Thematic Mapping Of Agricultural And Rural Research In Dagestan

Please cite this study if you use it in your research as follows:

Duman, Hakan. 2024. “Thematic Mapping Of Agricultural And Rural Research In Dagestan.” In Kafkasya Araştırmaları - II, edited by Oğuz Şimşek, Çağrı Akgün, and Çetin İzgi, 99–126. Ankara: Sonçağ Akademi.

Full text of the study

Abstract

This study uses bibliometric methods, including thematic mapping, to analyze agricultural and rural research on Dagestan, based on 90 studies from the Web of Science database. The analysis reveals two key periods: 1985–2016, marked by limited thematic diversity focused on “Islam” and “modernity,” and 2017–2023, characterized by thematic expansion into areas such as agricultural practices, environmental sustainability, and public health. Research activity surged post-2017, with a peak in 2018.

Key contributors, such as Dogeev GD and Gunya A, and journals like South of Russia: Ecology Development and Arid Ecosystems, are highlighted. Recurring themes include health behaviors, disease transmission, and agricultural productivity, reflecting the region’s unique ecological and social contexts. However, the lack of studies employing advanced methodologies like artificial intelligence underscores a critical research gap.

This work emphasizes Dagestan’s growing role in agricultural research, providing a roadmap for innovative solutions to regional challenges, fostering food security, and promoting environmental sustainability.

Keywords: R Programming, Bibliometrics, Caucasus, Dagestan, Agriculture

Note: The bibliometric data was deleted due to property rights of the data. The data can be accessed on Web of Science using the filter provided below.

WOS filter: “(dagestan OR daghestan) AND (agriculture OR farming OR agribusiness OR rural OR countryside OR farmstead OR homestead OR horticulture OR crop OR livestock)”

R Packages

This study used the R statistical environment, version 4.2.2, developed by R Core Team (2022). The tidyverse meta-package (version 2.0.0) which consists of the dplyr (Wickham, François, Henry, Müller, & Vaughan, 2023), ggplot2 (Wickham, 2016), readr (Wickham, Hester, & Bryan, 2024) and tidyr (Wickham, Vaughan, & Girlich, 2024) packages, created by Wickham et al. (2019), was used for data visualization, manipulation and cleaning. General plot attributes were set using the ggthemes package (version 4.2.4) (Arnold, 2021). ‘Bibliometrix’ (version 4.3.0) is used for bibliometric analysis (Aria & Cuccurullo, 2017), and the clipr package (version 0.8.0) was used for clipboard data operations (Lincoln, 2022).

Acknowledgements

This analysis adapted and modified code from various sources, such as books, package manuals, vignettes, and GitHub repositories. The sources that mainly utilized are cited as follows:

• Data preparing, manipulation, cleaning, and visualization: Wickham et al. (2019), Wang, Cook, & Hyndman (2020), Wang & contibutors (2024), Wickham & contibutors (2024), Wickham (2016), Wickham, Hester, et al. (2024), Wickham, Vaughan, et al. (2024)

• Conducting bibliometric analysis: Aria & Cuccurullo (2017), Aria et al. (2024)

Code References

Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An r-tool for comprehensive science mapping analysis. Journal of Informetrics. https://doi.org/10.1016/j.joi.2017.08.007

Aria, M., Cuccurullo, C., Misuraca, M., Spano, M., Belfiore, A., D’Aniello, L., & Gnasso, A. (2024). Bibliometrix: Homepage and tutorials. June 10, 2024, https://www.bibliometrix.org/home/

Arnold, J. B. (2021). Ggthemes: Extra themes, scales and geoms for ’ggplot2’. https://CRAN.R-project.org/package=ggthemes R package version 4.2.4

Lincoln, M. (2022). Clipr: Read and write from the system clipboard. https://CRAN.R-project.org/package=clipr R package version 0.8.0

R Core Team. (2022). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/

Wang, E., & contibutors. (2024). tidyverts/tsibble. June 2, 2025, https://github.com/tidyverts/tsibble

Wang, E., Cook, D., & Hyndman, R. J. (2020). A new tidy data structure to support exploration and modeling of temporal data. Journal of Computational and Graphical Statistics, 29(3), 466–478. https://doi.org/10.1080/10618600.2019.1695624

Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. https://ggplot2.tidyverse.org

Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L. D., François, R., … Yutani, H. (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43), 1686. https://doi.org/10.21105/joss.01686

Wickham, H., & contibutors. (2024). tidyverse/ggplot2. June 2, 2025, https://github.com/tidyverse/ggplot2

Wickham, H., François, R., Henry, L., Müller, K., & Vaughan, D. (2023). dplyr: A Grammar of Data Manipulation. https://dplyr.tidyverse.org R package version 1.1.4, https://github.com/tidyverse/dplyr

Wickham, H., Hester, J., & Bryan, J. (2024). readr: Read Rectangular Text Data. https://readr.tidyverse.org R package version 2.1.5, https://github.com/tidyverse/readr

Wickham, H., Vaughan, D., & Girlich, M. (2024). tidyr: Tidy Messy Data. https://tidyr.tidyverse.org R package version 1.3.1, https://github.com/tidyverse/tidyr