## 3 Results and Discussion
As a result, model learning was based not only on true yield variability, but also on noise or approximation errors inherent in the QDANN predictions. Additionally, yield sampling performed in this study could be improved by incorporating a stratified random sampling approach to more effectively retain the value distribution of crop yield from the entire dataset in generated point samples [82]. While this methodology significantly increases spatial coverage beyond what is achievable with combine harvester-based systems, and accounts for within-field variability that is absent in aggregated county-level statistics, it still falls short of the precision offered by harvester-mounted yield monitors, which are currently the most dependable source of ground truth data for such applications [83]. As more ground truth datasets become available, future work should prioritize the parallel assessment of emerging vegetation indices to assess their viability as indicators of agricultural potential. Therefore, future studies should implement direct yield mapping data from combine harvesters as an external validation dataset. Figure 4: Relative variable importance of the evaluated phenological metrics based on the most accurate machine learning model per crop yield dataset. Considering the potential improvements of the used methodology for crop yield potential prediction, the integration of remote sensing, weather, and topographic variables has been noted to enhance yield prediction accuracy. Additionally, Sentinel-1 images provide a complementary data source to Sentinel-2, potentially further improving prediction accuracy [29]. Improvements in predictive accuracy through the use of multivariate phenological metrics from Sentinel-2 images with machine learning were observed in comparison with the use of a single indicator [84], which produced notably lower coefficient of correlation values. Similarly, [PERSON] et al. [31] successfully utilized a growing degree day (GDD)-based feature aggregation to increase the out-of-year R\({}^{2}\), which could be additionally used to improve the results from this study. Additionally, the incorporation of static soil and field-level attributes was found to modestly enhance model performance, as [PERSON] et al. [85] reported that soil properties improved within-field soybean yield predictions. When combined with phenological and spectral features, environmental covariates may improve model accuracy by accounting for abiotic yield drivers, and enable more reliable stratification of environmental conditions. However, the issue of coarse spatial resolution in available environmental covariate data in comparison to Sentinel-2's spatial resolution, especially considering climate datasets [86], presents an obstacle for comprehensive data fusion, and should be explored in future studies. While static variables provide a baseline for site potential, dynamic indicators, such as phenological metrics based on vegetation indices, remain primary drivers of yield variability at the field level [33]. Figure 5: Relative variable importance of the evaluated vegetation indices based on the most accurate machine learning model per crop yield dataset.