## 4 Discussion  
This study supported the observation of previous studies regarding the potential of machine learning in cropland suitability prediction, indicating its benefits compared to the conventional approach. Recent advances in GISs and machine learning have led to the emergence of fusion models that combine the strengths of both approaches. This integration enables more robust predictive analytics in comparison to conventional GIS-based multicriteria analysis. However, the approach based on machine learning and the conventional multicriteria analysis approach should be evaluated according to independent ground-truth data to produce definite observations on their accuracy. The classification of suitability classes was achieved through unsupervised K-means clustering based on historical soybean parcel data, leading to the formation of training and testing samples essential for validating suitability predictions. However, the reliance on training and testing datasets may restrict its applicability to major crop types, potentially excluding rarer crops that are cultivated on fewer and smaller parcels [43]. The results indicate that, irrespective of the machine learning technique employed or the specific variables analyzed, the year 2021 consistently exhibited lower R\({}^{2}\), RMSE, and MAE values compared to earlier years. This decline in accuracy can be attributed to several factors, particularly the inconsistent interannual weather variations that are often difficult to model accurately within a GIS framework. For instance, extreme weather events, such as droughs or excessive rainfall, can lead to abrupt changes in vegetation dynamics that may not be captured by historical data or existing models [44]. Additionally, factors such as land management practices, fertilization rates, and other anthropogenic influences can introduce variability that cannot be reliably modeled in GIS-based approach. Due to these effects, several study years were chosen for research, which mitigate these effects and provide more robust representation of cropland suitability compared to single-year assessments. While ensemble machine learning methods in theory could provide superior prediction accuracy to individual methods, the results from this study do not support this observation due to two likely factors. Overfitting is a significant concern if the base models are too complex or correlated [45], and the ensemble may capture noise rather than the underlying  
Figure 3: The final cropland suitability map for soybean cultivation in continental Croatia according to FAO standards. data patterns, leading to poor generalization on unseen data. Additionally, lack of diversity among the models can hinder performance when models are too similar and tend to make correlated errors, which might have been the case of using RF and XGB, which base their predictions on similar principles based on decision trees [46]. Future studies should also evaluate the relationship between cropland suitability levels and environmental abiotic factors, preferably based on partial dependence plots and related metrics based on interpretable machine learning [47], which could produce relevant observations for upgrading the proposed methodology and its application in agricultural practice. Considering similar predictive mapping studies, [PERSON] et al. [17], within the framework of the SoilGrids project, developed an advanced machine learning-based prediction system that integrates soil samples with publicly available spatial data related to abiotic factors. This innovation marks a significant shift in the predictive mapping used for spatial environmental variables, extending its applicability beyond soil science to a variety of fields. Recently, researchers have acknowledged this potential for assessing cropland suitability, although this area is still in its nascent stages. For instance, [PERSON] et al.