## 5 Conclusions  
This study analyzed Se crop content and corresponding root soil data in Xiaoyan Town, Nanzhang County, employing FWofE, BP-NN, and SVR methods to delineate regions with different Se content levels and identify optimized planting zones for Se-rich crops. These findings provide scientific evidence and planting guidelines for the rational cultivation and sustainable development of Se-rich crops in the area, promoting the development of the local Se-rich industry.  
The study's main objective was to develop a reliable model for predicting Se-rich planting zones. Among the FWofE, BP-NN, and SVR models, the BP-NN model demonstrated the best predictive performance. The RMSE and R\({}^{2}\) of the BP-NN model verified by the test data were 0.0191 and 0.77, respectively. Additionally, the correct classification rate of the BP-NN model reached 86.67%. Specific predictions for Se-rich planting zones for wheat, rice, rapeseed, and tea were also made, with the model's R\({}^{2}\) values indicating that the predictions for rice (0.95) and rapeseed (0.99) were the most reliable.  
The correlation analysis and spatial analyses revealed certain patterns consistent with previous research on factors influencing Se uptake by crops. Elements that were significantly positively correlated with Se showed overlapping spatial distributions with Se-rich crop planting zones, reflecting their synergistic effects on Se uptake. Conversely, elements with significant negative correlations exhibited an opposite spatial distribution, indicating their antagonistic effects.  
Based on these findings, targeted cultivation recommendations are proposed. For Se-rich crop planting zones, appropriate chemical treatments or adjustments to soil physicochemical properties should be implemented to avoid the accumulation of elements that antagonize Se, ensuring the long-term sustainability of Se-rich crop cultivation. For Se-low crop planting zones, applying exogenous Se fertilizers, adjusting soil pH, and modifying redox conditions can enhance Se bioavailability in the soil and increase Se content in crops.  
Importantly, the prediction results can assist farmers in selecting optimal areas for Se-rich crop cultivation, improving yield quality and income. Local planners may also use these results for agricultural zoning, soil management, and policy-making aimed at sustainable land use and Se-rich food production.  
However, a limitation of this study is that the model does not incorporate Se bioavailability data, which could provide a more comprehensive understanding of Se uptake and improve predictions. Future work could integrate more data and extend the analysis to more crops and regions to validate model applicability.  
Conceptualization, J.L. and S.X.; methodology, J.L. and S.X.; software, J.L., W.Z., W.W. and H.S.; validation, W.Y., W.W. and H.S.; formal analysis, J.L.; investigation, W.Y.; data curation, J.L.; writing--original draft preparation, J.L. and W.Y.; writing--review and editing, S.X. and E.J.M.C.; visualization, J.L. and W.Z.; supervision, J.L. and S.X. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by the New geochemical exploration technology for shallow-covered landscapes in desert Gobi and alpine steppes (No. 2024 ZD1002400) under the subproject \"Geochemical intelligent prospecting prediction and software development for copper-nickel and gold deposits\" (No. 2024 ZD1002406) and the Geochemical evaluation of land quality in Nanzhang County, Hubei Province (No. HBJTD20160101).  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
We are very thankful for all the editors and reviewers who have helped us improve the manuscript.  
The authors declare no conflicts of interest.
Figure A1: The distributions of root soil elements (**(a)**: Se, **(b)**: Mo, **(c)**: P, and **(d)**: S).