# Research Frontiers in the Field of Agricultural Resources and the Environment
## 5 Conclusions  
Based on the project and paper datasets, this study investigated the method of topic extraction by the LDA model. By combining the wisdom of domain experts to judge the similarities and differences in topics, the frontier types were identified comprehensively using indicators such as the emerging degree, attention degree, innovation degree, and intersection degree of the topic. It put forward the identification and judgment methods for hot research frontiers, emerging research frontiers, extinction research frontiers, and potential research frontiers, and also carried out empirical research in the field of agricultural resources and the environment.  
The results 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 the efficient utilization of water resources\", the \"value-addedutilization 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 related to agricultural and animal husbandry\" was recognized as an emerging research frontier. The research proved that the method of extracting topics from project and paper data, combined with expert wisdom and frontier indicators for frontier identification, is an alternative method. It helps to improve the accuracy of frontier identification and the forward-looking value of frontier detection, which provides important support for knowledge services and technological innovation.  
However, there are still some shortcomings in the methods of topic extraction, discriminant indicators, and data source selection, which may lead to limitations and inaccuracies in the research results. Although the topics extracted by the LDA model largely compensate for the shortcomings of citation analysis and vocabulary analysis methods, the current topic recognition results still need to be improved. In addition, the current data sources only involve paper data and project data, and they do not consider and integrate data sources such as planning texts, policy texts, and patents representing technology research and development. In the future, it is necessary to increase the multidimensional data sources available, optimize the topic extraction models, and improve the judgment indicators, so that comprehensive support can be provided for scientific research and management layout decisions.  
Conceptualization, L.C., H.Z. and S.Y.; methodology, L.C., H.Z. and S.Q.; validation, L.C., J.Z. and Q.J.; formal analysis, L.C. and J.Z.; investigation, L.C. and H.Z.; data curation, L.C. and J.Z.; writing--original draft preparation, L.C. and J.Z.; writing--review and editing, Q.J. and S.Q.; supervision, S.Y. and H.Z.; project administration, L.C., J.Z. and H.Z. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the \"Science and Technology Innovation Project in Beijing Academy of Agriculture and Forestry Sciences\", grant numbers KJCX20230208, KJCX20240313, and KJCX20240311, and the \"Open Research Fund Program of Key Laboratory of Knowledge Mining and Knowledge Services in Agricultural Converging Publishing, National Press and Publication Administration\", and grant number 2023 KMKS01.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the author ([EMAIL_ADDRESS]) on request.  
We thank the National Institute of Food and Agriculture (NIFA) and the Web of Science (WOS) core collection database for providing the data.  
The authors declare no conflicts of interest.