Bibliometric Analysis of Black Soil Protection from the Perspective of Land-Use Monitoring

Abstract

Land use affects ecosystem stability and agricultural ecological security in black soil regions. Additional attention is required regarding the impact of different land-use patterns on black soil. However, the construction of sustainable agricultural ecological security in black soil environments is a dynamic process that depends on the reviews of experts and statistical analyses of literature data. This study quantitatively reviewed the past 20 years of the literature regarding black soil. Using the superposition of the expert knowledge map and machine clustering, knowledge regarding land use in black soil fields was classified structurally. Further, studies directly related to the spatiotemporal pattern of land use were identified, and frequently cited works of the literature were screened to build a dynamic knowledge network of black soil research. The results show that (1) the cooperative relationship among China, the United States, and Canada is the strongest, but the density of cooperation networks between other countries is low; (2) land-use research regarding black soil is divided into four research areas: soil microbial community and activity, soil erosion and ecological processes, ecological management of land use, soil organic matter, and element cycling; (3) the monitoring and management mode of land use in black soil areas should be established to include information management that incorporates knowledge of the cultivated land factor potential, grain production capacity assessment, soil erosion evaluation and prediction, and farmland landscape planning.

1. Introduction

Black soil, also known as chernozem (Russia, FAO), kastanozem, phaeozem (FAO), or mollisol (USDA soil taxonomy), covers a relatively small proportion of the world’s ice-free surface area (7%) [1] and is mainly distributed in North America, Russia, Argentina, Uruguay, and Northeast China. The black colour of the soil is due to humic acids coating the clay minerals that form stable dark complexes [2,3]. In addition, some minerals are also responsible for their black colour. The melted lava theory is often used to study fertile and deep soil, such as the characteristics of volcanic soil and black soil. Black soil has inherent productivity and fertility due to its higher soil organic matter content, and it is of global importance for its relevance to food security [4,5].

Land use/cover change (LUCC) is the most significant form of human activity in the ecological environment and is closely related to climate change, ecosystem evolution, and biodiversity changes [6,7,8]. The strong disturbance resulting from the interaction between human and environmental factors [9,10], and soil erosion and degradation caused by land use changes, are increasingly destroying precious black soil resources [11,12]. The mean depth of black soil has now been reduced to 30 cm [13]. More importantly, soil degradation that results from erosion, losses of organic matter and nutrients, or soil compaction is of great concern in the black soil regions of the world and threatens food production security [14,15]. Therefore, it is essential to promote its conservation and sustainable use to maintain its functions in order to protect the environment and mitigate climate change, while maintaining food security [16].

With the advent of big data, the number of scientific publications has increased significantly. The contrast between the scale of available data and people’s limited time and interest has become increasingly acute. Bibliometric analysis has become the principal method for evaluating current conditions, characterizing evolutionary trajectories, and forecasting developmental trends [17]. Derwent Data Analyzer (DDA) is a professional bibliometric tool from Clarivate (https://clarivate.com/, accessed on 2 September 2021). As a statistical analysis tool for data cleaning and data mining, it has the advantage of converting scientific literature into quantitative data statistic tables. Additionally, it is highly reproducible due to its simplicity of operation. VOSviewer measures two high-frequency co-occurring objects from a probabilistic perspective [18].

Scholars have focused on the current black soil erosion conditions, proposed key research areas, and identified key issues regarding erosion types [19,20]. Additionally, they have assessed the impact of the key erosion factors on the complexity of responses to erosion [21,22,23,24,25,26,27]. Some scholars have examined soil health management [28,29,30,31,32,33] and proposed the zoning of areas vulnerable to nutrient loss, as well as regional pollution reductions. Some studies have comprehensively assessed the consumption and stability of organic carbon in black soil and proposed the effect of conservation management on SOC dynamics and soil characteristics in black soil [34,35,36,37,38]. Regarding black soil conservation and utilization, conservation tillage and participatory management are the most promising conservation measures [39,40,41,42]. However, the limitations of methods based on bibliometric analysis when integrating knowledge into the field of land use in black soil areas indicate a lack of a comprehensive understanding of the topic. This is particularly important in the current context.

The automatic extraction of knowledge from bibliometric analyses requires the guidance of expert knowledge and subsequent decision-making in each stage of the system. In this context, expert and bibliometric knowledge can work together to maximize their respective capabilities. Bibliometrics can be used as supplementary sources of knowledge for expert systems, and expert knowledge can be used to guide bibliometric analyses.

Therefore, this systematic review aims to explore combining expert knowledge and bibliometric-based black soil protection research, to identify popular frontier topics, and to track trends within the black soil protection field and the evolution of research disciplines [43,44]. The review also contributes to raising awareness of the importance of maintaining healthy ecosystems and human well-being and encouraging societies to improve black soil health from the perspective of land-use monitoring. More specifically, three research questions (RQs) are answered—RQ1: What is the structure of the international knowledge of black soil research networks? RQ2: What are the important thematic areas of black soil research, and what is the focus of black soil research from the land-use perspective? RQ3: What decision-making framework can be proposed for black soil conservation and utilization from the perspective of land use? These RQs are answered by identifying keywords related to “black soil” and selecting samples from the literature that are highly relevant to land-use research, by combining expert knowledge and machine analysis. Additionally, these RQs are the main mechanisms for pointing to the core directions of land-use research, which are intended to safeguard ecological security by addressing the growing challenges in black soil management.

To answer RQ1, important information had to be identified in international black soil literature, such as publication area, journal, and author. The research results had to be included in the joint analysis techniques. This review focused on the keywords and terms that can express the topic and structure of the knowledge of the research area [45,46]. The Derwent Data Analyzer and VOSviewer software were used to identify the superposition results of the keyword clustering and keyword grouping of the black soil land-use research using a human–computer interaction method. To answer RQ2, the black soil area land use was subjected to cluster analysis. Subsequently, the future research trajectory of this land use was subjected to a discourse analysis. From the perspective of land use, the practical framework of black soil protection and utilization was presented, as was required by RQ3. We adopted the bibliometric analysis method and content analysis method based on strict mathematical tools to strengthen the artificial analysis in terms of literature metrology and structured exploration. Finally, we formed a set of repeatable literature measurement analysis methods based on expert knowledge systems, providing a structure to explore some specific topics in the research area.

2. Methodology

2.1. Defining the Appropriate Search Terms

To classify and manage all international papers related to “black soil” topics, this review included all the libraries of Web of Science, including Retrieved by Web of Science Core Ensemble, Russian Science Citation Index, Current Contents Connect, and Chinese Science Citation Database (referred to as The Retrieved Database). The search syntax was as follows: TS = (soil and (Mississippi Plains or Ukrainian plain or Northeast China Plain)) OR TS = (black soil, chernozem, kastanozem, phaeozem, mollisol, or umbrisol), time span = 2000–2021. The selected search parameters provided a comprehensive and scientific coverage of the dynamics of the literature on “black soil” research. A total of 14,628 articles were retrieved. The literature search covered the period from January 2000 to September 2021. We attempted to collect all the literature on the subject of the study to ensure the accuracy and validity of the data throughout the retention period. We were convinced that the inclusion of the relevant literature for 2021 was necessary because we initially reviewed key literature published during this period that made significant contributions to the topic.

For example, Guan Yupeng et al. [47], Xie Yanhua et al. [48], and Bao Kunshan et al. [49] published several studies that have investigated land-use effects on soil properties on agricultural land. The papers were published in high-ranking journals, such as Science of the Total Environment, Remote Sensing of Environment, and Environmental Pollution (January to September 2021). The top scientific research institutes (such as the Chinese Academy of Sciences and the U.S. Department of Agriculture) published over a hundred articles during this time [50,51]. These high-level works are key components that are indispensable for bibliometric analyses and in-depth reviews of the literature on black soil protection from the perspective of land-use monitoring.

2.2. Building and Analysing the Database

There are various types of qualitative and quantitative literature review methods. Examples include systematic literature reviews, meta-analyses, bibliometric analyses, and content analyses [52]. The specific process of data collection and extraction in this study was as follows: The original comprehensive phrases (title, abstract, and key words) of all works of the literature in the retrieval database were extracted. This resulted in a total of 241,945 publications. Using the DDA software, 3535 comprehensive phrases were further extracted more than five times, and a classification system of land-use phrases in black soil areas was constructed. Based on expert knowledge, the comprehensive phrases, after being cleaned and refined, were used to determine a group of phrases. Out of 3535 phrases, those that were repeated more than 20 times were selected as representative phrases. There were 218 of these phrases.

To ensure the reproducibility of the study, we mapped out the technical flow chart of the workflow of the systematic review, as follows: (1) descriptive statistical analysis: based on the identification of core search terms and search periods, the visualization of the descriptive statistical analysis of the collected literature for relevant indicators (such as country, institution, journal, author, etc.) was conducted using R (version 4.1); (2) bibliometric analysis: the visual mapping of the research topic was mapped through a visual interaction method in VOSviewer for cluster analysis (construct phrase cluster) of the comprehensive word matrix; (3) combining qualitative- and quantitative-atlas-mapping analysis: based on expert knowledge, the classification system of core keywords (the phrase group) was determined, and the group and cluster were superimposed to distinguish the hot spots and research frontiers of land use in black soil areas; (4) in-depth review of the literature: we selected the core papers that were directly related to the research topics, screened out the highly cited papers among them, and conducted an in-depth review of the theoretical viewpoints, research fields, and development trends in the relevant literature (Figure 1).

2.3. Determination of Research Hotspots Enabled by Bibliometrics and Expert Knowledge

2.3.1. Bibliometric Analysis Based on a Systematic Clustering Approach

VOSviewer was introduced to develop a knowledge map based on the symbiosis matrix; the process of building the knowledge map included three steps [53]. Equations (1)–(3) were derived from VOSviewer. A similarity matrix could be obtained from the co-occurrence matrix by first correcting the matrix according to the difference in the total number of item occurrences or co-occurrences. This similarity measure is sometimes referred to as the proximity or probability affinity index (PAI). Using the strength of the association, the similarity between two items was calculated using Equation (1):

(1)$S_{ij}=\frac{c_{ij}}{w_i{w}_j}$

where, $c_{ij}$, denotes the number of co-occurrences of items i and j, while w_i and w_j denote either the total number of occurrences of items i and j or the total number of co-occurrences of these items.

In the second step, the VOS mapping technique was applied to the similarity matrix to construct the mapping. The concept of the VOS mapping technique is to minimize the weighted sum of the squared Euclidean distances between all pairs of terms. The greater the similarity between the two terms, the greater the weight of their squared distance in the sum of Equation (2):

(2)$V\left(x_1,\dots ,x_n\right)={\displaystyle \sum }_{i<j}{S}_{ij}\Vert x_1-x_j{\Vert }^2$

where vector $x_i=\left(x_{i1},x_{i2}\right)$ denotes the location of an item on a two-dimensional map, and ||•|| denotes the Euclidean norm. The minimization of the objective function is performed subject to the constraint:

(3)$\frac{2}{n\left(n-1\right)}{\displaystyle \sum }_{i<j}\Vert x_1-x_j\Vert =1$

The constrained optimization problem of minimizing (2) subject to (3) is solved numerically in two steps.

In the third step, the knowledge map was panned, rotated, and reflected. Identical coeval matrices should always produce the same mapping (ignoring differences caused by local optima). To achieve this, it is necessary to transform the solution obtained for the optimization problem discussed in Step 2. Typical transformation methods include translation, rotation, and reflection.

2.3.2. Knowledge Mining and Semantic Association Analysis

A total of 3535 comprehensive phrases, an increase from the previous number of phrases present in the search library by more than a factor of 5, was proposed as the basis for constructing the expert knowledge base. Knowledge regarding black soil classification content, characteristics, and association relations was collected. Additionally, comprehensive knowledge mining and a semantic association analysis were performed. A multi-type land-use expert knowledge classification system regarding black soil was formulated (Figure 2). The two principal research methods are presented below.

The first step was the collection of external domain expertise. We extracted the black soil research classification already constructed in VOSviewer clustering, and structured, semi-structured, and unstructured interviews were conducted with experts and scholars in the field of soil and land use. Multiple classification systems of black soil and its land-use data were collected, overlapped, de-weighted, and screened.

The second step was the formulation of a domain ontology. Phrase entities were extracted from the expert knowledge base and removed. Region/country phrases included terms such as Northeast China, Sanjiang Plain, the USA, etc.; year/year phrases included terms such as 2000, 3 years, past 300 years, etc. Further lexical recognition, lexical annotation, and shallow analysis of comprehensive phrases were achieved using knowledge-based categorization methods. A sample of close or synonymous phrases was extracted for temporary categorization. Based on the temporarily categorized phrase samples, other uncategorized phrases were located and identified with high confidence. However, external domain expert knowledge had to be consulted for further extraction and integration. Finally, the relationship extraction of fuzzy phrases was carried out to identify the relationship between fuzzy phrases and the context of the relevant papers to realize the clear categorization of difficult phrases.

3. Characteristics of the Literature Regarding Black Soil

3.1. Descriptive Analysis: Data Statistics

3.1.1. Annual Contribution and Influential Journals

Appropriate search terms were recognized as a critical component of the systematic literature review (Table 1). Among the six search terms, chernozem was the most frequent, with 5462 occurrences, followed by black soil, with 3738 occurrences. The remaining four search terms (mollisol, phaeozem, kastanozem, and umbrisol) had a lower number of posts, with a total of 1126 occurrences, accounting for only 10% of all keyword occurrences. Since 2000, chernozem has accounted for more than 50% of stage searches every five years. Chernozems are also a reference soil group in the World Reference Base for Soil Resources (WRB). Although the number of searches for chernozem and black soil has been increasing over the last two decades, the growth rate of searches for mollisol, phaeozem, kastanozem, and umbrisol has increased up to 32%. Scholars in this field are also expanding the breadth and depth of black soil research and improving its comprehensiveness (Table 1).

The retrieved literature database had a total of 11,672 articles and showed a yearly increase. The number of articles exceeded 1000 during 2019 (Figure 3). This indicates that black soil research is attracting attention in the global academic field, as the number of research results and academic exploration in this field by scholars is increasing annually. Among these occurrences, 4294 were from the Web of Science (WoS) core collection, and 3228 were from the CSCD (publication records have only been available since 2009). The WoS core collection contains authoritative and high-impact academic journals and conference literature globally, spanning more than 250 disciplines in total. The CSCD is mainly directed towards the inclusion of Chinese core journals in the fields of mathematics, physics, and chemistry, covering authoritative literature in the field of natural sciences in China. As is shown in Figure 3, the literature recorded in the CSCD repository shows an alternating growth trend with the core ensemble starting from 2009, but the overall difference is not significant. From 2016 to date, the WoS core collection has seen a sudden increase in black soil literature, and there were 167 articles from the CSCD library in 2018. This indicates that black soil is gradually receiving focused attention on a global scale, and the related authors are more willing to publish relevant papers in high-level foreign journals. This indicates that black soil research is gradually becoming global.

There were 20 publications with more than 100 articles in the search pool. Eurasian Soil Science published the most (614 articles), followed by Dostizheniya Nauki I Tekhniki Apk and Zemledelie (476 and 354 articles, respectively) (Figure 4). These journals are principally in the fields of agricultural and forestry sciences and cover subjects such as soil science, ecology, and agronomy. In addition to English journals, there were Russian and Chinese journals among our results. Russia occupies a major position in the field of international black soil research, but Russian publications are concentrated in national journals with marginal international influence. As one of the major countries in black soil research, China accounts for four of the top twenty international journals. This indicates that Chinese authors tend to submit to English and core journals when it comes to black soil research.

3.1.2. Relevant Institutions and Core Authors

In terms of the number of international publications, China had the most publications (4198), followed by Russia (1634) and the United States (973) (Figure 5). As is shown by the top 20 publishers and authors, Chinese Acad Sci ranked first, with 826 publications and 14,672 citations. Eleven Chinese research institutions were among the top 20 publishers. A total of 18 Chinese authors were among the top 20 publishers. However, Chinese scholars generally have a large number of co-authors listed for each publication, which may distort individual author counts. In terms of distribution regions, countries with more publications were mostly concentrated in the middle and high latitudes of the northern hemisphere, which has a high overlap with the three major black soil distribution regions in the world. All three black soil distribution areas are in the northern hemisphere in the cold-temperate humid region, which has low winter temperatures that are favourable for the accumulation of organic matter. There is also a black soil distribution area in the southern hemisphere, which is the most dominant reddened black soil distribution area in the world. This region is endowed with unique black soil resources, but research on black soil in these areas is scarce.

The size of national labels is proportional to the degree of contribution of each country. The line between national labels represents the interaction between each country. The thickness of the line represents the total number of relevant publications [54,55,56]. Although the number of publications in India is the greatest in cooperative network 3, its research is relatively independent and has not formed strong cooperative relationships with other countries. The Czech Republic, Germany, Hungary, Poland, Russia, and Serbia belong to the same cluster with significant interconnection; however, these countries cooperate on a regional scale. Canada, China, and the USA belong to the same cluster, with China being the core in developing international cooperation with the United States and Canada. Since 2021, there have been obvious complementarities and interoperability between the northeastern black soil regions of China and Russia. This is especially true in the Far East in terms of resource factor endowments and products. However, the cooperative relationship between these two countries in black soil research is marginal.

3.2. Domains of Research from Keywords to Linkages

3.2.1. Clustering of Comprehensive Phrases Based on Machine Analysis

Core keywords (n = 218) from more than 20 articles were extracted from the retrieval database, and a keyword matrix was constructed via a DDA software analysis. A land-use classification system for black soil research areas was constructed based on the VOSviewer software, and a keyword network co-existence diagram was drawn (Figure 6, Table 2). The length and thickness of the lines connecting two keywords indicate the frequency of the keywords appearing together. The size of the keyword tag indicates its frequency of appearing as a keyword. The interconnected and differently coloured networks in Figure 6 represent the seven clusters. Cluster 1 (blue cluster) contained 26.60% of the keywords that were mainly related to the effect of soil fertilization, including mineral fertilizers (38/48), ordinary chernozem (58/84), and grey forest soil (25/34). Cluster 2 (green cluster) contained 20.64% of the keywords that were mainly related to human activities and soil erosion, including soil erosion (176/318), remote sensing (32/39), and land use (175/264). Cluster 3 (purple cluster) contained 16.51% of the keywords that were mainly related to the soil microbial community and activity, including enzyme activities (38/63), chemical fertilizers (49/80), and soil microbial communities (55/82). Clusters 4–7 together contained 36.25% of the keywords, including soil organic carbon (189/238), fertilizer application (34/39), and available phosphorus (38/50). Cluster 4 (red cluster) related to soil fertilization and ecological processes, Cluster 5 (lavender cluster) to soil organic matter, Cluster 6 (yellow cluster) to soil organic carbon, and Cluster 7 (pink cluster) was related to trace elements, such as nitrogen and phosphorus.

It is noteworthy that SOM is the collective term for humus, plant, and animal residues and microbiomes formed through microbial action, the carbon content of which is known as SOC. Some components of SOC are more sensitive to changes in factors such as land-use practices than is SOM. To avoid conceptual generalization, we kept both cluster 5 (soil organic matter) and cluster 6 (soil organic carbon) in our study. This clustering accounts for various aspects, such as soil distribution area; classification and development process; soil structure, nature, and composition; ecological management measures; climate change; soil erosion; and pollution. However, these clusters are vague and cannot consider the spatial and temporal patterns of land use in black soil areas, ecological risk management, ecological monitoring and management, and other reasonable perspectives on black soil erosion, conservation, and utilization from the perspective of land use.

3.2.2. Grouping of Comprehensive Phrases Based on Expert Knowledge

According to the constructed knowledge base regarding land use in black soil areas based on expert knowledge determination (Table 3), the statistics regarding the number of articles belonging to the six categories show a large difference in the number of articles issued among the various categories of spatial and temporal patterns of land use in black soil areas; however, they all show trends of yearly increases and reach their maximums in 2020. “Micro-drivers” accounts for 7124 articles. These articles account for 48.7% of the mainstream trend. Additionally, the “micro-driver” articles reach a maximum number of 649 articles in 2020. They mainly cover microbial biomass (count 196/frequency 280), microbial community (130/162), and bacterial communities (92/133). The “natural drivers” are second in this area and mainly include climate change (105/148), climatic conditions (40/42), and global warming (31/37). “Ecological risk management” is in third place but had the lowest number of articles issued in 2001, with only three articles issued annually. It mainly includes soil erosion (176/318), gully erosion (40/113), and water erosion (50/75). The fourth category is social drivers. It includes net returns (12/26), economic returns (10/14), and economic efficiency (7/11). The fifth category is “ecological monitoring management”, which mainly includes remote sensing (32/39), sustainable agriculture (34/38), and digital soil mapping (17/20). The final category is the “spatial and temporal pattern of land use”, which principally includes land use (175/264), long-term changes (12/13), and spatial variations (11/13). The expert-knowledge-based classification refines each research point from a land-use perspective, focusing on ecological risks and their monitoring and management measures from a land-use perspective, as well as the impact of social drivers, natural drivers, and micro-drivers on land use in black soil areas. However, this has resulted in large category gaps, such as a nearly 50% share of micro-drivers, which has clear research dynamics, but they obscure the trend changes in other categories.

4. Dynamic Network Analysis and In-Depth Review of Land Use within the Black Soil Domain

The high-frequency phrases in the six groups were extracted, and the overlapping high-frequency phrases were calculated between groups and clusters (Figure 7). Group 2 (micro-drivers) had more overlapping words within each cluster due to its larger phrase base. Most occurrences were in regard to soil microbial community and activity. The majority of the research in this area detects dynamic changes in soil and soil cover [57,58]. In addition, this area further elucidates the characteristics of soil fertility indicators in black soil areas and their interrelationships [59] and clarifies soil fertility problems in black soil [60,61,62]. It is crucial to clarify the ecological processes of black soils and manage black soil degradation. In addition, cluster 4 (soil ecological processes), cluster 5 (soil organic matter), and cluster 6 (soil organic carbon) phrases, which mostly focus on soil organic matter, are highly interdisciplinary [63,64]. This can be summarized as soil organic matter and element cycling. By studying the stability of soil aggregates and soil organic carbon stocks under different land-use practices [35], we assessed the contribution of soil chemistry and binding agents to soil aggregation [36,37,38,65], explaining the spatial variability of SOC, refining agricultural management practices, and improving sustainable land use [66].

Group 1 (natural drivers) and group 3 (social drivers), with cluster 1 (influence of soil fertilization) and cluster 2 (soil erosion), had the most overlapping words. We merged group 1, group 3, cluster 1, and cluster 2 into “soil erosion and ecological processes”. Based on a large amount of recent research data, it has been shown that centralized storage of core data can be achieved by establishing a comprehensive black soil evaluation big data platform. This provided extensive real-time farmland data for the accurate management of agricultural resources, dynamic monitoring of crop growth and nutrient status [67], and assessment of arable land factor potential and food production capacity [68]. In addition, it provided a soil erosion risk evaluation and early warning [69] and services such as consultation regarding farmland landscape layout [70]. This supports the collaborative implementation of intelligent agricultural equipment, water and fertilizer integration equipment, and automatic control equipment. This has improved the ability of precise operation, variable operation, farm machinery scheduling, and automatic control. Groups 4 (ecological monitoring and management), 5 (spatiotemporal pattern of land use), and 6 (ecological risk management) in cluster 2 (soil erosion) focus on the ecological management of land use. Recently, near-Earth full-spectrum camera imaging has been used as the primary source for black land data. Specifically, the telemetry system consists of a “hyperspectral + multi-spectral + infrared integrated” modular system. It utilizes multi-platform multi-type UAV mission planning, regional network control, and other key technologies [71]. It can be used to evaluate the adverse changes in some soil properties caused by the long-term use of black soil in agriculture. It can also be used to analyse crop rotation patterns based on landscapes and ecologies and the interactive effects of black soil land management practices and irrigation systems on soil properties and crop productivity.

5. Conclusions

Based on 11,672 documents in the search library, we conducted an overlay analysis of the knowledge map and an in-depth review of the literature on black soil protection from the perspective of land-use monitoring. The current research indicates that the literature regarding land use in black soil areas is diverse, and the connections within it are complex. The coupling of bibliometric analysis and expert knowledge determination can clarify the research lineage of land use in black soil areas. These domains are interrelated, although the study phrases based on machine clustering show some errors when compared with the study phrases based on expert knowledge. A bibliometric analysis of the literature coupled with expert knowledge can point out research gaps and pinpoint the core directions of land-use research in black soil areas to develop cutting-edge methods that support land-use monitoring in order to safeguard ecological security in black soil areas.

The following aspects can be enhanced for the scientific community, as well as policymakers and black soil managers, to safeguard the ecological security in black soil regions: (1) Strengthen transnational cooperation in black soil research. Black soil research is attracting attention in the global academic field, and the number of research results and level of academic exploration in this field by scholars are increasing annually. In terms of distribution regions, countries with more publications were mostly concentrated in the middle and high latitudes of the northern hemisphere, which has a high overlap with the three major black soil distribution regions in the world. The cooperative relationship between China, the United States, and Canada is the strongest, but the density of cooperation networks between other countries is low; (2) Establish more timely and scientific focus on the black soil theme by combining expert knowledge and bibliometric methods. Calculate the overlapping high-frequency phrases between clusters and groups by combining machine analysis and expert knowledge. This method could help to show a clear research theme of black soil protection from the perspective of land-use monitoring. It includes adjustment of soil microbial community and activity, optimization of the soil erosion and ecological processes, enhancement of ecological management of land use, increasing soil organic matter, and promotion of element cycling; (3) Propose a decision-making framework for the conservation and utilization of black soil from the perspective of land-use monitoring. First, we should provide a scientific basis for soil and water conservation work in black soil regions. Through the integrated monitoring system of “sky–air–ground”, one can explore the influence of soil erosion and establish a more reasonable land-use structure. Second, we should formulate the development of feasible carbon sink schemes in black soil regions. To investigate the soil characteristics of black soils under different fertilization methods, the effects of fertilization on black soils must be explored. In addition, considering the increase in organic matter in black soils with different fertilization structures and patterns is important. Third, we should use the available resources in the black soil zone in a more efficient and sustainable manner. Interpretation regarding the components of agricultural systems in black soil zones through the agroecological monitoring of cropping systems is important. Overall, we should regulate the command system for optimizing the conservation and use of black soil areas. The management system for large-scale cultivation, the data-sharing system, and a crop-growth monitoring system guide are necessary for decision making from the perspective of land use in black soil areas.

Author Contributions

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

Data Availability Statement

Not applicable.

Acknowledgments

We would like to thank all the partners involved in this study and the experimental platform provided by Northeast Forestry University. The authors are also grateful to the journal’s editors and anonymous reviewers for their helpful comments.

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1. Workflow of the systematic review. The data collection and refinement process is on the left. The process of bibliometric analysis and review is on the right.

Figure 2. Expert knowledge workflow fusion research on land use in black soil areas. It combines external domain professional data with ontology data within the domain.

Figure 3. The annual number of articles. The related literature was searched for in the database from January 2000 to September 2021.

Figure 4. The number of publications. The related literature was searched for in the database from January 2000 to September 2021.

Figure 5. Relevant institutions, core authors, and the number of articles published globally, January 2000 to September 2021. Summary of the top 20 institutions and top 20 authors by the number of publications. Summary of the number of global publications and the significant links in the top 10 countries.

Figure 6. Four main research domains are distinguished according to the keyword network: soil erosion and ecological processes, fertilizer management and element cycling, soil microbial community and activity, and soil organic matter.

Figure 7. Number distributions of high-frequency phrase overlaps of clusters and groups. The size of the circle represents the words that the cluster and group repeats among the 218 words.

Table 1

Number of posts for six search terms at 5-year intervals.

	Chernozem	Black Soil	Mollisol	Phaeozem	Kastanozem	Umbrisol
2000–2005	465	293	79	55	13	7
2006–2010	949	765	138	74	10	10
2011–2015	1591	1147	160	74	14	18
2016–2021	2457	1533	272	142	38	22

Table 2

Clustering and grouping of comprehensive phrases.

Clustering of Comprehensive Phrases		Grouping of Comprehensive Phrases
Cluster 1	Influence of soil fertilization	Group 1	Nature drivers
Cluster 2	Soil erosion	Group 2	Micro drivers
Cluster 3	Soil microbial community	Group 3	Social drivers
Cluster 4	Soil ecological process	Group 4	Ecological monitoring and management
Cluster 5	Soil organic matter	Group 5	Spatiotemporal pattern of land use
Cluster 6	Soil organic carbon	Group 6	Ecological risk management
Cluster 7	Trace elements of phosphorus and nitrogen

Table 3

Classification system based on expert knowledge.

	Knowledge Source	Category
Collection of external domain expertise	Machine-clustering results	Influence of soil fertilization
		Soil erosion
		Soil microbial community
		Soil ecological process
		Soil organic matter
		Soil organic carbon
		Trace elements of phosphorus and nitrogen
	Specialist research	Soil classification and developmental processes
		Soil structure, properties, and composition
		Crop type, growth process, and yield
		Soil erosion and loss
		Climate change
		Sustainable agriculture management
		Soil trace element stabilization and health
		Spatial distribution and spatial and temporal variation
Construction of domain ontology knowledge base	Spatiotemporal pattern of land use	Temporal dynamics of land use
		Spatial variability of land use
	Social drivers	Driven by socio-economic factors
		Impact of agricultural resources
	Natural drivers	Changes in climate temperature
		The influence of regional geography
	Micro drivers	Biological properties such as soil properties
		Physical properties such as soil structure and composition
	Ecological risk management	Soil erosion and loss
		Land management and soil and water conservation
	Ecological monitoring and management	Agricultural monitoring management
		Scientific and technical methods of monitoring
		Regulatory governance of relevant policies

References

1. Liu, X.B.; Charles, L.B.; Kravchenko, Y.S.; Duran, A.; Huffman, T.; Morras, H.; Studdert, G.; Zhang, X.Y.; Richard, M.C.; Yuan, X.H. Overview of Mollisols in the world: Distribution, land use and management. Can. J. Soil Sci.; 2012; 92, pp. 383-402. [DOI: https://dx.doi.org/10.4141/cjss2010-058]

2. Liu, J.J.; Sui, Y.Y.; Yu, Z.H.; Shi, Y.; Chu, H.Y.; Jin, J.; Liu, X.B.; Wang, G.H. Soil carbon content drives the biogeographical distribution of fungal communities in the black soil zone of northeast China. Soil Biol. Biochem.; 2015; 83, pp. 29-39. [DOI: https://dx.doi.org/10.1016/j.soilbio.2015.01.009]

3. Yadav, D.; Vishwakarma, A.K.; Sharma, N.K.; Biswas, A.K.; Ojasvi, P.R.; Kumar, D.; Kumawat, A.; Singh, D. Sustaining the properties of black soil in Central India through crop residue management in a conservation-agriculture-based soybean–wheat system. Land Degrad. Dev.; 2021; 32, pp. 2906-2921. [DOI: https://dx.doi.org/10.1002/ldr.3891]

4. Liu, X.B.; Zhang, X.Y.; Wang, Y.X.; Sui, Y.Y.; Zhang, S.L.; Herbert, S.J.; Ding, G. Soil degradation: A problem threatening the sustainable development of agriculture in Northeast China. Plant Soil Environ.; 2010; 56, pp. 87-97. [DOI: https://dx.doi.org/10.17221/155/2009-PSE]

5. Duan, X.W.; Xie, Y.; Ou, T.H.; Lu, H.M. Effects of soil erosion on long-term soil productivity in the black soil region of northeastern China. Catena; 2011; 87, pp. 268-275. [DOI: https://dx.doi.org/10.1016/j.catena.2011.06.012]

6. Kalnay, E.; Cai, M. Impact of urbanization and land-use change on climate. Nature; 2003; 423, pp. 528-531. [DOI: https://dx.doi.org/10.1038/nature01675]

7. Letourneau, A.; Verburg, P.H.; Stehfest, E. A land-use systems approach to represent land-use dynamics at continental and global scales. Environ. Model. Softw.; 2012; 33, pp. 61-79. [DOI: https://dx.doi.org/10.1016/j.envsoft.2012.01.007]

8. Liu, S.H.; Wang, D.Y.; Lei, G.P.; Li, H.; Li, W.B. Elevated risk of ecological land and underlying factors associated with rapid urbanization and overprotected agriculture in Northeast China. Sustainability; 2019; 11, 6203. [DOI: https://dx.doi.org/10.3390/su11226203]

9. Luna-Robles, E.O.; Cantú-Silva, I.; González-Rodríguez, H.; Marmolejo-Monsiváis, J.; Yáñez-Díaz, M.I.; Hernández, F.J.; Béjar-Pulido, S.J. Effects of forest management on the physical and hydrological properties of an Umbrisol in the Sierra Madre Occidental. Rev. Chapingo Ser. Cienc. For.; 2021; 27, pp. 19-32. [DOI: https://dx.doi.org/10.5154/r.rchscfa.2019.11.085]

10. Junior, R.V.; Varandas, S.; Fernandes, L.S.; Pacheco, F.A.L. Environmental land use conflicts: A threat to soil conservation. Land Use Pol.; 2014; 41, pp. 172-185. [DOI: https://dx.doi.org/10.1016/j.landusepol.2014.05.012]

11. Jiang, C.; Zhang, H.Y.; Zhao, L.L.; Yang, Z.Y.; Wang, X.C.; Yang, L.; Wen, M.L.; Geng, S.B.; Zeng, Q.; Wang, J. Unfolding the effectiveness of ecological restoration programs in combating land degradation: Achievements, causes, and implications. Sci. Total Environ.; 2020; 748, 141552. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2020.141552] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32827896]

12. Zerihun, M.; Mohammedyasin, M.S.; Sewnet, D.; Adem, A.A.; Lakew, M. Assessment of soil erosion using RUSLE, GIS and remote sensing in NW Ethiopia. Geoderma Reg.; 2018; 12, pp. 83-90. [DOI: https://dx.doi.org/10.1016/j.geodrs.2018.01.002]

13. Tang, J.; Liu, G.; Xie, Y.; Wu, Y.Q.; Wang, D.A.; Gao, Y.; Meng, L.Q. Effect of topographic variations and tillage methods on gully erosion in the black soil region: A case-study from Northeast China. Land Degrad. Dev.; 2022; 26, pp. 779-788. [DOI: https://dx.doi.org/10.1002/ldr.4423]

14. Zhu, G.Y.; Tang, Z.S.; Shangguan, Z.P.; Peng, C.H.; Deng, L. Factors affecting the spatial and temporal variations in soil erodibility of China. J. Geophys. Res. F Earth Surf.; 2019; 124, pp. 737-749. [DOI: https://dx.doi.org/10.1029/2018JF004918]

15. Wang, B.Q.; Hao, J.M.; Meng, D.B.; Li, W.; Yuan, Q.Z. Methods of land consolidation in chernozems region of China. Trans. Chin. Soc. Agric. Eng.; 2008; 24, pp. 44-48. [DOI: https://dx.doi.org/10.3321/j.issn:1002-6819.2008.09.009]

16. Hu, X.J.; Liu, J.J.; Zhu, P.; Wei, D.; Jin, J.; Liu, X.B.; Wang, G.H. Long-term manure addition reduces diversity and changes community structure of diazotrophs in a neutral black soil of northeast China. J. Soils Sediments.; 2018; 18, pp. 2053-2062. [DOI: https://dx.doi.org/10.1007/s11368-018-1975-6]

17. Maditati, D.R.; Munim, Z.H.; Schramm, H.J.; Kummer, S. A review of green supply chain management: From bibliometric analysis to a conceptual framework and future research directions. Resour. Conserv. Recy.; 2018; 139, pp. 150-162. [DOI: https://dx.doi.org/10.1016/j.resconrec.2018.08.004]

18. Eck, N.J.V.; Waltman, L. How to normalize cooccurrence data? An analysis of some well-known similarity measures. J. Am. Soc. Inf. Sci.; 2009; 60, pp. 1635-1651. [DOI: https://dx.doi.org/10.1002/asi.21075]

19. Yang, X.M.; Zhang, X.P.; Deng, W.; Fang, H.J. Black soil degradation by rainfall erosion in Jilin, China. Land Degrad. Dev.; 2003; 14, pp. 409-420. [DOI: https://dx.doi.org/10.1002/ldr.567]

20. Zhang, K.L.; Liu, H.Y. Research progresses and prospects on freeze thaw erosion in the black soil region of Northeast China. Sci. Soil Water Conserv.; 2018; 16, pp. 17-24. [DOI: https://dx.doi.org/10.16843/j.sswc.2018.01.003]

21. Vanmaercke, M.; Poesen, J.; Van Mele, B.; Demuzere, M.; Bruynseels, A.; Golosov, V.; Bezerra, J.F.R.; Bolysov, S.; Dvinskih, A.; Frankl, A. et al. How fast do gully headcuts retreat?. Earth-Sci. Rev.; 2016; 154, pp. 336-355. [DOI: https://dx.doi.org/10.1016/j.earscirev.2016.01.009]

22. Fu, Q.; Hou, R.J.; Li, T.X.; Wang, M.; Yan, J.W. The functions of soil water and heat transfer to the environment and associated response mechanisms under different snow cover conditions. Geoderma; 2018; 325, pp. 9-17. [DOI: https://dx.doi.org/10.1016/j.geoderma.2018.03.022]

23. Gao, S.; Jia, Y.F.; Fan, H.M.; Li, G.N. Effect of freezing and thawing on soil anti-scourability under different land use types in the black soil region of northeast China. J. Soil Water Conserv.; 2015; 29, pp. 69-73. [DOI: https://dx.doi.org/10.13870/j.cnki.stbcxb.2015.06.013]

24. Li, H.; Cruse, R.M.; Liu, X.B.; Zhang, X.Y. Effects of topography and land use change on gully development in typical Mollisol region of Northeast China. Chin. Geogr. Sci.; 2016; 26, pp. 779-788. [DOI: https://dx.doi.org/10.1007/s11769-016-0837-7]

25. Deng, Q.C.; Qin, F.C.; Zhang, B.; Wang, H.P.; Luo, M.L.; Shu, C.Q.; Liu, H.; Liu, G.C. Characterizing the morphology of gully cross-sections based on PCA: A case of Yuanmou Dry-Hot Valley. Geomorphology; 2015; 228, pp. 703-713. [DOI: https://dx.doi.org/10.1016/j.geomorph.2014.10.032]

26. Li, H.; Zhang, X.Y.; Liu, S.; Kravchenko, Y.; Lvanova, K.; Li, X.F.; Chen, Q. Evolvement of gully erosion in village scale in the typical black soil area. Sci. Soil Water Conserv.; 2012; 2, pp. 21-28. [DOI: https://dx.doi.org/10.16843/j.sswc.2012.02.004]

27. Castillo, C.; Gómez, J.A. A century of gully erosion research: Urgency, complexity and study approaches. Earth-Sci. Rev.; 2016; 160, pp. 300-319. [DOI: https://dx.doi.org/10.1016/j.earscirev.2016.07.009]

28. Frąc, M.; Hannula, S.E.; Bełka, M.; Jędryczka, M. Fungal biodiversity and their role in soil health. Front. Microbiol.; 2018; 9, 707. [DOI: https://dx.doi.org/10.3389/fmicb.2018.00707]

29. Zhang, S.L.; Zhang, X.Y.; Huffman, T.; Liu, X.B.; Yang, J.Y. Influence of topography and land management on soil nutrients variability in Northeast China. Nutri. Cycl. Agroecosyst.; 2011; 89, pp. 427-438. [DOI: https://dx.doi.org/10.1007/s10705-010-9406-0]

30. Hou, X.Y.; Han, X.Z.; Wang, S.Q.; Song, C.; Zhang, D. Effect of land-use type on enzyme activity in black soil. Chin. J. Ecol.; 2009; 17, pp. 215-219. [DOI: https://dx.doi.org/10.3724/SP.J.1011.2009.00215]

31. Yu, Z.H.; Liu, J.J.; Liu, J.D.; Jin, J.; Liu, X.B.; Wang, G.H. Responses of ammonia-oxidizing bacterial communities to land-use and seasonal changes in Mollisols of Northeast China. Eur. J. Soil Biol.; 2016; 74, pp. 121-127. [DOI: https://dx.doi.org/10.1016/j.ejsobi.2016.03.012]

32. Zhelezova, A.D.; Tkhakakhova, A.K.; Yaroslavtseva, N.V.; Garbuz, S.A.; Lazarev, V.I.; Kogut, B.M.; Kutovaya, O.V.; Kholodov, V.A. Microbiological parameters of aggregates in typical chernozems of long-term field experiments. Eurasian Soil Sci.; 2017; 50, pp. 701-707. [DOI: https://dx.doi.org/10.1134/S1064229317060126]

33. Blagodatskii, S.A.; Bogomolova, I.N.; Blagodatskaya, E.V. Microbial biomass and growth kinetics of microorganisms in chernozem soils under different land use modes. Microbiology; 2008; 77, pp. 99-106. [DOI: https://dx.doi.org/10.1134/S0026261708010141]

34. Xu, X.R.; Pei, J.B.; Xu, Y.D.; Wang, J.K. Soil organic carbon depletion in global Mollisols regions and restoration by management practices: A review. J. Soils Sediments; 2020; 20, pp. 1173-1181. [DOI: https://dx.doi.org/10.1007/s11368-019-02557-3]

35. Mikhailova, E.A.; Bryant, R.B.; Vassenev, I.I.; Schwager, S.J.; Post, C.J. Cultivation effects on soil carbon and nitrogen contents at depth in the Russian Chernozem. Soil Sci. Soc. Am. J.; 2000; 64, pp. 738-745. [DOI: https://dx.doi.org/10.2136/sssaj2000.642738x]

36. Huang, J.Y.; Song, C.C. Effects of land use on soil water soluble organic C and microbial biomass C concentrations in the Sanjiang Plain in northeast China. Acta Agric. Scand. B Soil Plant Sci.; 2010; 60, pp. 182-188. [DOI: https://dx.doi.org/10.1080/09064710802680387]

37. Ryzhova, I.M.; Telesnina, V.M.; Sitnikova, A.A. Dynamics of Soil Properties and Carbon Stocks Structure in Postagrogenic Ecosystems of Southern Taiga during Natural Reforestation. Eurasian Soil Sci.; 2020; 53, pp. 240-252. [DOI: https://dx.doi.org/10.1134/S1064229320020106]

38. Zhao, M.S.; Zhang, G.L.; Wang, D.C.; Li, D.C.; Pan, X.Z.; Zhao, Y.G. Spatial variability of soil organic matter and its dominating factors in Xu-Huai alluvial plain. Acta Petrol. Sin.; 2013; 50, pp. 1-11.

39. Xu, X.Z.; Xu, Y.; Chen, S.C.; Xu, S.G.; Zhang, H.W. Soil loss and conservation in the black soil region of Northeast China: A retrospective study. Environ. Sci. Policy; 2010; 13, pp. 793-800. [DOI: https://dx.doi.org/10.1016/j.envsci.2010.07.004]

40. Zentner, R.P.; Lafond, G.P.; Derksen, D.A.; Campbell, C.A. Tillage method and crop diversification: Effect on economic returns and riskiness of cropping systems in a Thin Black Chernozem of the Canadian Prairies. Soil Tillage Res.; 2002; 67, pp. 9-21. [DOI: https://dx.doi.org/10.1016/S0167-1987(02)00028-4]

41. Fang, X.Q.; Ye, Y.; Zhang, C.P.; Tang, C.C. Cropland cover change and its environmental impacts in the history of China. J. Palaeogeogr.; 2019; 21, pp. 160-174.

42. Chan, C.; Sipes, B.; Ayman, A.; Zhang, X.; LaPorte, P.; Fernandes, F.; Pradhan, A.; Chan-Dentoni, J.; Roul, P. Efficiency of conservation agriculture production systems for smallholders in rain-fed uplands of India: A transformative approach to food security. Land; 2017; 6, 58. [DOI: https://dx.doi.org/10.3390/land6030058]

43. Yu, D.J.; Kou, G.; Xu, Z.S.; Shi, S.S. Analysis of collaboration evolution in AHP research: 1982–2018. Int. J. Inf. Technol. Decis. Mak.; 2021; 20, pp. 7-36. [DOI: https://dx.doi.org/10.1142/S0219622020500406]

44. Xu, X.M.; Feng, C. Mapping the knowledge domain of the evolution of emergy theory: A bibliometric approach. Environ. Sci. Pollut. Res.; 2021; 28, pp. 43114-43142. [DOI: https://dx.doi.org/10.1007/s11356-021-14959-3] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34152539]

45. Donthu, N.; Kumar, S.; Ranaweera, C.; Sigala, M.; Sureka, R. Journal of Service Theory and Practice at age 30: Past, present and future contributions to service research. J. Serv. Theory Pract.; 2021; 31, pp. 265-295. [DOI: https://dx.doi.org/10.1108/JSTP-10-2020-0233]

46. Emich, K.J.; Kumar, S.; Lu, L.; Norder, K.; Pandey, N. Mapping 50 years of small group research through small group research. Small Group Res.; 2020; 51, pp. 659-699. [DOI: https://dx.doi.org/10.1177/1046496420934541]

47. Guan, Y.P.; Jiang, N.N.; Wu, Y.X.; Yang, Z.Z.; Bello, A.; Yang, W. Disentangling the role of salinity-sodicity in shaping soil microbiome along a natural saline-sodic gradient. Sci. Total Environ.; 2021; 765, 142738. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2020.142738]

48. Xie, Y.H.; Lark, T.J. Mapping annual irrigation from Landsat imagery and environmental variables across the conterminous United States. Remote Sens. Environ.; 2021; 260, 112445. [DOI: https://dx.doi.org/10.1016/j.rse.2021.112445]

49. Bao, K.S.; Zhang, Y.F.; Zaccone, C.; Meadows, M.E. Human impact on C/N/P accumulation in lake sediments from northeast China during the last 150 years. Environ. Pollut.; 2021; 271, 116345. [DOI: https://dx.doi.org/10.1016/j.envpol.2020.116345]

50. Weitzman, J.N.; Groffman, P.M.; Adler, P.R.; Dell, C.J.; Johnson, F.E.; Lerch, R.N.; Strickland, T.C. Drivers of hot spots and hot moments of denitrification in agricultural systems. J. Geophys. Res. Biogeosci.; 2021; 126, e2020JG006234. [DOI: https://dx.doi.org/10.1029/2020JG006234]

51. Wang, L.J.; Ma, S.; Zhao, Y.G.; Zhang, J.C. Ecological restoration projects did not increase the value of all ecosystem services in Northeast China. For. Ecol. Manag.; 2021; 495, 119340. [DOI: https://dx.doi.org/10.1016/j.foreco.2021.119340]

52. Zupic, I.; Čater, T. Bibliometric methods in management and organization. Organ. Res. Methods; 2015; 18, pp. 429-472. [DOI: https://dx.doi.org/10.1177/1094428114562629]

53. Eck, N.J.V.; Waltman, L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics; 2010; 84, pp. 523-538. [DOI: https://dx.doi.org/10.1007/s11192-009-0146-3] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/20585380]

54. Mao, G.; Shi, T.; Zhang, S.; Crittenden, J.; Guo, S.; Du, H. Bibliometric analysis of insights into soil remediation. J. Soils Sediments; 2018; 18, pp. 2520-2534. [DOI: https://dx.doi.org/10.1007/s11368-018-1932-4]

55. Guo, Y.; Zhou, X.; Porter, A.L.; Robinson, D.K.R. Tech mining to generate indicators of future national technological competitiveness: Nano-Enhanced Drug Delivery (NEDD) in the US and China. Technol. Forecast. Soc. Chang.; 2015; 97, pp. 168-180. [DOI: https://dx.doi.org/10.1016/j.techfore.2014.02.026]

56. Zhang, Y.; Porter, A.L.; Hu, Z.Y.; Guo, Y.; Newman, N.C. “Term clumping” for technical intelligence: A case study on dye-sensitized solar cells. Technol. Forecast. Soc. Chang.; 2014; 85, pp. 26-39. [DOI: https://dx.doi.org/10.1016/j.techfore.2013.12.019]

57. Steinberger, Y.; Doniger, T.; Sherman, C.; Applebaum, I.; Eshel, G. Effect of Soil Aggregate Size on Vineyard Bacterial Communities under Organic and Conventional Agro-Managements. Land; 2022; 11, 1517. [DOI: https://dx.doi.org/10.3390/land11091517]

58. Fathololoumi, S.; Vaezi, A.R.; Alavipanah, S.K.; Ghorbani, A.; Saurette, D.; Biswas, A. Improved digital soil mapping with multitemporal remotely sensed satellite data fusion: A case study in Iran. Sci. Total Environ.; 2020; 721, 137703. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2020.137703]

59. Soares, M.D.R.; Souza, Z.M.; Campos, M.C.C.; Silva, R.B.; Esteban, D.A.A.; Noronha, R.L.; Gomes, M.G.S.; Cunha, J.M. Land-use change and its impact on physical and mechanical properties of Archaeological Black Earth in the Amazon rainforest. Catena; 2021; 202, 105266. [DOI: https://dx.doi.org/10.1016/j.catena.2021.105266]

60. Savin, I.; Prudnikova, E.; Chendev, Y.; Bek, A.; Kucher, D.; Dokukin, P. Detection of Changes in Arable Chernozemic Soil Health Based on Landsat TM Archive Data. Remote Sens.; 2021; 13, 2411. [DOI: https://dx.doi.org/10.3390/rs13122411]

61. Qiao, Y.F.; Miao, S.J.; Han, X.Z.; You, M.Y.; Zhu, X.; Horwath, W.R. The effect of fertilizer practices on N balance and global warming potential of maize–soybean–wheat rotations in Northeastern China. Field Crops Res.; 2014; 161, pp. 98-106. [DOI: https://dx.doi.org/10.1016/j.fcr.2014.03.005]

62. Nagy, J.; Dobos, A.; Sum, O.; Szabó, G. The effect of fertilization and soil cultivation on the yield of maize (Zea mays L.). Cereal Res. Commun.; 2000; 28, pp. 161-167. [DOI: https://dx.doi.org/10.1007/BF03543588]

63. Liang, A.Z.; Yang, X.M.; Zhang, X.P.; McLaughlin, N.; Shen, Y.; Li, W.F. Soil organic carbon changes in particle-size fractions following cultivation of Black soils in China. Soil Tillage Res.; 2009; 105, pp. 21-26. [DOI: https://dx.doi.org/10.1016/j.still.2009.05.002]

64. Han, S.J.; Wang, E.H.; Chen, X.W.; Fu, Y. Accumulation of SOM fractions to croplands and plantations converted from cropland with black soil. Land Degrad. Dev.; 2022; 33, pp. 638-648. [DOI: https://dx.doi.org/10.1002/ldr.4187]

65. Man, W.D.; Yu, H.; Li, L.; Liu, M.Y.; Mao, D.H.; Ren, C.Y.; Wang, Z.M.; Jia, M.M.; Miao, Z.H.; Lu, C.Y. Spatial expansion and soil organic carbon storage changes of croplands in the Sanjiang Plain, China. Sustainability; 2017; 9, 563. [DOI: https://dx.doi.org/10.3390/su9040563]

66. Liu, D.W.; Wang, Z.M.; Zhang, B.; Song, K.S.; Li, X.Y.; Li, J.P.; Li, F.; Duan, H.T. Spatial distribution of soil organic carbon and analysis of related factors in croplands of the black soil region, Northeast China. Agric. Ecosyst. Environ.; 2006; 113, pp. 73-81. [DOI: https://dx.doi.org/10.1016/j.agee.2005.09.006]

67. Schima, R.; Mollenhauer, H.; Grenzdörffer, G.; Merbach, I.; Lausch, A.; Dietrich, P.; Bumberger, J. Imagine all the plants: Evaluation of a light-field camera for on-site crop growth monitoring. Remote Sens.; 2016; 8, 823. [DOI: https://dx.doi.org/10.3390/rs8100823]

68. Ye, S.Q.; Song, C.Q.; Cheng, F.; Zhang, L.N.; Cheng, C.X.; Zhang, C.; Yang, J.Y.; Zhu, D.H. Cultivated land health-productivity comprehensive evaluation and its pilot evaluation in China. Trans. Chin. Soc. Agric. Eng.; 2019; 35, pp. 66-78.

69. Xiong, M.Q.; Sun, R.H.; Chen, L.D. A global comparison of soil erosion associated with land use and climate type. Geoderma; 2019; 343, pp. 31-39. [DOI: https://dx.doi.org/10.1016/j.geoderma.2019.02.013]

70. Huang, M.Q.; Li, Y.B.; Ran, C.H.; Li, M.Z. Dynamic changes and transitions of agricultural landscape patterns in mountainous areas: A case study from the hinterland of the Three Gorges Reservoir Area. J. Geogr. Sci.; 2022; 32, pp. 1039-1058. [DOI: https://dx.doi.org/10.1007/s11442-022-1984-7]

71. Tan, X.P.; Shan, Y.Q.; Wang, X.; Liu, R.P.; Yao, Y.L. Comparison of the predictive ability of spectral indices for commonly used species diversity indices and Hill numbers in wetlands. Ecol. Indic.; 2022; 142, 109233. [DOI: https://dx.doi.org/10.1016/j.ecolind.2022.109233]