# A Geographically Weighted Random Forest Approach to Predict Corn Yield in the US Corn Belt
## 4 Discussion  
This study addresses several important issues in building models for corn yield prediction. Our results demonstrate that model selection plays an important role in achieving more accurate yield predictions. We found that GWRFR performs better than other non-spatial machine learning algorithms. Nearby samples are more important than those distant ones in yield prediction at a specific location [51; 85; 81]. Our results are consistent with other studies that compared the performance of spatially weighted methods with that of non-spatial methods in crop yield prediction [11; 29]. The Moran's I values of residuals show that GWRFR can better capture spatial heterogeneity than traditional non-spatial models. Although GWFR has a better performance than other models in county-level corn yield prediction in this study, more research needs to be conducted to evaluate its effectiveness in predicting the yield for other crops in other areas at different spatial scales. The performance of SVR trained with full length features and NDVI/EVI was second best; however, its performance gradually decreases when trained with GPP, climate data, and  
Figure 10: The spatial distribution of prediction errors of (**a**) MLR, (**b**) PLR, (**c**) SVR, (**d**) DTR, (**e**) RFR, and (**f**) GWRFR using (1) full-length features, (2) vegetation indices derived from RS data, (3) GPP, (4) climate data, and (5) soil data. soil data. This shows that SVR can compete with GWRFR when full-length features or linearly correlated features (NDVI/EVI) are available. Our results also show that feature selection is also very important in corn yield prediction. We observed the highest R\({}^{2}\) and the lowest RMSE in the models trained with full length features. Our findings are consistent with previous studies [28; 94] as the accuracy of machine learning models is heavily dependent on the training data. NDVI and EVI have been widely used in crop yield prediction [44]. VIs such as NDVI and EVI can reduce the noise in raw reflectance values [95] and provide vegetation-specific information for predicting corn yield. Moreover, VIs can be utilized in several other ways to improve the accuracy of machine learning models. For example, NDVI and EVI can be used to derive phenological information and the underlying environmental factors in different growth stages to further improve model performance [96; 97]. Extreme climatic conditions can negatively impact crop yield and limit the predictive power of models if relevant data are not present. Furthermore, increased precipitation can have different impacts on crop yield depending on the spatio-temporal distribution of precipitation [98]. These additional factors affecting crop yield can only be addressed by including detailed climate data in the model training process. The overall performance of soil data is not very well compared with other categories of features such as VIs, GPP, and climate data. Furthermore, we found that time-series variables such as NDVI, GPP, and climate data can help improve model performance. This is because time-series features can better capture crop conditions in different stages of growth. Lastly, our study also has some limitations. County-level yield prediction involves the aggregation of yield and associated factors over a large area. Data aggregation can result in the loss of field-level or pixel-level information. Thus, further research needs to be conducted to examine if our findings can be used for field-level crop yield predictions. Moreover, several management factors such as plant density and planting dates also affect crop yield but were not included in this study. More research needs to be conducted to further examine the impacts of these factors on crop yield prediction. Another aspect in crop yield prediction is data availability. Since VIs and GPP datasets are globally available and consistent in terms of spatial and temporal resolution, they can be used to construct scalable crop yield models.