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Research Frontiers in the Field of Agricultural Resources and the Environment

文献类型： 外文期刊

作者： Chuan, Limin 1 ; Zhao, Jingjuan 1 ; Qi, Shijie 1 ; Jia, Qian 1 ; Zhang, Hui 1 ; Ye, Sa 2 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Inst Data Sci & Agr Econ, Beijing 100097, Peoples R China

2.Chinese Acad Agr Sci, Agr Informat Inst, Key Lab Knowledge Min & Knowledge Serv Agr Converg, Natl Press & Publicat Adm, Beijing 100081, Peoples R China

关键词： LDA model; research frontier; agricultural resources and the environment; frontier indicators; theme fusion

期刊名称：APPLIED SCIENCES-BASEL （ 影响因子：2.5； 五年影响因子：2.7 ）

ISSN：

年卷期： 2024 年 14 卷 12 期

页码：

收录情况： SCI

摘要： From the perspective of project and paper datasets, research frontier recognition in the field of agricultural resources and the environment using the Latent Dirichlet Allocation (LDA) topic extraction model was studied. By combining the wisdom of domain experts to judge the similarities and differences of clustering topics between the two data sources, multidimensional indicators, such as the emerging degree, attention degree, innovation degree, and intersection degree, were comprehensively constructed for frontier identification. The methods for hot research frontiers, emerging research frontiers, extinction research frontiers, and potential research frontiers were proposed. The empirical research in the field of agricultural resources and the environment showed that the "interaction mechanism of plant-rhizosphere-microbial diversity" was a hot research frontier in the years 2016-2021. The themes of "wastewater treatment technology and efficient utilization of water resources", the "value-added utilization of agricultural wastes and sustainable development", the "soil ecological response mechanism under agronomic management measures", and the "mechanism of soil landslide, erosion, degradation and prediction evaluation" were judged as potential research frontiers. The theme of "ecosystems management and pollution control of agricultural and animal husbandry" was recognized as an emerging research frontier. The results confirm that the fusion method of extracting topics from project and paper data, combined with expert intelligence and frontier indicators for fine classification of frontiers, is an optional approach. This study provides strong support for accurately identifying the forefront of scientific research, grasping the latest research progress, efficiently allocating scientific and technological resources, and promoting technological innovation.

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