PERTANIKA JOURNAL OF SCIENCE AND TECHNOLOGY

 

e-ISSN 2231-8526
ISSN 0128-7680

Home / Regular Issue / JST Vol. 34 (2) Apr. 2026 / JST-6125-2025

 

Review Article

Visualising Global Trends in Soil Infiltration Study by using Bibliometric Analysis Nor Farah Atiqah Ahmad, Siti Nooraiin Mohd Razali, Suhaila Sahat, and Muhammad Azraie Abdul Kadir

Pertanika Journal of Science & Technology, Volume 34, Issue 2, April 2026

DOI: https://doi.org/10.47836/pjst.34.2.17

Keywords: Bibliometric analysis, hydrology, research trends, scopus database, soil infiltration, VOSViewer

Published on: 2026-04-30

Soil infiltration plays a crucial role in the hydrological cycle, water resource management, and sustainable agricultural practices. It is important to identify the emerging trends and research gaps for this study. Therefore, this study aims to present a comprehensive and systematic academic review of soil infiltration in the field using bibliometric analysis with the aid of VOSviewer to evaluate global research trends in soil infiltration based on author keywords, affiliated countries, and co-authorships. This study retrieved 494 articles published between 2000 and 2025, as of January 16th, 2025, from the Scopus database. Results have shown a steady decrease and increased for annual number of publications, with the lowest drop in 2024 with 22 publications and the highest rise in 2021 with 30 publications. The leading contributors are from China and the USA, leading by 28% and 21% respectively. Meanwhile, Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering contributed the most publications. Although the Science of The Total Environment (STOTEN) journal ranked second after Water (Switzerland) in the top journal, the CiteScore 2023 for STOTEN is the highest with 17.6. This study underscores the effectiveness of a water management system, which includes it supports for flood risk mitigation, enhances agricultural productivity and improves groundwater recharge. Future research should focus on optimising the soil infiltration model by adapting AI tools to consider climate change factors