# Intra-Plot Variable N Fertilization in Winter Wheat through Machine Learning and Farmer Knowledge
## 3 Results and Discussion
### Validation of RF Ability to Predict Whole-Plot Yield Using Data from the Remaining Fields  
Studies such as those conducted by [PERSON] et al. [40] or [PERSON] et al. [41] estimated the yield of specific pixels using different sources of information and algorithms. However, this study analyzed the potential of using satellite information and elevation (derived from a high-resolution DEM) to estimate the yield of an entire plot for which detailed yield information was not available. For this purpose, the RF algorithm was trained 13 times using the information of 12 parcels to estimate the yield of the remaining one (the \"leave one out\" validation technique). This process is not usual since few works have performed this process [41]. In this different validation, the highest mean R\({}^{2}\) was 0.60 while the lowest mean RMSE was 1223.32. These results were obtained using seven variables in each tree (MTRY = 7). Individual analyses of each plot showed that the R\({}^{2}\) ranged from 0.13 to 0.88 (Table 2) and the RMSE from 562.34 to 2229.17 (with these values corresponding to the Menor and Prado plots, respectively). However, the analysis showed that the predictive ability of RF to forecast an entire plot is weaker than that of predicting a single pixel. For example, the RMSE mean increases from 496 to 1200.49. The cause of the worsening of the prediction capability could be the size of the dataset and plot heterogeneity. For example, Babea and Kukura are similar, so the algorithm trained with information from one of these has good capability to predict the other. The worst prediction was of the Ollavarre plot. Although the average production of this plot is similar to that of Parque, the two are probably not similar in terms of the auxiliary variables, and so the forecast was not good. However, when 70% of the data is used in the training process, some of the data will come from the Ollavarre plot. In the validation process, when the remaining 30% of the data information is used, there will be some similar data, allowing a better prediction. Despite this, the results obtained in the test validation of the whole plots are similar to those reported by [PERSON] et al. [42], where the RMSE% is around 20%. Considering these results, the RMSE% of the Torres plot prediction is expected to be close to 20%, although there is no certainty about this because RMSE variation is very high (Table 2). Therefore, it is essential to collaborate with the farmer and discuss with him the estimated yield map to see if it fits well with his yield knowledge acquired over years of fieldwork. The RF hyperparameters were adjusted to 500 trees and the MTRY to 7. The calculation of the importance of the permutation characteristic of each prediction shows that the variable that contributes most to the model is the DEM. On the other hand, the images that contribute the most are those corresponding to the month of June (28 June and 18 June). Similar results were reported by [PERSON] et al. [34]. In addition, the image corresponding to 19 February is the third largest contributor. This is not surprising, as