A scientometric analysis of agricultural pollution by using bibliometric software VoSViewer and Histcite™

Abstract

While modern agriculture brings more food to people, it causes environmental pollution as well. Agricultural pollution has attracted extensive public attention. A lot of reviews on agricultural research were conducted from different research aspects, but there is a lack of work on analyzing the research trend from large volumes of publications in the field of agricultural pollution. In the present work, a scientometric analysis of agricultural pollution was conducted to fill the gap by using the software of VoSviewer and HistCite™. The datasets are collected from the core database of Web of Science from 1991 to 2019, totally 1338 records on the topic of agricultural pollutions. In most years (1996, 1999, 2002, 2006, 2009, 2011, and 2013), the total local citation score (TLCS) and total global citation score (TGCS) have coincident peaks. Zhang, Ju, and Zhu have the highest TLCS and TGCS. In terms of institutes, Chinese Acad Sci and China Agr Univ are the leading institutes in this field. The Univ Calif Davis, INRA, and USDA ARS have very high global impacts. From the research hot topics, the representative words include "soil," "agriculture," "contamination," "environment," "lead," and "balance." Representative words like "heavy-metals," "groundwater," "land-use," and "water" are emerging in the latter time period. Five leading research co-cited reference clusters are identified, including environment management, underground water, monitoring and alarming for the agriculture-environment standards, intrinsic mechanism to the circulatory system, and ecology system and land use. The recent trend is revealed from the bibliographical-coupling network, focusing on classical and old-fashion research, like pollution chemicals including N management, pesticides, and heavy metal. This work provides a holistic picture on the research in the field of agriculture pollution.

Keywords: Agricultural pollution; Environmental pollution; Network analysis; Scientometric analysis; Soil pollution; Water pollution.