# Intra-Plot Variable N Fertilization in Winter Wheat through Machine Learning and Farmer Knowledge
## 3 Results and Discussion
### Variable Fertilizer Application Considerations and On-Farm Experimentation  
To fertilize the plot homogeneously with a conventional dose of 110 kg N ha\({}^{-1}\) would require 1345 kg (Table 3, final column). However, zone-based fertilization allowed a reduction in the N supply to 1064 kg (Table 3, second column). This equates to a reduction of 281 kg N, about 20.8%, thus achieving the new goal of reducing fertilizer consumption by 20% as established by the EU in its From Farm to Fork strategy. Moreover, in 2020, when this experiment was carried out, the price of ANC 27% fertilizer was 0.22 \(\upmu\) kg N\({}^{-1}\)[47]. The use of the VRF technique allowed a saving of 231.3 \(\upmu\) of fertilizer in this plot. However, the price of fertilizers is increasing dramatically and the use of this technique, in addition to contributing to reducing the impact of agriculture on the environment, would allow the farmer to increase the economic return of the farm. The reduction of the environmental impact comes from a better utilization of the nutrients by crops. The application of a higher dose in the more productive areas and a lower dose in the less productive ones [48] is expected to reduce fertilizer losses [49]. Crop vigor monitoring with the S2 NDVI showed that, as the crop cycle advances, the variability of crop vigor increasingly resembled the fertilization prescription map (Figure 5C). This trend was maintained until the onset of plant senescence (GS80). Since a yield monitor was not available in this plot, the farmer was asked if he had seen any change in the yield. The farmer's response was negative, confirming that the VRF had not decreased the expected production. Even the farmer commented that, in less productive zones, the yield might have been somewhat better. These results are similar to those obtained by [PERSON] et al. [50] when using variable fertilization. There have been glimpses of the economic and environmental improvements that the use of VRF techniques could bring since the beginning of precision agriculture [51]. However, the latest study published by [PERSON] et al. [52] shows that the net economic return obtained by small farmers adopting this technique is related to soil variability, fertilizer price, and the cost of the sensor used to make measurements. The cost of fertilizer fluctuates from season to season depending on the price of energy and fuel [53]. High fertilizer prices reduce the farmer's profit margin, which means an increase in the effort the farmer has to make [54]. Rising energy prices have caused the price of fertilizer to triple from the 2021 season to the 2022 season [47]. However, it has opened the door to the adoption of variable rate application technology because of its ability to optimize fertilizer use without decreasing yield. Bearing in mind socio-economic circumstances and new technological development capabilities, it is essential to reinforce collaborative work between academics and farmers with a view to integrating agronomic knowledge into new technological developments. However, one of the main barriers to a more fluid collaboration between farmers and scientists is the experimental design used by researchers and the tendency of scientists to generalize the results obtained in experimental plots. Close collaboration with farmers would make it possible to evaluate the spatiotemporal variability of their plots in a less costly and more efficient way than if this had to be achieved employing classical controlled field trials. As reported by [PERSON] et al. [55], in this type of trial, the differences caused by spatiotemporal variability may be greater than those caused by the different treatments. To avoid this tendency and to obtain more realistic and scalable results, the trial should be conducted using commercial plots. OFE considers the heterogeneity of farmers' circum