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Drawing a scientific map of the drivers of agricultural land use change with a scientometric approach
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Freshteh Avatefi Akmal    , Yaser Mohammadi * |
| Department of Agricultural Extension and Education, Faculty of Agriculture, Bu-Ali Sina University, Hamadan, Iran , y.mohammadi@basu.ac.ir |
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Abstract: (157 Views) |
Background and aim: This study was conducted to analyze the scientometrics of agricultural land use changes between 2000 and 2024 and aims to identify the main drivers of these changes.
Materials and methods: To draw the scientific map, a total of 1801 publications from the Scopus database, covering the years 2006 to 2024, were analyzed using bibliometric techniques, including co-authorship networks, keyword co-occurrence mapping, citation impact analysis, and trend evaluation. Statistical analyses were conducted using VOSviewer, and correlation testing and exploratory factor analysis were performed using IBM SPSS version 28.
Findings: The results of the study showed that attention to the field of agricultural land use change has increased sharply in recent years, especially since 2015, and countries such as China and the United States have the most published articles in this field. Keyword network analysis identified key terms related to agricultural land use change, including environmental factors, human impacts, and technological methodologies. Based on the results of the correlation test, a positive and significant relationship was observed between the number of published articles and the year of publication at one percent level (P<0.01). Factor analysis identified five critical drivers—economic, social, environmental, spatial, and legal—impacting agricultural land use changes, with economic and environmental factors exerting the most influence.
Conclusion: Economic, social, legal, environmental, and physical drivers influence land use change, and the role of economic and environmental drivers is particularly prominent. Research has moved toward advanced techniques such as remote sensing and artificial intelligence algorithms that can help policymakers identify and predict land use change patterns. |
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| Keywords: Scientific mapping, Agricultural land use change, Drivers, Scientometric |
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Full-Text [PDF 1204 kb]
(56 Downloads)
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Type of Study: Orginal |
Subject:
Scientometrics
Received: 2024/05/15 | Revised: 2025/05/6 | Accepted: 2025/05/22 | ePublished: 2025/05/22
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