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Review

Knowledge Atlas of Cultivated Land Quality Evaluation Based on Web of Science Since the 21st Century (2000–2023)

by
Pingluo Xue
,
Chongyang Shen
*,
Huaizhi Tang
,
Yunjia Liu
and
Yuanfang Huang
College of Land Science and Technology, China Agricultural University, Beijing 100193, China
*
Author to whom correspondence should be addressed.
Land 2024, 13(10), 1697; https://doi.org/10.3390/land13101697
Submission received: 4 September 2024 / Revised: 7 October 2024 / Accepted: 10 October 2024 / Published: 17 October 2024
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Abstract

:
Cultivated land is the most important natural resource for human survival and development. The quality of cultivated land is closely related to grain output, and whether it can guarantee stable food supply is directly related to national food security. Cultivated land quality evaluation is an effective tool for understanding and mastering cultivated land quality. However, few studies have applied bibliometrics to quantitatively and systematically analyze this field. We used VOSviewer 1.6.19 and CiteSpace 6.3.1 software to visually analyze and construct 2478 documents related to cultivated land quality evaluation retrieved from the Web of Science core collection database from 2000 to 2023. Results show that cultivated land quality evaluation is still a popular research field. The collaboration ability among authors is weak and the distribution of institutions and countries publishing in this field is very uneven. In addition, the relevant research has been published in a variety of journals such as agriculture, environment, ecology, and computer technology. The research content is becoming more and more interdisciplinary. Keywords such as “Soil quality”, “Swat”, “Remote sensing”, “Heavy metals” and “Ecosystem services” have become hot topics in this field. In the future, it is necessary to further deepen the connotation of cultivated land quality, develop a long time series dynamic model of cultivated land quality evaluation and monitoring, and enhance the transformation of research results into practical applications.

1. Introduction

Since the beginning of the 21st century, people have experienced several global food crises, and food security is an important guarantee for world peace and development. As of 2020, 2.37 billion people in the world were insecure in their food supply and by 2021, that number had increased by almost 320 million, indicating that the world is facing a serious food crisis [1,2]. Cultivated land is crucial for the survival and development of human society, which plays a key role in ensuring national food security and maintaining social stability and sustainable development [3]. Scientific and accurate cultivated land quality evaluation is critical to the protection, management, and dynamic monitoring of cultivated land resources [4,5]. In this context, cultivated land quality evaluation, as an effective method to intuitively grasp the status of cultivated land quality, has received considerable attention by scholars all over the world. The number of related publications has increased rapidly.
Cultivated land quality evaluation is an important research field with continuous development at multiple levels and dimensions. Lu et al. (2006) reviewed and summarized the main methods of the fertility evaluation of cultivated land [6]. Bünemann et al. (2018) reviewed soil quality and related concepts from the perspective of definitions and evaluation and summarized common evaluation methods and indicators [7]. Diaz-Gonzalez et al. (2022) reviewed the application of machine learning and remote sensing to estimate soil indicators [8]. However, the traditional review method is limited and highly subjective [9]. Therefore, it is necessary to carry out a comprehensive and quantitative systematic review in the field of cultivated land quality evaluation.
Bibliometric analysis was proposed by Pritchard in 1969. It is a quantitative method to predict and identify hotspots and trends in the research field [10,11,12]. In addition, bibliometric analysis can conduct a visual analysis of a large number of documents with the help of relevant software. This method can not only grasp the development process of the field from a macro perspective, but also clarify the research hotspots from a micro perspective. It can make up for the shortcomings of the traditional review method, such as fewer references, only a qualitative summary of research content, and high subjectivity [13]. CiteSpace and VOSviewer are commonly used to conduct visual analyses of documents. CiteSpace (https://citespace.podia.com/, accessed on 28 March 2024) is a free bibliometrics visualization software tool, which was developed by Professor Chen Chaomei at Drexel University (USA) in 2004 [14]. Its outstanding strength is identifying research frontiers [15,16]. VOSviewer (https://www.vosviewer.com, accessed on 28 March 2024) is a free software tool based on Java, developed in 2009 by van Eck Nees Jan and Waltman Ludo at the Center for Scientific and Technological Studies (CWTS) of Leiden University in the Netherlands. It is used to build a visual network analysis of data [17]. The clustering in VOSviewer is calculated based on the strength of the association. Its visual mapping image is clear and beautiful. CiteSpace and VOSviewer’s official websites provide relevant reference books, manuals, and tutorials.
Therefore, this study aims to review the development of cultivated land quality evaluation from micro, meso, and macro perspectives by using a bibliometric analysis since the 21st century, explore the development process, and track the hotspots. Specifically, based on the Web of Science core collection database, VOSviewer 1.6.19 and CiteSpace 6.3.1 software were used to conduct visual analyses of documents on cultivated land quality evaluation. Considering the different advantages of VOSviewer 1.6.19 and CiteSpace 6.3.1, this study applied VOSviewer 1.6.19 to analyze the cooperation and co-citation networks, and the keywords were analyzed by CiteSpace. Our work is helpful for scholars to understand the research hotspots and scientific frontiers in the field of cultivated land quality evaluation in an all-round and multi-angle way, which provides theoretical reference for further development in this field.

2. Data Source and Methods

2.1. Data Source

In terms of the database selection, there is a relatively low quality of the metadata available in Google Scholar and difficulty in controlling it [18]. Martín-Martín (2021) analyzed subject categories’ relative overlap between Scopus and Web of Science (WoS), based on 2515 English language highly cited documents published in 2006 from 252 subject categories; at the time, Scopus found 93% of the citations found by WoS [19]. In addition, in natural sciences and engineering (NSE), WoS has more exclusive journals than Scopus [20]. Based on the discussion above, our study used datasets from WoS.
WoS is a high-quality digital database developed by Thomson Scientific that is highly recognized and widely used by researchers around the world [21,22]. WoS is also a suitable database because it contains a variety of data, such as titles, authors, institutions, countries, abstracts, keywords, references, citation counts, and impact factors [23,24]. In this study, the Science Citation Index Expanded (SCI-Expanded) and Social Sciences Citation Index (SSCI) from the WoS core dataset were selected as data sources for searching. Both core databases cover the period 1900 to the present. In this study, the starting time was set as “1 January 2000”, the ending time was set as “31 December 2023”, and the search began at 18:58 PM on 28 March 2024. The search details are shown in Table 1. These documents were exported from the WoS database in plain text file format with “Full Record and Cited References” for bibliometric analysis on the same day after the search was completed. CiteSpace software was used to redo the downloaded data.

2.2. Data Analysis

In this study, VOSviewer 1.6.19 and CiteSpace 6.3.1 software were used to mine the information of cultivated land quality evaluation documents, analyze the research theme and frontiers, and clarify the development trends in cultivated land quality evaluation [25]. Specifically, VOSviewer was applied to generate cooperation networks of authors, institutions, and countries, as well as co-citation networks of references and journals. CiteSpace was used for keywords’ co-occurrence, clustering, and timeline analysis.

3. Results and Analysis

3.1. Description of Publications

The number of publications each year can clearly and accurately reflect the development trend in the research field, which can elucidate the past research status and clarify the future development status [26]. As shown in Figure 1, the horizontal, left, and right vertical axes represent the publication year, the number of annual publications, and the percentage of annual publications, respectively.
According to the search results of this study, 2478 documents related to cultivated land quality evaluation were published between 2000 and 2023. It can be seen that the number of annual publications is generally increasing, which indicates that the research on cultivated land quality evaluation has gained attention gradually.
As shown in Figure 1, the number of publications has stabilized at more than 100 per year since 2014. The years 2022 and 2023 had the highest publication frequency, during which more than 300 publications were reported. The number of publications has grown explosively since 2020, which may be due to the impacts of extreme global agrometeorological disasters, great power gains, and frequent epidemics [27,28], so the uncertainties and risks of international food trade have increased [29,30]. Therefore, against this backdrop of unstable and uncertain natural, social, and economic risks, the global food security situation remains serious. So, countries from around the world are paying more attention to improving food production and ensuring their own food security. It can be seen that cultivated land quality evaluation is a popular research direction in recent years and has a good development trend in the future.

3.2. Cooperation Analysis of Authors, Institutions, and Countries

The analysis of cooperation network of authors, institutions, and countries can intuitively express the field of cultivated land quality evaluation from micro, meso, and macro perspectives, which is helpful to find possible cooperation opportunities between scholars, institutions, and countries [31]. The larger the node in the visualization view, the more the corresponding number of publications. The lines between different nodes indicate the strength of the cooperative relationship between nodes. Similarly, the number of citations reflects the influence.

3.2.1. Cooperation Network of Authors

VOSviewer 1.6.19 was used to visualize the authors’ cooperation network with more than three publications among the 11,014 authors in the study sample. A total of 326 authors met this condition.
Table 2 shows the top 10 authors publishing research during our search period, indicating that these authors have significant academic influence. We can explore the development trend in this field by understanding the publication status of representative scholars with high attention in this field. Lal R is the most influential author (with 16 publications and 733 citations). He mainly focused on soil quality, with a particular focus on soil carbon, such as soil carbon and climate change and food security [32], soil carbon sequestration and soil sustainable management [33]. Cherubin MR is the second most influential author, with 14 publications and 489 citations. His main focus was soil quality evaluation, with a greater focus on the impact of sugarcane expansion in Brazil on soil quality and sustainable management [34,35]. As the third most influential author, Karlen DL mainly focused on the effects of crop residue mulch on soil nutrients [36,37]. In addition, as can be seen from Figure 2, most authors have no cooperative relationship, and only a few authors are connected, indicating that the collaboration ability among authors is weak. This may be because most scholars prefer to cooperate with familiar peers or even colleagues in the same institution, so a denser cooperation network that can be extended to a larger group has not yet been formed. There is still a need to strengthen broad collaboration among scholars.

3.2.2. Cooperation Networks of Institutions and Countries

VOSviewer 1.6.19 was used to visualize the collaboration between 116 countries and 3008 institutions in the study sample. Institutions with 20 or more publications and countries with 10 or more publications were visualized in our study.
Table 3 shows the top 10 institutions publishing research during our search period. The Chinese Academy of Sciences, with 252 publications and 8508 citations, is ranked first in our study. The subsequent institutions are the Agricultural Research Service and the University of Chinese Academy of Sciences, with 111 and 88 publications and 4564 and 2212 citations, respectively. The top 10 institutions are all from China and the United States. As shown in Figure 3, the top 15 countries form five collaborative networks. The leading participating countries of the first cooperative group are China and the United States. Table 4 shows the top 15 countries publishing research during our search period. It can be seen that China is ranked first in our study (987 publications). The United States (544 publications), Germany (158 publications), the United Kingdom (122 publications), and Italy (106 publications) all have more than 100 publications. Most countries in the top 15 are located in Europe, followed by Asia. The Chinese Academy of Sciences and China are ranked first in the number of publications, which may be related to the great importance of the Chinese government. China is a populous country, ensuring food security is the premise of social stability. As the lifeblood of grain production, improving cultivated land quality is of great significance to enhance the productivity of cultivated land and ensure national food security.
In general, China and the United States have a higher influence and are in a leading position. The cooperation networks of institutions and countries are relatively weak, and the distribution of them publishing in this field is very uneven. The cross-institutional and cross-country cooperation groups have not yet formed. In the future, academic exchanges of more institutions and countries should be strengthened.

3.3. Analysis of Hotspots

Research hotspots refer to a group of research questions or topics concerned by researchers in a certain period of time [38]. The keyword is a noun or phrase that expresses the important content of the article [39] and is also the core of the article [40]. Therefore, the analysis of keywords can help scholars capture the current research hotspots and provide valuable guidance for scholars to carry out research in the future. In this study, CiteSpace was used to analyze the keywords’ co-occurrence, clustering, and timeline. The parameters of CiteSpace software were set as follows: the time slice was 1 year, the threshold was set to the top 50, and the k value in the g-index was 10. “Pathfinder” was selected to simplify the network and highlight the more important features. A total of 292 nodes and 1207 links were generated.

3.3.1. Keyword Co-Occurrence

Keywords with higher frequency means more popularity in this field. Table 5 shows the top 10 keywords during our searching period, which are quality (count: 366), management (count: 340), land use (count: 309), water quality (count: 247), indicators (count: 178), nitrogen (count: 177), model (count: 162), tillage (count: 155), ecosystem services (count: 148), and climate change (count: 147). Among them, “quality” is the expression of the research theme in this field, “model” indicates the relevant model tools that are used in the process of cultivated land quality evaluation, such as the SWAT model and random forest model. The keywords “management”, “land use”, “water quality”, “indicators”, “nitrogen”, “ecosystem services”, and “climate change” reflect the main research content and hot issues in the field of cultivated land quality evaluation. It shows that cultivated land quality is a comprehensive attribute of natural and social factors [41]. Its evaluation has mainly focused on cultivated land soil quality, management quality, ecological quality, and indicators selected for evaluation. In addition, in CiteSpace, nodes with a centrality of more than 0.1 are called “key nodes” [42]. Table 5 also summarizes keywords with a centrality of more than 0.1, namely system (centrality: 0.14), land use (centrality: 0.11), and area (centrality: 0.11), while only land use (0.11) has a centrality greater than 0.1 among the top 10 keywords.

3.3.2. Keyword Cluster

Based on the keyword co-occurrence analysis, we used the log–likelihood ratio (LLR) algorithm in CiteSpace to cluster keywords. It is generally believed that a clustering modularity value (Q value) > 0.3 means that the clustering structure is significant. Mean silhouette (S value) > 0.5 means that clustering is reasonable, and S > 0.7 means that clustering is convincing [43,44]. In this study, Q value = 0.3995 (greater than 0.3) and S value = 0.7269 (greater than 0.7) after clustering, indicating that the results are reasonable. We can see that the top five hotspots formed during the study period are as follows: “Soil quality”, “Swat”, “Remote sensing”, “Heavy metals”, and “Ecosystem services” (Figure 4). To gain further insight into the evolution of these clusters, we used CiteSpace to visualize the timeline view of the clusters (Figure 5).
Cluster 0 is “Soil quality”. The focus of research content has gradually shifted from the productivity of cultivated land in the early stage to a multi-dimensional evaluation including ecology and health status. Cluster 1 is “Swat”. This model is the most commonly used hydrological model, which can be applied to the simulation of non-point source pollution, the impact of future climate change, the impact of land use change, and the prediction and simulation of management measures. As one of the important factors affecting cultivated land quality, water resource is the most urgent problem that threatens food security, and it is very important to maintain grain yield in production practice; as a key environmental factor affecting cultivated land quality, it is an indispensable part of the evaluation factors [45,46]. In addition, improper agricultural production activities can also affect water quality in turn, such as the excessive use of N and P fertilizers, leading to the eutrophication of water, thus causing serious water pollution problems [47,48,49,50]. Therefore, cultivated land quality evaluation should not only pay attention to production quality and ecological quality, but also to the health and pollution problems caused by human activities, especially the impact of soil and water pollution on cultivated land quality.
Cluster 2 is “Remote sensing”, which is a common technical term in this field. It can help us better understand and carry out cultivated land quality evaluation from a macro perspective. With the deepening and refinement of the research content, people have put forward higher requirements for the timeliness, accuracy, and comprehensiveness of cultivated land quality evaluation results. Remote sensing technology can quickly identify the ground features in the construction and management of cultivated land quality. The technology can also be used to obtain the normalized difference vegetation index (NDVI), the normalized multi-band drought index (NMDI), the salinity index (SI), and other related remote sensing indicators that can reflect soil fertility, moisture, and soil salinity to carry out cultivated land quality evaluations [51,52,53,54,55]. At the same time, remote sensing can also be used to monitor cultivated land in all weather, with full coverage and multi-resolution and multi-scale capabilities [56,57,58,59].
With the improvement in global industrialization, the heavy metal pollution of cultivated land is becoming more and more serious, which not only affects people’s health, but also relates to the country’s food and ecological security [60]. Therefore, it is important to accurately understand and evaluate the pollution situation and find the source of pollution to control the cultivated land environment and realize green ecological agriculture. This study shows that the research on heavy metals has gradually shifted from point to area, and from focusing on the pollution level to focusing on health risk assessment. As newly emerging clusters, “Heavy metals” and “Ecosystem services” also reflect the shift in the focus of research on cultivated land quality evaluation to a focus on health and sustainability.
These keywords are interrelated rather than independent. For example, in the process of cultivated land quality evaluation, soil and water are usually matched. Indexes related to soil quality (e.g., soil texture, slope, and pH) and water resource environments (e.g., irrigation water quantity, mineralized degree of groundwater) were often widely used as evaluation indicators of cultivated land quality [61,62,63]. At the same time, in order to ensure food security, the intensity of agricultural activities will be further increased. The intensive use of cultivated land will lead to the emergence of pollution problems. Pollutants generated will be discharged through groundwater and rivers, which will lead to the deterioration of water quality and the degradation of cultivated land quality [64].

3.4. Co-Citation Analysis of References and Journals

Co-citation analysis means that when two references (or journals) are both cited by a third party, they are related to some extent, even if they are not directly cited by each other [65,66]. In this study, VOSviewer was used to analyze the reference and journal co-citation network. The references’ co-citation network provides insight into the connections between co-cited references, and the journals’ co-citation network tends to represent a collective knowledge base of a given area of knowledge [67].

3.4.1. Reference Co-Citation Network

VOSviewer 1.6.19 was used to visualize the references’ co-citation network. The citation threshold was set to 22, and 96 references met the condition. Table 6 lists the 10 most cited references. It can be seen that the top five references were cited more than 105 times. Arnold JG, 1998 (126 citations) is ranked first, followed by Andrews SS, 2002 (121 citations), Andrews SS, 2004 (109 citations), Karlen DL, 1997 (107 citations), and Nash JE, 1970 (106 citations). Among them, Arnold JG’s (1998) article entitled “Large area hydrologic modeling and assessment part I: model development” described the use of the SWAT model in detail, which serves as a guide to the use of the SWAT model [68]. Andrews SS (2002) performed a systematic and comprehensive comparison of soil quality indexing methods for vegetable production systems in Northern California, including expert opinions and PCA in the selection of the minimum dataset, linear and non-linear scoring in the transformation of indicators, and additive, weighted additive, and decision support systems in the integration of transformation indicators [69]. Due to the different scope of applications of different evaluation methods, several methods are often compared for the same region to obtain an appropriate evaluation method that is beneficial to the region. This paper provides a reference for further research by other scholars. Andrews SS (2004) proposed the soil management assessment framework (SMAF) for the first time [70]. Karlen DL’s work (1997), entitled “Soil Quality: A Concept, Definition, and Framework for Evaluation”, is a classic article on the definition of soil quality [71]. Nash JE’s work (1970), entitled “River flow forecasting through conceptual models part I—A discussion of principles”, discussed the application of the conceptual model technique to river flow forecasting. The necessity for a systematic approach to the development and testing of the model was explained and some preliminary ideas were presented, which had important contributions to the accurate measurement of river discharge [72]. The accurate prediction of river flow can help us improve agricultural production infrastructure according to local conditions and effectively prevent flood disasters, such as irrigation methods and drainage conditions, which are important indicators in the cultivated land quality evaluation [73]. It can be seen that although the references are published in journals with a relatively low five-year impact factor, they are still frequently cited in this field. Therefore, through the analysis of references’ co-citation network, we can avoid the situation of ignoring some highly cited literature due to excessive attention to the five-year impact factor. In addition, it can also help us quickly understand the research basis of this field, which helps us to track the follow-up research of relevant scholars and conduct further exploration according to their interests.

3.4.2. Journal Co-Citation Network

VOSviewer 1.6.19 was used to visualize the journals’ co-citation network. The citation threshold was set to 200, and 106 journals met the condition. A network was created, which consisted of five clusters including soil environment, soil ecology, soil water, remote sensing, and management policy (Figure 6).
The soil environment cluster includes Sci Total Environ, Environ Pollut, Chemosphere, and other journals. The soil ecology cluster includes Soil Till Res, Agr Ecosyst Environ, Land Degrad Dev, and other journals. The soil water cluster includes J Hydrol, J Environ Qual, J Soil Water Conserv, and other journals. The remote sensing cluster includes Remote Sens Environ, Remote Sens-Basel, Int J Remote Sens, and other journals. The management policy cluster includes Land Use Policy, J Environ Manage, J Clean Prod, and other journals. It can be seen that the evaluation of cultivated land quality is not limited to a single evaluation, but has been involved in a multi-field research. The analysis of the number of co-citation journals can help scholars establish a theoretical foundation, understand the influential literature sources in the field, and have a basic cognition of the main categories accepted by each journal. Through this basic cognition, authors can find the target journals according to the needs of submission and submit high-quality articles.

4. Conclusions

In this study, with the help of the different advantages of VOSviewer 1.6.19 and CiteSpace 6.3.1, 2478 documents obtained with cultivated land quality evaluation as the research topic since the 21st century retrieved from the SCI and SSCI core databases were visually analyzed. In this study, authors, institutions, countries, references, and journals were discussed. The current research hotspots and evolution trends in this research field were also revealed. First of all, from the number of annual publications, since the beginning of the new century, the research field of cultivated land quality evaluation has attracted increasing attention worldwide. Particularly, since 2020, the number of publications has shown an explosive growth, indicating that the current research in this field is in a prosperous period. Second, for the cooperation networks, collaboration among institutions and countries are more frequent than among authors, but the collaboration ability between institutions and countries is still weak, and a larger and more intensive cooperation group across institutions and countries has not been formed. Third, for the co-citation networks, the references’ co-citation network can show the classic references in this field, which can help scholars to grasp the foundation of the field quickly and track the follow-up research of relevant authors. The journals’ co-citation network indicates that cultivated land quality evaluation covers many fields such as agriculture, environment, ecology, and computer technology. At the same time, it can also help scholars quickly find popular journals in this field and understand the categories of related journals, which is conducive to helping scholars determine target journals in the submission process and match periodical needs according to their own research content. In addition, with the continuous development of society, the research content in this field has become more and more specific and diversified and has gradually expanded from the single evaluation of cultivated land production to the comprehensive evaluation of cultivated land production, ecology, health, and sustainable development. The number of hot topics in research is also increasing. Among them, “Soil quality”, “Swat”, “Remote sensing”, “Heavy metals”, and “Ecosystem services” have become five frontiers in the field of cultivated land quality evaluation.
Through the analysis of documents on cultivated land quality evaluation, this study shows that the research content in this field has been developed to different degrees in both breadth and depth, and future work should pay attention to interdisciplinary research and multi-technology cross-border integration. In order to further improve and develop this field, the following suggestions are put forward. First, the connotation of cultivated land quality should be further deepened and improved. Considering the needs of the current era of green and sustainable development of the trinity of quantity, quality, and ecology, the connotation of cultivated land quality is further improved from the perspectives of soil science, ecological science, environmental science, and other disciplines so as to help scholars strengthen their understanding of the relationship between cultivated land quality and various elements in the new era and carry out cultivated land quality evaluation in the new era more comprehensively and accurately. Second, we should carry out long-term cultivated land quality evaluations and monitoring. Traditional sampling method for large-area sampling requires a lot of manpower and material resources, and it is difficult to monitor the cultivated land quality dynamically in a long time series. Remote sensing technology provides a new way for rapid and large-area data acquisition. In the future, more remote sensing technology and geographic information systems can be combined in order to improve the timeliness of cultivated land quality evaluation results and promote the development of cultivated land quality evaluation towards a dynamic direction. In addition, it can also combine artificial intelligence with big data to build a public network monitoring system. Third, the transformation of research results into practical applications should be strengthened. Although there are many studies on cultivated land quality evaluation, there are few studies on how to transform cultivated land quality evaluation results into actual production and living indicators, such as the transformation between evaluation results and economic benefits. In the future, we should pay attention to the research on the value transformation between evaluation results and practical applications so as to better explore the relationship between cultivated land quality, ecology, and economic development.

Author Contributions

Writing—original draft preparation, P.X.; writing—review and editing, C.S.; methodology and research ideas, C.S., H.T., Y.L. and Y.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Key Research and Development Program of China (grant no. 2021YFD1500201).

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflicts of interest.

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Land 13 01697 g001
Figure 1. Number of publications each year from 2000 to 2023.
Figure 1. Number of publications each year from 2000 to 2023.
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Figure 2. Authors’ cooperation network visualization.
Figure 2. Authors’ cooperation network visualization.
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Figure 3. Countries’ cooperation network visualization.
Figure 3. Countries’ cooperation network visualization.
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Figure 4. Visualization of keyword cluster analysis.
Figure 4. Visualization of keyword cluster analysis.
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Figure 5. Visualization of keyword timeline.
Figure 5. Visualization of keyword timeline.
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Figure 6. Journals’ co-citation network visualization.
Figure 6. Journals’ co-citation network visualization.
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Table 1. Search details.
Table 1. Search details.
Search Name Search Rule
Citation indexesSCI-Expanded, SSCI
Searching period1 January 2000 to 31 December 2023
Searching topicCultivated land quality assessment OR Cultivated land quality evaluation OR Arable land quality assessment OR Arable land quality evaluation OR Farmland quality assessment OR Farmland quality evaluation OR Cropland quality evaluation OR Cropland quality assessment OR Tillage land quality evaluation OR Tillage land quality assessment
Document typesArticle or review
LanguageEnglish
Sample size2478
Table 2. Information on top 10 authors in terms of number of publications.
Table 2. Information on top 10 authors in terms of number of publications.
RankingAuthorDocumentsCitations
1Lal R16733
2Cherubin MR14489
3Karlen DL13642
4Arnold JG12708
5White MJ12335
6Mccarty GW10183
7Lee S9127
8Shen ZY9353
9Srinivasan R9317
10Zhao R9435
Table 3. Information on top 10 institutions in terms of number of publications.
Table 3. Information on top 10 institutions in terms of number of publications.
RankingInstitutionDocumentsCitations
1Chinese Academy of Sciences2528508
2Agricultural Research Service1114564
3University of Chinese Academy of Sciences882212
4Beijing Normal University641905
5China Agricultural University631727
6Northwest A&F University41772
7Chinese Academy of Agricultural Sciences351039
8Zhejiang University301010
9Nanjing University291002
10China University of Geosciences29576
Table 4. Information on top 15 countries in terms of number of publications.
Table 4. Information on top 15 countries in terms of number of publications.
RankingCountryDocumentsCitations
1China98722,176
2United States54421,644
3Germany1585313
4United Kingdom1226639
5Italy1064581
6Netherlands873792
7Brazil852101
8Canada791966
9India761643
10France722921
11Spain721755
12Iran561255
13Switzerland522712
14Japan511972
15Poland50943
Table 5. Information on top 10 keywords’ co-occurrence.
Table 5. Information on top 10 keywords’ co-occurrence.
RankingKeywordCountRankingKeywordCentrality
1quality3661systems0.14
2management3402land use0.11
3land use3093area0.11
4water quality247
5indicators178
6nitrogen177
7model162
8tillage155
9ecosystem services148
10climate change147
Table 6. Information on top 10 references in terms of citations.
Table 6. Information on top 10 references in terms of citations.
RankingReferenceCitationsJournalFive Year Impact Factor
1Arnold JG, 1998 [68]126J. Am. Water Resour. Assoc.2.9
2Andrews SS, 2002 [69]121Agr. Ecosyst. Environ.6.4
3Andrews SS, 2004 [70]109Soil Sci. Soc. Am. J.2.8
4Karlen DL, 1997 [71]107Soil Sci. Soc. Am. J.2.8
5Nash JE, 1970 [72]106J. Hydrol.6.4
6Moriasi DN, 2007 [74]100T. Asabe1.5
7Steffan-Dewenter I, 2002 [75]76Ecology5.5
8Bünemann EK, 2018 [7]75Soil Biol. Biochem.10.4
9Doran JW, 1994 [76]73SSSA Special Publications
10Gassman PW, 2007 [77]68T. Asabe1.5
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Xue, P.; Shen, C.; Tang, H.; Liu, Y.; Huang, Y. Knowledge Atlas of Cultivated Land Quality Evaluation Based on Web of Science Since the 21st Century (2000–2023). Land 2024, 13, 1697. https://doi.org/10.3390/land13101697

AMA Style

Xue P, Shen C, Tang H, Liu Y, Huang Y. Knowledge Atlas of Cultivated Land Quality Evaluation Based on Web of Science Since the 21st Century (2000–2023). Land. 2024; 13(10):1697. https://doi.org/10.3390/land13101697

Chicago/Turabian Style

Xue, Pingluo, Chongyang Shen, Huaizhi Tang, Yunjia Liu, and Yuanfang Huang. 2024. "Knowledge Atlas of Cultivated Land Quality Evaluation Based on Web of Science Since the 21st Century (2000–2023)" Land 13, no. 10: 1697. https://doi.org/10.3390/land13101697

APA Style

Xue, P., Shen, C., Tang, H., Liu, Y., & Huang, Y. (2024). Knowledge Atlas of Cultivated Land Quality Evaluation Based on Web of Science Since the 21st Century (2000–2023). Land, 13(10), 1697. https://doi.org/10.3390/land13101697

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