Van Yüzüncü Yıl University, Faculty of Agriculture , Department of Soil Science and Plant Nutrition, Van, Türkiye
Abstract
In scientific investigations worldwide, bibliometric analysis is frequently employed to examine the processes of change. In this regard, bibliometric analysis is crucial for identifying research trends. This study presents an extensive bibliometric analysis of publications in the field of soil survey and mapping. The Web of Science Core Collection database provided the study's data, which covered the years 2000 through 2025. In order to assess publishing trends, citation structures, international partnerships, and keyword patterns, 4,688 publications were examined using bibliometric approaches and VOSviewer software. The results revealed a remarkable increase in scientific output, with annual publications rising from 54 in 2000 to 373 in 2025, representing nearly a sevenfold increase. The retrieved publications accumulated 141,051 citations, demonstrating the growing scientific influence of the field. The findings showed a marked rise in citation counts and publication output over time, suggesting an increase in the field's level of scientific interest. The most significant nations in terms of productivity and citation effect were found to be the United States, China, and Australia. Digital soil mapping, machine learning, and remote sensing are the most popular study subjects, according to keyword analysis. The results show a distinct shift from conventional soil survey techniques to computational and data-driven methodologies.
Keywords
Digital soil mapping,Bibliometric analysis,Soil survey,Machine learning,Remote sensing
How to Cite
KARACA, S., & AYDIN, M. (2026). Bibliometric Analysis of Studies Conducted in the Field of Soil Survey and Mapping. MAS Journal of Applied Sciences, 11(3), 687–702. https://doi.org/10.5281/zenodo.21932708
📄Akbaş, F., Yıldız, H., 2004. Use of geostatistical techniques in mapping soil properties. 3rd Geographic Information Systems Informatics Days, 6-9 October 2004, Istanbul, Türkiye, pp. 1-10.
📄Aria, M., Cuccurullo, C., 2017. bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4): 959-975.
📄Birkle, C., Pendlebury, D.A., Schnell, J., Adams, J., 2020. Web of Science as a data source for research on scientific and scholarly activity. Quantitative Science Studies, 1(1): 363-376.
📄Chen, C., 2006. CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3): 359-377.
📄Chen, S., Arrouays, D., Mulder, V.L., Poggio, L., Minasny, B., Roudier, P., Libohova, Z., Lagacherie, P., Shi, Z., Hannam, J., Meersmans, J., Richer-de-Forges, A.C., Walter, C., 2022. Digital mapping of GlobalSoilMap soil properties at a broad scale: A review. Geoderma, 409: 115567.
📄Çağlar, K.Ö., 1958. Soil science. Ankara University Faculty of Agriculture Publications, No: 10, Ankara, Türkiye.
📄Çomaklı, E., 2021. Bibliometric analysis of studies on soil organic carbon and soil organic carbon stocks. European Journal of Science and Technology, 25: 517-524.
📄Çullu, M.A., 2012. The necessity of soil survey, mapping and soil management. Journal of Soil Science and Plant Nutrition, 1(1): 23-25.
📄Dengiz, O., Bayramin, İ., 2003. Evaluation of Ankara Gölbaşı soils according to different soil classification systems. Journal of Harran University Faculty of Agriculture, 7: 61-68.
📄Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., Lim, W.M., 2021. How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133: 285-296.
📄FAO, 2018. The future of food and agriculture: Alternative pathways to 2050. Food and Agriculture Organization of the United Nations, Rome, Italy.
📄Güler, E., Doğan, A., Orhan, C., 2016. Soil information system in republic of Turkey ministry of forestry and water affairs. EuroSoil International Congress, Conference Proceedings Book, 17–22 July, İstanbul, Türkiye, p:105.
📄Haunschild, R., Bornmann, L., Marx, W., 2016. Climate change research in view of bibliometrics. PlosOne, 11(7): e0160393.
📄Hengl, T., Heuvelink, G.B.M., Stein, A., 2004. A generic framework for spatial prediction of soil variables based on regression-kriging. Geoderma, 120(1-2): 75-93.
📄Hengl, T., Mendes de Jesus, J., MacMillan, R.A., Batjes, N.H., Heuvelink, G.B.M., Ribeiro, E., Samuel-Rosa, A., Kempen, B., Leenaars, J.G.B., Walsh, M.G., Gonzalez, M.R., 2014. SoilGrids1km–global soil information based on automated mapping. PlosOne, 9(8): e105992.
📄Hengl, T., Heuvelink, G.B.M., Kempen, B., Leenaars, J.G.B., Walsh, M.G., Shepherd, K.D., Sila, A., MacMillan, R.A., Mendes de Jesus, J., Tamene, L., Tondoh, J.E., 2015. Mapping soil properties of Africa at 250 m resolution: Random forests significantly improve current predictions. PlosOne, 10(6): e0125814.
📄Kurşun, G., Dengiz, O., 2018. Classification and distribution of soils formed under arid continental ecosystem conditions. Journal of Soil and Water, 7(2): 1-11.
📄Li, X.D., Poon, C.S., Liu, P.S., 2001. Heavy metal contamination of urban soils and street dusts in Hong Kong. Applied Geochemistry, 16(11-12): 1361-1368.
📄McBratney, A.B., Mendonça Santos, M.L., Minasny, B., 2003. On digital soil mapping. Geoderma, 117(1-2): 3-52.
📄Minasny, B., McBratney, A.B., 2006. A conditioned Latin hypercube method for sampling in the presence of ancillary information. Computers & Geosciences, 32(9): 1378-1388.
📄Minasny, B., McBratney, A.B., 2016. Digital soil mapping: A brief history and some lessons. Geoderma, 264: 301-311.
📄Nardi, P., Di Matteo, G., Palahi, M., Mugnozza, G.S., 2016. Structure and evolution of Mediterranean forest research: A science mapping approach. PlosOne, 11(5): 1-20.
📄Poggio, L., de Sousa, L.M., Batjes, N.H., Heuvelink, G.B.M., Kempen, B., Ribeiro, E., Rossiter, D., 2021. SoilGrids 2.0: Producing soil information for the globe with quantified spatial uncertainty. Soil, 7(1): 217-240.
📄Pranckutė, R., 2021. Web of Science (WoS) and Scopus: The titans of bibliographic information in today's academic world. Publications, 9(1): 12.
📄Putri, C.R., Soleh, S.M., Saregar, A., Anugrah, A., Susilowati, N.E., 2021. Bibliometric analysis: Augmented reality-based physics laboratory with VOSviewer software. Journal of Physics: Conference Series, 1796(1): 012056.
📄Radoglou-Grammatikis, P., Sarigiannidis, P., Lagkas, T., Moscholios, I., 2020. A compilation of UAV applications for precision agriculture. Computer Networks, 172: 107148.
📄Stenberg, B., Viscarra Rossel, R.A., Mouazen, A.M., Wetterlind, J., 2010. Visible and near infrared spectroscopy in soil science. Advances in Agronomy, 107: 163-215.
📄Van Eck, N.J., Waltman, L., 2010. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2): 523-538.
📄Wadoux, A.M.J.C., Minasny, B., McBratney, A.B., 2020. Machine learning for digital soil mapping: Applications, challenges and suggested solutions. Earth-Science Reviews, 210: 103359.
📄Were, K., Bui, D.T., Dick, Ø.B., Singh, B.R., 2015. A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape. Ecological Indicators, 52: 394-403.
📄Zhang, H., Liu, X., Yi, J., Yang, X., Wu, T., He, Y., Tian, P., 2020. Bibliometric analysis of research on soil water from 1934 to 2019. Water, 12(6): 1631.