*Article* **Spatiotemporal Patterns in and Key Influences on Cultivated-Land Multi-Functionality in Northeast China's Black-Soil Region**

**Heyang Gong <sup>1</sup> , Zhibo Zhao <sup>2</sup> , Lei Chang <sup>1</sup> , Guanghui Li <sup>1</sup> , Ying Li <sup>1</sup> and Yuefen Li 1,\***


**Abstract:** Cultivated-land multi-functionality has become an important way to achieve sustainable cultivated-land protection, and it has become a hot spot in the field of land-management policy. Taking the cultivated black soils in the grain-producing area of Jilin Province, Northeast China, as a case study, this paper assessed the multi-functions of cultivated land over the past 30 years by applying the improved TOPSIS model. Furthermore, the key limiting factors and influencing factors of the multi-functions of cultivated land were identified through the obstacle-degree model and the Geo-detector. The results show that the level of multi-functionality rose from 1990 to 2020, but an increase in both economic and social functions hindered improvements in the ecological function of cultivated land. There were obvious spatial differences in the functions of cultivated land in different counties, with ecological functions showing the highest degree of differentiation, followed by social and economic functions. The per capita agricultural output, the degree of agricultural mechanization, the average output from cultivated land, and the agricultural-labor productivity had the most restrictive effects on the functions of cultivated land, with barrier-degree values of 15.90, 13.90, 11.76, and 10.30, respectively. Coupling–coordination in the multi-functions and sub-functions of cultivated land showed an upward trend, from "low coupling coordination–antagonistic coupling coordination" to "high coupling coordination-optimal coupling coordination". The government should include the level of multi-functional utilization in future policies for the management and utilization of cultivated land and take measures to reduce the differences in the functions of cultivated land among regions. Quantifying the multi-functional value of cultivated land and subsidizing land cultivation should encourage farmers to protect the land and help to strengthen multi-functional planning and functional design, improve ecological utilization, and promote the sustainable use of cultivated land.

**Keywords:** multi-functionality of cultivated land; breadbasket; spatiotemporal variation; coupling–coordination degree; influencing factors

#### **1. Introduction**

As a scarce and non-renewable resource, cultivated land provides many essential products and services for human society [1–3]. With the development of more urbanized societies and economies, cultivated land is not just limited to the traditional function of supplying food products but also carries many other non-productive functions, such as an economic-return function, a social-security function, an ecological function, and a landscape function [4–6]. However, the different functions of cultivated land have not been paid enough attention to in the utilization and management of cultivated land, which makes the contradiction between the supply and demand of cultivated-land functions

**Citation:** Gong, H.; Zhao, Z.; Chang, L.; Li, G.; Li, Y.; Li, Y. Spatiotemporal Patterns in and Key Influences on Cultivated-Land Multi-Functionality in Northeast China's Black-Soil Region. *Land* **2022**, *11*, 1101. https:// doi.org/10.3390/land11071101

Academic Editors: Li Ma, Yingnan Zhang, Muye Gan and Zhengying Shan

Received: 8 June 2022 Accepted: 14 July 2022 Published: 19 July 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

and eventually leads to the occurrence of unsustainable conditions such as cultivatedland degradation, non-grain cultivation, and abandonment [7–9]. The future expansion of cultivated-land production is likely to encounter a complex situation of competing demands and trade-offs [10]. Effective measures must be taken to balance the supply and demand of multi-functional cultivated land [11,12]. This requires in-depth knowledge of the level and changing characteristics of cultivated-land functionality and the factors influencing them to provide scientific support for the sustainable utilization and protection of cultivated land.

Multi-functional research originates from studies of agricultural multi-functionality [13], referring to the fact that, in addition to food production, agriculture also has a role in ecological services, landscape maintenance, employment security, and cultural heritage [14,15]. However, because of the differences in the types of crops grown and the responsibilities between cultivated land and agriculture, the multi-functionality of cultivated land expands the implications of economic, social, and ecological functions based on agricultural multifunctionality [16]. Particularly in the context of family-based agricultural production in China (a household-responsibility system), cultivated land has many participants who need to produce food to ensure food security, and the connotations of multi-functional cultivated land are rich and complex [17,18].

Quantitative evaluation is key to the study of multi-functional cultivated land and has been applied since the implementation of the Land Use and Land Cover Change (LUCC) program [19]. Currently, research is centered on two main aspects: evaluating a single function of cultivated land and a more comprehensive evaluation of the multifunctionality of cultivated land. The former includes the social value of cultivated land [20], and ecological [21] and monetary compensation [22]. The latter includes spatiotemporal analyses and understanding the driving factors behind the multiple functions of cultivated land [23–26]. However, the emphasis is often on the imbalance of a single function or a specific time point, and it is difficult to effectively trace temporal and spatial variations in the characteristics of cultivated land and its functions. As a result, our understanding of the multi-functionality of cultivated land is still poor. Long-term studies can be used to examine the rates of change over time and test the effectiveness of policies [27], yet there is a lack of long-term research on the multi-functionality of cultivated land. Equally, in terms of research application, the majority of studies focus on analyzing and evaluating the results, and rarely propose measures and policies to improve the function of cultivated land. Currently, the research outputs do not provide any guidance on the actual management of cultivated land, and the focus is usually on developed urbanized areas, where the conflict with cultivated land is more pronounced. Less attention has been paid to the multi-functionality of cultivated land in important grain-producing areas. To improve the shortcomings of existing research, a clear understanding of the historical change in cultivated-land functionality in major grain-producing regions is needed, and the obstacles and driving factors behind the changes in cultivated-land functions need to be identified, so that effective policies can be adopted for the future. The multi-functional utilization of cultivated land should, therefore, become the focus for the protection of cultivated land and the goal of sustainable utilization.

One of the world's major black-soil regions is found in northeast China. This fertile soil represents a key grain-producing area in China, and northeast China is an important exporter of commercial grains. The agricultural functional areas identified in "National Main Functional Area Plan" are also important in maintaining China's food security [28]. However, this region faces serious cropland degradation, including soil-nutrient loss [29], the thinning of the cultivated layer [30], and the loss of soil physicochemical properties. Simultaneously, unfavorable conditions for land cultivation, such as population outflow, low food prices, and reduced agricultural production efficiency, have begun to emerge [25], directly threatening the future sustainable use of cultivated land and the development of the region's economy and society. While these problems have existed across China for some time, they are particularly prominent in the main grain-producing areas. Cultivated land has various functions, but no one is investing in them. The government has not prioritized the issue of multi-functionality and currently lacks effective control measures, which is eroding farmers' rights and interests and reducing their enthusiasm for cultivated-land protection.

Counties represent the smallest unit with complete administrative power in China and are the basic unit for policy formulation and implementation. Carrying out research at the county level could yield direct and targeted suggestions for formulating practical and effective cultivated-land-use policies. Breadbaskets are the core unit for grain production in China's major grain-producing areas and are also important county-level units for cultivated-land management, making the black-soil region ideal for multi-functional research. Considering the research gaps highlighted above, this study used the breadbaskets in Jilin Province, in the hinterland of Northeast China, as a research area to evaluate the multi-functionality of cultivated land, analyze the obstacles to and driving factors behind the multi-functionality, and ultimately put forward suggestions for improving the multi-functionality of cultivated land. An improved Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model was used to evaluate the multi-functionality of cultivated land in the breadbaskets in 1990, 2000, 2010, and 2020. An obstacle-degree model was then used to determine the key obstacles limiting the functionality of the cultivated land. Geographic detectors were used to analyze the main factors changing cultivated-land functions. Finally, based on the analyses, effective measures to improve the multi-functional utilization of cultivated land in this area are proposed as a reference point for future developments.

#### **2. Materials and Methods**

#### *2.1. Study Area*

The study area was located in the geographic geometric center of Northeast Asia, spanning from 121◦380 to 131◦19 0 E and from 40◦500 to 46◦19 0 N, and is known as "Hometown of Black Soil", representing one of the world's three major black-soil belts. The soil in this region is fertile, with a high organic matter content (the average organic matter content being > 27 g/kg) and abundant cultivated-land resources.

The breadbaskets of China are the top-ranked counties based on the proportion of commercial grain output, overall grain production, and area sown for grain, accounting for 50%, 25%, and 25% by weight of all grain produced. In 2009, China's State Council promulgated "National Plan for Newly Increased Grain Production Capacity of 100 Billion catties (2009–2020)", and a total of 800 breadbaskets were identified as the core areas for grain production across the country. The breadbaskets chosen for this study were located in Jilin Province, northeast China (Figure 1); in total, 28 research units were represented and nine prefecture-level cities, including Changchun, Jilin, and Siping, for a total land area of 118,259.42 km<sup>2</sup> . Together, the breadbaskets account for 78.25% of the cultivated land in Jilin Province and contribute 89.7% of the province's grain output. However, while this area has made significant contributions to national and regional food security, its economic and social development faces serious challenges. It is the most important grain-production base in China, but over the last 10 years the population of the study area fell by an astonishing 25.08%, twice the overall rate for Jilin Province. Over the last 10 years, the gross domestic product (GDP) decreased by 10.54%, and the per capita income decreased by 3%. In contrast, over the same time period China's GDP and per capita income considerably grew, by 146.53% and 133.05%, respectively. The problems highlighted in this region are prevalent in many major grain-producing regions in the country; this study, therefore, can provide a reference point for similar regions.

**Figure 1.** The location and main land types in the black-soil breadbaskets in Jilin Province, Northeast China(The map of China in the figure is produced under the supervision of the Ministry of Natural Resources of the People's Republic of China, drawing number: GS (2019) No. 1673). **Figure 1.** The location and main land types in the black-soil breadbaskets in Jilin Province, Northeast China (The map of China in the figure is produced under the supervision of the Ministry of Natural Resources of the People's Republic of China, drawing number: GS (2019) No. 1673).

#### *2.2. Data Collection and Pre-Processing 2.2. Data Collection and Pre-Processing*

The details of the data used for the study are presented in Table 1. The Gauss–Kruger projection and 2000 National Geodetic Coordinate System (CGCS2000) were used, and the scale was unified to counties. The details of the data used for the study are presented in Table 1. The Gauss–Kruger projection and 2000 National Geodetic Coordinate System (CGCS2000) were used, and the scale was unified to counties.


**Table 1.** Descriptions of the data sources. **Table 1.** Descriptions of the data sources.

#### *2.3. Methods*

2.3.1. Classification and Quantification of the Multi-Functional Value of Cultivated Land

The varied classification criteria used for cultivated-land functions can be grouped into three main categories: economic, social, and ecological (Figure 2). *Land* **2022**, *11*, x FOR PEER REVIEW 5 of 19

**Figure 2.** A classification framework for the functions of cultivated land. **Figure 2.** A classification framework for the functions of cultivated land.

However, because of the differential development of human societies and different research areas, the functions of cultivated land and the strength of each function vary significantly, and the selection of indicators for a particular project must adhere to the principles of correlation and availability [31]. Table 2 presents a summary of the indicators chosen for this study. However, because of the differential development of human societies and different research areas, the functions of cultivated land and the strength of each function vary significantly, and the selection of indicators for a particular project must adhere to the principles of correlation and availability [31]. Table 2 presents a summary of the indicators chosen for this study.

**Table 2.** Indices for assessing cultivated-land functions. The overall per capita grain demand was determined as 400 kg per person per year [32]; the safety standard for chemical-fertilizer application followed the international chemical fertilizer application safety standard of 225 kg per hectare [33]; the degree of fragmentation of cultivated land was represented by the ratio of the number of cultivated-land patches to defined area, which was calculated based on an ArcGIS platform; the ecological value of cultivated land was calculated by referring to the ecological service value coefficient table compiled by Xie et al. [34]. **Table 2.** Indices for assessing cultivated-land functions. The overall per capita grain demand was determined as 400 kg per person per year [32]; the safety standard for chemical-fertilizer application followed the international chemical fertilizer application safety standard of 225 kg per hectare [33]; the degree of fragmentation of cultivated land was represented by the ratio of the number of cultivatedland patches to defined area, which was calculated based on an ArcGIS platform; the ecological value of cultivated land was calculated by referring to the ecological service value coefficient table compiled by Xie et al. [34].


output Gross agricultural output/Total population CNY/person Positive 0.1208

tion Total grain production/Total population kg/person Positive 0.078

× Population) Dimensionless Positive 0.078

tivated-land area kW/hm2 Negative 0.0976

Per capita cultivated land Cultivated-land area/Total population hm2/person Positive 0.0394

Per capita agricultural

Grain commodification index

Per capita grain produc-

Degree of agricultural mechanization

Social

Total power of agricultural machinery/Cul-

Grain production/(Per capita grain demand


**Table 2.** *Cont.*

The grain output of an area of land represents the traditional grain yield of cultivated land. As well as this, the economic function should also consider the increase in output value generated by cultivating the land, based on the average rate of rural-labor output and the value of the per capita agricultural output. The social function includes employment security for the local farmers and food security. The function of food security can be further divided into two types: intra-regional and extra-regional guarantees, which are expressed as per capita grain output and the grain-commercialization index, respectively. The per capita cultivated-land area, the degree of agricultural mechanization, and the labor-transfer index represent the employment-security function of cultivated land.

Cultivated land is also a part of an ecosystem and has an ecological function. Cultivated land has a positive effect on the ecological needs of human beings and supports biodiversity but can also have a negative impact on the environment if used unsympathetically. Positive effects can be quantified by the proportional ecological value of cultivated land, the effective-irrigation index, and the coefficient of land reclamation. The proportional land ecological value is calculated from the ecological-service-value coefficient [34], which reflects the ecological contribution of cultivated-land systems in all ecosystems and is an important indicator reflecting the basic ecological attributes of cultivated land. Negative effects mainly include the overuse of pesticides, an increase in agricultural energy consumption, and the fragmentation of cultivated land as a result of overuse. These are quantified using the fertilizer-use-intensity index, energy consumption per CNY ten thousand of output value, and the degree of fragmentation of cultivated land, respectively. The specific calculations used for each index are shown in Table 2.

2.3.2. Calculating the Multi-Functional Utilization of Cultivated Land and Determining any Obstacles

An improved TOPSIS ("the distance method between superior and inferior solution") model was used to evaluate the functional value of cultivated land [35]. The TOPSIS model is a commonly used multi-objective decision-making method that considers the advantages and disadvantages of a scheme by determining the distance between the index, and the "positive ideal solution" and "negative ideal solution". If the scheme is close to the "positive ideal solution" and far from the "negative ideal solution", it is superior; the converse means it is inferior. This method not only overcomes the lack of objectivity of, for example, the AHP and Delphi methods, but also the information-loss problem in factor analyses and mutation analyses [36]. TOPSIS models are widely used for decision analyses, environmental assessments, and land evaluations. However, the traditional TOPSIS model does not consider the weights of the indicators, as the weight of each indicator is the same

by default. This is inconsistent with real-life situations and widens the difference between the model results and empirical data. In this study, the weight determined with information entropy was used to modify the decision matrix, making the TOPSIS calculations more objective [35]. The specific steps applied were as detailed below.

Step 1: Build a decision matrix. First, the data were normalized, and indicator weights calculated. The range-standardization method was used to eliminate the differences between the dimensions and data levels for each indicator, and the entropy-weight method was used to determine the weight of each indicator (see Table 2 for the calculated weights). Both the range-standardization method and the entropy-weight method are objective and are widely used in statistics, geography, and elsewhere [26,36,37]. When applying the range-standardization method, data translation must be performed to eliminate the interference of extreme values and make the results as accurate as possible. The value of each item of standardized matrix *P* is then multiplied by its weight vector matrix *W* to obtain improved decision matrix *G*:

$$\mathbf{G} = \mathbf{P} \times \mathbf{W} = \begin{bmatrix} p\_{11} & p\_{12} & \cdots & p\_{1j} \\ p\_{21} & p\_{22} & \cdots & p\_{2j} \\ \vdots & \vdots & \ddots & \vdots \\ p\_{i1} & p\_{i2} & \cdots & p\_{ij} \end{bmatrix} \times \begin{bmatrix} w\_{1} \\ w\_{2} \\ \vdots \\ w\_{i} \end{bmatrix} = \begin{bmatrix} p\_{11} \times w\_{1} & p\_{12} \times w\_{1} & \cdots & p\_{1j} \times w\_{1} \\ p\_{21} \times w\_{2} & p\_{22} \times w\_{2} & \cdots & p\_{2j} \times w\_{2} \\ \vdots & \vdots & \ddots & \vdots \\ p\_{i1} \times w\_{i} & p\_{i2} \times w\_{i} & \cdots & p\_{ij} \times w\_{i} \end{bmatrix} = \begin{bmatrix} \mathbf{g}\_{11} & \mathbf{g}\_{12} & \cdots & \mathbf{g}\_{1j} \\ \mathbf{g}\_{21} & \mathbf{g}\_{22} & \cdots & \mathbf{g}\_{2j} \\ \vdots & \vdots & \ddots & \vdots \\ \mathbf{g}\_{i1} & \mathbf{g}\_{i2} & \cdots & \mathbf{g}\_{ij} \end{bmatrix} \tag{1}$$

where *pij* refers to the standardized value of index *i* in research unit *j*; *w<sup>i</sup>* refers to the weight of index *i*; and *gij* refers to the improved value of index *i* in research unit *j*.

Step 2: Calculate the ideal solution and the ideal value distance. The "positive ideal solution", *V* + *i* , and "negative ideal solution", *V* − *i* , of index *i* in the improved decision matrix were determined, and distances *D* + *j* and *D* − *j* from research unit *j* to *V* + *i* and *V* − *i* were measured. The closeness of the ideal solution to research unit *j*, Degree *T<sup>j</sup>* , was calculated as:

$$\begin{array}{l} V\_i^+ = \{ \max \text{g}\_{\overline{i}\overline{j}} | i = 1, 2, \dots \cdot m \} = \{ \mathbf{g}\_1^+, \mathbf{g}\_2^+, \dots, \mathbf{g}\_m^+ \} \\ V\_i^- = \{ \min \text{g}\_{\overline{i}\overline{j}} | i = 1, 2, \dots \cdot m \} = \{ \mathbf{g}\_1^-, \mathbf{g}\_2^-, \dots, \mathbf{g}\_m^- \} \end{array} \tag{2}$$

$$\begin{aligned} D\_j^+ &= \sqrt{\sum\_{i=1}^m \left( \mathcal{g}\_{ij} - \mathcal{g}\_i^+ \right)^2} (i = 1, 2, \dots, m) \\ D\_j^- &= \sqrt{\sum\_{i=1}^m \left( \mathcal{g}\_{ij} - \mathcal{g}\_i^- \right)^2} (i = 1, 2, \dots, m) \\ &= \dots \quad D^- \quad (1, \dots, 1, \dots) \end{aligned} \tag{3}$$

$$T\_j = \frac{D^-}{D^- + D^+} (1 \le j \le n) \tag{4}$$

where *T<sup>j</sup>* is the closeness of index *j*. With 0 ≤ *T<sup>j</sup>* ≤ 1, the larger the value for *T<sup>j</sup>* is, the better the overall effect of the multi-functional evaluation of cultivated land in the region is; conversely, the smaller the value is, the worse the effect is. When *T<sup>j</sup>* is closer to 1, the index is closer to the "positive ideal solution", indicating that the multi-functionality of the cultivated land is optimal, and the multi-functional use of the cultivated land has reached the expected goal. When *T<sup>j</sup>* is closer to 0, the index is closer to the "negative ideal solution", indicating that the multi-functionality of the cultivated land is poor, and the full potential multi-functionality of the cultivated land has not been reached.

Step 3: Determine any obstacles. Based on the multi-functional evaluation, an obstacledegree model was used to identify any obstacles affecting the multi-functionality of the cultivated land. This can be used as a baseline for the scientific and practical utilization of cultivated land and can improve the feasibility and effectiveness of cultivated-land protection policies and utilization. The obstacle degree was calculated as:

$$\mathcal{O}\_{\dot{j}} = \mathcal{R}\_{i\dot{j}} w\_{\dot{j}} / \left(\sum\_{i=1}^{m} \mathcal{R}\_{i\dot{j}} w\_{\dot{j}}\right), \mathcal{R}\_{i\dot{j}} = 1 - b\_{\dot{j}} \tag{5}$$

where *O<sup>j</sup>* is the obstacle degree of cultivated-land function *i* and index *j*.
