## 4 Results and Discussion
### Prediction of Optimal Planting Zones for Se-Rich Crops  
#### 4.2.1 Fuzzy Weights-of-Evidence  
A common challenge in applying the FWofE model for prediction lies in the selection of evidence layers. In this study, the selection of evidence layers for the FWofE method and training data layers for ML models was guided by the correlation analysis results and previous research findings. Because numerous root soil indicators are related to Se crop content, factor analysis was first performed to reduce dimensionality. To avoid the closure effect of the root soil data, the data should be opened by log-ratio transformation before performing factor analysis. Because the centered log-ratio (CLR) transformation preserves the correlation structure between components and is computationally straightforward, it was applied to process the root-zone soil data [48; 49]. The parameters and diagnostic metrics of the factor analysis are presented in Table 12. The resulting factor loadings are shown in Table 1, where Factor3 has high negative loadings on Mo and Se, while Factor4 has high negative loadings on P and S. According to the correlation analysis results, Factor3 contains information on root soil elements that are positively correlated with Se crop content, while Factor4 contains information on root soil elements that are negative correlated with Se crop content. Therefore, Factor3 and Factor4 were identified as significantly correlated with Se crop content. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Elements** & **Factor1** & **Factor2** & **Factor3** & **Factor4** & **Factor5** \\ \hline As & 0.054 & \(-\)0.733 & 0.025 & 0.089 & \(-\)0.284 \\ Cd & \(-\)0.648 & \(-\)0.200 & \(-\)0.353 & \(-\)0.112 & 0.038 \\ Co & 0.715 & 0.390 & \(-\)0.109 & 0.153 & \(-\)0.087 \\ Cr & 0.750 & 0.529 & \(-\)0.021 & 0.098 & 0.053 \\ Cu & 0.690 & \(-\)0.308 & \(-\)0.088 & 0.004 & 0.306 \\ F & 0.680 & \(-\)0.104 & \(-\)0.040 & 0.120 & \(-\)0.130 \\ Hg & \(-\)0.116 & \(-\)0.240 & \(-\)0.197 & \(-\)0.717 & \(-\)0.061 \\ Mn & 0.090 & \(-\)0.260 & \(-\)0.032 & 0.269 & \(-\)0.611 \\ Mo & \(-\)0.073 & \(-\)0.425 & \(-\)0.623 & 0.285 & \(-\)0.185 \\ Ni & 0.870 & \(-\)0.027 & \(-\)0.100 & 0.169 & \(-\)0.085 \\ P & \(-\)0.281 & \(-\)0.161 & 0.320 & \(-\)0.739 & \(-\)0.042 \\ Pb & \(-\)0.225 & \(-\)0.025 & \(-\)0.056 & 0.164 & 0.816 \\ S & \(-\)0.246 & 0.271 & \(-\)0.045 & \(-\)0.723 & \(-\)0.027 \\ Se & \(-\)0.377 & 0.284 & \(-\)0.736 & 0.197 & 0.054 \\ Zn & 0.250 & 0.131 & 0.021 & 0.207 & 0.840 \\ SiO\({}_{2}\) & 0.062 & 0.913 & 0.080 & 0.095 & 0.016 \\ TFe\({}_{2}\)O\({}_{3}\) & 0.851 & 0.259 & 0.153 & 0.219 & 0.046 \\ MgO & 0.082 & 0.254 & 0.588 & 0.459 & 0.224 \\ CaO & \(-\)0.402 & \(-\)0.449 & 0.707 & \(-\)0.046 & \(-\)0.084 \\ K\({}_{2}\)O & 0.367 & 0.739 & 0.118 & 0.217 & 0.055 \\ pH & \(-\)0.151 & 0.226 & 0.810 & 0.248 & \(-\)0.135 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Factor loadings of root soil elements and pH. Based on the actual conditions of the study area and FWofE experiments, the final evidence layers included the factor score distribution maps of Factor3 and Factor4, as well as the 500-m nine-ring buffers of the study area's main geological strata (Silurian, Permian, Triassic, Cretaceous, and Paleogene) (Figure 3). Before building the FWofE model, it was necessary to identify Se-rich crop samples. This required setting a threshold to distinguish Se-rich crops, making threshold determination a key step in the modeling process. Due to the availability of standardized Se thresholds only for Se-rich rice according to national regulations and due to the absence of national standards for other crops (which follow regional standards, such as DBS42-002-2022, issued by Hubei Health Commission, China), this study faced challenges in defining consistent criteria for Se-rich crops [50].