# Research Frontiers in the Field of Agricultural Resources and the Environment
## 4 Discussion  
### About the Theoretical Implications  
This study has deepened and expanded the knowledge on agricultural resources and the environment by applying new methods and technologies, such as text mining tools involving LDA. By systematically analyzing a large amount of textual data, it revealed the research frontiers in this field, and provided new insights and supplements to the existing knowledge system. This study put forward the hot research frontiers, emerging research frontiers, extinction research frontiers, and potential research frontiers in the field of agricultural resources and the environment. It can provide strong support for the \begin{table}
\begin{tabular}{l l l} \hline \hline
**Number** & **Category** & **Research Hot Spot or Frontier** \\ \hline
1 & Hot spot & Influence of soil-crop management on greenhouse gas emissions and its regulation \\  & & Response and mechanism of soil and rhizosphere \\
2 & Hot spot & microorganisms compared to land use patterns \\
3 & Hot spot & Research and efficient utilization of new fertilizers \\
4 & Hot spot & Mechanism and regulation of nitrogen fixation in plants \\
5 & Hot spot & Effect and mechanism of growth-promoting and \\  & & stress-resistant plant beneficial bacteria \\
6 & Hot spot & Assembly patterns and control mechanism of the root \\  & & microbiome \\
7 & Key hot spot & Soil carbon sequestration potential and its regulation \\  & & mechanism \\
8 & Frontier & Microbial mechanism of soil nitrogen transformation and its \\  & & agronomic environmental effects \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Research hot spots and frontiers in the field of agricultural resources and the environment proposed by the Chinese Academy of Agricultural Sciences. research direction and topic selection of frontline researchers in the field of agricultural resources and the environment. Researchers still need to focus on in-depth research in the areas of plant-soil-microbial diversity, water treatment and utilization, agricultural wastes utilization, soil ecology and quality conservation, and ecological pollution and remediation. The use of methods such as LDA and text mining tools is of great significance in supporting researchers to conduct research in a target field. These tools can provide an effective data analysis method to help researchers extract useful information from a large amount of text. By applying these methods, researchers can gain a deeper understanding of the research direction, providing strong support for future research topics. This study proposed a method for topic clustering based on the LDA model and verified the applicability of the LDA model in topic extraction. This model can automatically extract implicit topic structures from large-scale text sets through unsupervised learning, providing powerful tools for organizing, understanding, and applying text data. By using LDA and other machine learning tools, existing knowledge about agricultural resource utilization and environmental maintenance can be further improved. These tools can help researchers more accurately identify and classify themes in text data, providing more accurate analysis results. In addition, machine learning can also be used to predict and optimize the utilization of agricultural resources, as well as formulate corresponding environmental protection strategies. This study provides new ideas and methods for sustainable development by delving into the relationship between agricultural resource utilization and the environment. By optimizing the utilization of agricultural resources and reducing their impact on the environment, the sustainable development of agriculture can be promoted while protecting ecosystems and natural resources. This study provides a new approach for identifying frontier topics and theoretical and technical support for the selection of scientific research directions and technological innovation. With the acceleration of globalization, there is an increasing amount of cross-domain and cross-language textual data.