## 4 Discussion  
Tree-based modeling can lead to the reliable prediction of organic matter content in soil (Figure 2). Based on the model performance analysis results, we found that GB explained 59% of the nationwide variation in soil organic matter and yielded the highest accuracy among the models evaluated, followed by RF and then DT. The precipitation variable consistently emerged as the most influential predictor in all these models under Korean conditions. Therefore, the results suggest that the promotion of supplemental irrigation is necessary for sustainable crop production due to changes in precipitation [49; 50]. Subsequently, we found that soil organic matter content under field crops also depended on soil macronutrients, such as K, P, and Ca levels. Since the soil supply of these nutrients is often supplemented by fertilizer, this suggests the importance of efficient soil fertility management under variable climates. In addition, analyzing the bee swarm summary plots provided a more comprehensive understanding of how other variables influenced the prediction (Figures 3 and 4). Here, GB uniquely identified precipitation and available P as the most influential variables, unlike DT and RF, which prioritized precipitation and K. Such differences in captured feature importance in complex data might account for improved predictive accuracy. All models revealed positive relationships between macronutrients and soil organic matter (Figure 5), but the intensity and shape of these relationships varied among the models. For example, the two-dimensional _PD_ plots illustrated the divergent interaction effects of precipitation and exchangeable K (Figure 6). The findings suggested that the relationship with organic matter is more robust when exchangeable K exceeds 1 cmol\({}^{+}\) kg\({}^{-1}\) and annual precipitation surpasses 1400 mm. Thus, organic matter content is contingent upon both precipitation and K on a large scale, suggesting specific field conditions where practices effectively increase soil organic matter [51]. This assessment comprehensively explains the varying importance levels attributed to main predictors using distinct modeling approaches. However, the impact of these features on the model is still not fully understood [52]. Only the DT model revealed a conspicuous stepwise linkage, discernible at specific values of precipitation (1400 mm), available P (600 mg kg\({}^{-1}\)), and K (1 cmol\({}^{+}\) kg\({}^{-1}\)) (Figure 5). Figure 6: Partial dependence of soil organic matter between precipitation (RN) and exchangeable potassium (K). The numbers on the plot are the predicted value of soil organic matter in g kg\({}^{-1}\). The DT model tended to create distinct, threshold-based splits in the data. In contrast, the RF and GB models captured smoother and more nonlinear relationships that offer a comprehensive understanding of how variables incrementally affect soil organic matter in a more detailed and integrated manner. This _PD_ analysis underscores the nuances in how different models represent the intricate interplay between soil variables and organic matter. This insight provides valuable clarity regarding these two variables' complex interplay and collective impact on soil organic matter. Up to this point, our focus was on elucidating the overarching behavior of global models, aiming to comprehend the insights derived from the data. However, this approach falls short in explaining the nuances of local model behavior, a critical aspect in discerning the factors deemed important by the models when predicting values for specific instances. We also employed the SHAP method to assess the importance of variables at a specific local site. The results indicated that pH was more important than precipitation, identified as the most influential factor in controlling global model behavior. Therefore, future research is needed to gain a more general understanding of local factors and site-specific targeting for more efficient organic matter management.