# Intra-Plot Variable N Fertilization in Winter Wheat through Machine Learning and Farmer Knowledge
## 3 Results and Discussion
### Fertilization Prescription Map
This area was filled in with low-quality soil during the operation conducted to allow agricultural mechanization. This plot comes from the union of several smaller plots that have been combined over the years. This combination process is common in the area and can be verified by studying old orthophotos that are available from 1945 and by gathering information from farmers in the area. This line of work is interesting and would very probably provide valuable information and knowledge to be able to estimate the cereal yield in this and other plots. Furthermore, the farmer confirmed that the high-yielding zone 4 (Figure 5A) was in a deeper and colder soil and is situated next to a stream, thus having a greater soil volume that holds more water. Usually, the crop has sprouting difficulties because of excess water but afterwards tends to grows better because the crop has more water available. This is a clear example of the knowledge held by the farmer about his field and his comprehension of the surrounding environment. After comparing the map obtained with the machine learning algorithm (Figure 5A) and the map provided by the farmer (Figure 5B), an N fertilizer prescription map (Figure 5C) was agreed upon with the farmer. The zones of the prescription map must be large enough to work with a fertilizer spreader with a working width of 24 m. The fertilizer used was CAN in granular form, which has a nitrogen concentration of 27%. It was applied following the prescription map (Figure 5C) using an Amazone ZA fertilizer spreader with section cutting. Fertilizer doses were established based on the potential yield obtained by RF. Soil Nmin was not considered in this study for several reasons. Firstly, the spatial and temporal variability of Nmin in the soil is high [45], and the time and effort required to obtain an accurate sampling is completely unaffordable in commercial exploitations. For example, the Association of German Agricultural Research Institutes recommends taking 15 soil samples every 90 m\({}^{2}\) to capture Nmin variability. Considering the size of this plot, this would mean 1333 sampling points. Secondly, the soil Nmin value can change rapidly depending on leaching, mineralization, and gaseous losses. Thirdly, previous studies performed in the study area showed that Nmin values at the beginning of winter were low [46], always below 60 kg N ha\({}^{-1}\). Regarding crop uptake, ARVALIS, the French arable crops R&D institute ([[https://[EMAIL_ADDRESS]](https://[EMAIL_ADDRESS])]([https://[EMAIL_ADDRESS]](https://[EMAIL_ADDRESS])) (22 September 2022)), measured that wheat extracts between 28 and 32 kg of N per ton of yield. With this data as a benchmark, a nitrogen recommendation was made in consultation with the farmer and based on a yield prescription map. The lowest fertilizer dose, 55 kg N ha\({}^{-1}\) as top-dressing, was assigned to the least productive area (zone 1) while the usual top-dressing dose of 110 kg N ha\({}^{-1}\) was maintained for the most productive area (zone 4), and intermediate values (88 and 99 kg ha\({}^{-1}\)) for the other two zones (Table 3). Specifically, the amount of N applied in the least productive areas was half that of the most productive.