A Bibliometric and Thematic Approach to Digital Agriculture Research

Year 2025, Volume: 2 Issue: 1, 1 - 10, 14.07.2025

Ahmet Ayaz , Rukiye Ayaz

Abstract

This study presents a bibliometric analysis aiming to identify research trends, main themes and strategic issues in the field of digital agriculture from 2019 to 2023. The number of publications, author productivity, citations and keyword trends were assessed by analyzing 508 publications obtained from the Scopus database with Bibliometrix software. The findings show a notable increase in academic output around the main themes of data governance, agricultural data and data access. This study contributes to the field by systematically mapping existing knowledge and research gaps and provides valuable insights for future research. Prominent findings include the integration of technological innovations in agriculture and their impact on productivity and sustainability. The study recommends the development of technological infrastructure and the provision of farmer training programs to promote the effective adoption of digital agricultural technologies.

Keywords

Digital agriculture, Bibliometric analysis, Thematic analysis

Thanks

This study was presented as an oral presentation at the 11th International Management Information Systems Conference, October 24-26, 2024, Konya Food and Agriculture University, Konya, Türkiye.

Dijital Tarım Araştırmalarına Bibliyometrik ve Tematik Bir Yaklaşım

Abstract

Bu çalışma, 2019-2023 yılları arasında dijital tarım alanındaki araştırma eğilimlerini, ana temaları ve stratejik konuları belirlemeyi amaçlayan bibliyometrik bir analiz sunmaktadır. Bibliometrix yazılımı ile Scopus veri tabanından elde edilen 508 yayın analiz edilerek yayın sayısı, yazar üretkenliği, atıflar ve anahtar kelime eğilimleri değerlendirilmiştir. Bulgular, veri yönetişimi, tarımsal veri ve veri erişimi ana temaları etrafında akademik çıktılarda kayda değer bir artış olduğunu göstermektedir. Bu çalışma, mevcut bilgi ve araştırma boşluklarını sistematik olarak haritalandırarak alana katkıda bulunmakta ve gelecekteki araştırmalar için değerli içgörüler sağlamaktadır. Öne çıkan bulgular arasında teknolojik yeniliklerin tarıma entegrasyonu ve bunların verimlilik ve sürdürülebilirlik üzerindeki etkileri yer almaktadır. Çalışma, dijital tarım teknolojilerinin etkili bir şekilde benimsenmesini teşvik etmek için teknolojik altyapının geliştirilmesini ve çiftçi eğitim programlarının sağlanmasını önermektedir.

Keywords

Dijital tarım, Bibliyometrik analiz, Tematik analiz

References

• Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
• Aria, M., Misuraca, M., & Spano, M. (2020). Mapping the evolution of social research and data science on 30 years of Social Indicators Research. Social Indicators Research. https://doi.org/10.1007/s11205-020-02281-3
• Bertoglio, R., Corbo, C., Renga, F. M., & Matteucci, M. (2021). The digital agricultural revolution: A bibliometric analysis literature review. IEEE Access, 9, 108658–108674. https://doi.org/10.1109/ACCESS.2021.3101211
• Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the fuzzy sets theory field. Journal of Informetrics, 5(1), 146–166. https://doi.org/10.1016/j.joi.2010.10.002
• Cobo, M. J., Martínez, M. A., Gutiérrez-Salcedo, M., Fujita, H., & Herrera-Viedma, E. (2015). 25 years at Knowledge-Based Systems: A bibliometric analysis. Knowledge-Based Systems, 80, 3–13. https://doi.org/10.1016/j.knosys.2014.12.035
• Harzing, A.-W., & Alakangas, S. (2016). Google Scholar, Scopus and the Web of Science: A longitudinal and cross-disciplinary comparison. Scientometrics, 106(2), 787–804. https://doi.org/10.1007/s11192-015-1798-9
• Hasan, A. S. M. M., Sohel, F., Diepeveen, D., Laga, H., & Jones, M. G. K. (2021). A survey of deep learning techniques for weed detection from images. Computers and Electronics in Agriculture, 184, 106067. https://doi.org/10.1016/j.compag.2021.106067
• Jin, R., Gao, S., Cheshmehzangi, A., & Aboagye-Nimo, E. (2018). A holistic review of off-site construction literature published between 2008 and 2018. Journal of Cleaner Production, 202, 1202–1219. https://doi.org/10.1016/j.jclepro.2018.08.195
• Klerkx, L., Jakku, E., & Labarthe, P. (2019). A review of social science on digital agriculture, smart farming and Agriculture 4.0: New contributions and a future research agenda. NJAS - Wageningen Journal of Life Sciences, 90–91, 100315. https://doi.org/10.1016/j.njas.2019.100315
• Klerkx, L., & Rose, D. (2020). Dealing with the game-changing technologies of Agriculture 4.0: How do we manage diversity and responsibility in food system transition pathways? Global Food Security, 24, 100347. https://doi.org/10.1016/j.gfs.2019.100347
• Mongeon, P., & Paul-Hus, A. (2016). The journal coverage of Web of Science and Scopus: A comparative analysis. Scientometrics, 106, 213–228. https://doi.org/10.1007/s11192-015-1765-5
• Moral-Muñoz, J. A., Herrera-Viedma, E., Santisteban-Espejo, A., & Cobo, M. J. (2020). Software tools for conducting bibliometric analysis in science: An up-to-date review. El Profesional de la Información, 29(1), e290103. https://doi.org/10.3145/epi.2020.ene.03
• Rachmawati, R. R. (2021). Smart farming 4.0 untuk mewujudkan pertanian Indonesia maju, mandiri, dan modern. Forum Penelitian Agro Ekonomi, 38(2), 137–154. https://doi.org/10.21082/fae.v38n2.2020.137-154
• Singh, G., Kalra, N., Yadav, N., Sharma, A., & Saini, M. (2022). Smart agriculture: A review. Siberian Journal of Life Sciences and Agriculture, 14(5), 218–229.
• Soheyb, A., Abdelmoutia, T., & Labib, T. S. (2021). Toward Agriculture 4.0: Smart farming environment based on robotic and IoT. In 2021 4th International Symposium on Advanced Electrical and Communication Technologies (ISAECT) (pp. 1–6). IEEE. https://doi.org/10.1109/ISAECT53676.2021.9620095
• Sott, M. K., Nascimento, L. S., Foguesatto, C. R., Furstenau, L. B., Faccin, K., Zawislak, P. A., Mellado, B., Kong, J. D., &
• Bragazzi, N. L. (2021). A bibliometric network analysis of recent publications on digital agriculture to depict strategic themes and evolution structure. Sensors, 21(16), 5281. https://doi.org/10.3390/s21165281
• Tjhin, V. U., & Riantini, R. E. (2022). Smart farming: Implementation of Industry 4.0 in the agricultural sector. In Proceedings of the 2022 International Conference on Computer Science and Artificial Intelligence (CSAI '22) (pp. 108–112). ACM. https://doi.org/10.1145/3576642.3576649
• Zhou, R., & Yin, Y. (2023). Digital agriculture: Mapping knowledge structure and trends. IEEE Access, 11, 30612–30630. https://doi.org/10.1109/ACCESS.2023.3252032
• Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429–472. https://doi.org/10.1177/1094428114562629