Affiliation:
1. Institute of Data Science and Agricultural Economics, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
2. Key Laboratory of Knowledge Mining and Knowledge Services in Agricultural Converging Publishing, National Press and Publication Administration, Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Abstract
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.
Funder
Science and Technology Innovation Project in Beijing Academy of Agriculture and Forestry Sciences
Open Research Fund Program of Key Laboratory of Knowledge Mining and Knowledge Services in Agricultural Converging Publishing, National Press and Publication Administration
Reference29 articles.
1. Early identification of breakthrough research from sleeping beauties using machine learning;Li;J. Informetr.,2024
2. How to conduct a Delphi consensus process;Savic;Anaesthesia,2023
3. A detection method for science and technology frontiers based knowledge tree;Zeng;Inf. Stud. Theory Appl.,2024
4. Khodyakov, D., Grant, S., Kroger, J., Gadwah-Meaden, C., Motala, A., and Larkin, J. (2023). Disciplinary trends in the use of the Delphi method: A bibliometric analysis. PLoS ONE, 18.
5. Identifying disruptive technologies by integrating multi-source data;Liu;Scientometrics,2022
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