology in the future to generate high-resolution land use maps for 2025, enabling the timelier assessment of environmental effects from land changes. Meanwhile, the acute expansion of cropland in the study area, characterized by a rising proportion of paddy fields from 1970 to 2020, alters surface and subsurface hydrological regimes, thereby accelerating the deployment of agricultural irrigation infrastructure and riverbed maintenance projects. Consequently, a primary objective of the forthcoming research will be to quantify the long-term impacts of land use changes on hydrological dynamics, with the aim of generating evidence-based insights to service sustainable water resource management strategies.

5. Conclusions

This study centered on China’s national commodity grain base, conducting a comprehensive 50-year investigation into the coupled ‘land use change–trajectory migration–landscape evolution–ecological effects’ process through an integrated methodology, encompassing spatial analysis technologies, reclamation rate classification systems, landscape metrics, migration centroid/directional algorithms, and the improved ecosystem service model. The principal conclusions are as follows.

(1) Satellite observation results revealed that the cropland area expanded from 23,672.69 km² in 1970 to 42,856.17 km² by 2020, showing an 81.04% growth rate. Correspondingly, the reclamation rate increased from 31.91% to 57.77%, i.e., from a low to a medium level. Regional agricultural improvements ranged from 2.64% to 52.47%, with primary expansion toward the east, south, and north. We also found that the cropland structure exhibited sustained rice field expansion and a peak-then-decline pattern with respect to the upland fields from 1970 to 2020.

(2) Examination of the center of gravity migration trajectories and directional shift revealed the cropland’s biphasic shift, i.e., first east-to-west then north-to-south, resulting in a cumulative southeastward displacement of 19.39 km² over 50 years. Rice fields in cold temperate zones showed first a southward and then a northward migration, totaling 11.88 km², with sustained northward movement indicating improved cold resistance, and upland fields showed first a northward and then a southward migration, an 18.01 km² trajectory.

(3) From 1970 to 2020, the rapid expansion of cropland coincided with improved landscape integrity (SHDI = −0.08%), characterized by reduced fragmentation (PD = −48.14%), increased connectivity (CON = +8.82%) and patch advantage (LPI = +16.73%), and enhanced edge integrity (LSI = −15.71%). Within cropland structures, the substantial expansion of the rice field drove all landscape indices toward rice-favoring conditions, while upland fields exhibited the opposite trend.

(4) Another important discovery was that this study revealed a significant decline in ecosystem service value from 2337.84 × 10⁸ to 1654.01 × 10⁸ CNY, with a net loss of 683.84 × 10⁸ CNY over the 50-year period, representing a 33.65% reduction rate (i.e., one-third), which was primarily driven by the low-ecosystem service of cropland expansion and substantial losses in the high-ecosystem service of forests/wetlands. Functional assessments—i.e., supply, regulation, support, and cultural services—were also conducted for the period under study.