# Intra-Plot Variable N Fertilization in Winter Wheat through Machine Learning and Farmer Knowledge
## 3 Results and Discussion  
### NDVI Evolution and Correlation with Yield  
Figure 3 shows the changes over time of the NDVI in the 14 study plots, which was similar to that reported by [PERSON] et al. [32]. The onset of each phenological period was determined using the STICS (Simulateur multidisciplinaire pour les Cultures Standard) software [33], as Zadoks growth stages were not determined in the field. The stem elongation phase (GS31), which lasted until 30 March, was characterized by a rapid increase in NDVI (Figure 3). In this phase, three plots (Parque, Menor, and Baratua) showed a lower NDVI value and, at the end of the crop season, their yields were among the lowest (Table 1). The heading phase (GS39) was characterized by a slow NDVI increase and lasted until 14 May, when the highest values were reached. The maximum NDVI values ranged from 0.91 for the Alto plot to 0.70 for the Menor plot. The Alto plot was the most productive (8.6 t ha\({}^{-1}\)) and the Menor plot the least (5.3 t ha\({}^{-1}\)) (Table 1). From 13 June onwards, the NDVI drops rapidly, showing that the crop was in the ripening stage (GS85) in each plot.  
Figure 3: Seasonal trend in NDVI data of the 14 plots over the 2019 season. The thick red line shows changes in the Pearson correlation coefficient (r) between NDVI and yield (Torres plot not included). GS refers to the wheat growth stage. GS21 Tillering, GS31 Stem elongation, GS39 Heading, and GS85 Ripening.  
Figure 3 shows the NDVI evolution for each plot. In addition, the Pearson correlation (thick red line) between the mean NDVI of all plots and the final yield is also represented. This correlation is similar to the one reported by [PERSON] et al. [34], with a modest increment during the tillering phase (GS21) and a steady increase from the stem elongation stage (GS31) until the end of heading (GS59). Surprisingly, before reaching the maximum correlation coefficient on 18 June, the correlation collapsed on 13 June (Figure 3). The NDVI value for this date also underwent a slight drop (Figure 3). The loss of correlation may be due to a failure in the NDVI reading. This relationship between NDVI and yield supported the use of NDVI as one of the auxiliary variables for yield estimation. The climatic variables affecting crop development were not considered in this study as the plots were close to each other and the data from only one campaign were analyzed.  
The work in collaboration with the farmer started at this point. The NDVI images corresponding to the highest correlation with yield were analyzed together with the farmer to better understand the underlying reasons for yield variability. The farmer confirmed that, in general, the NDVI was a good representation of what had happened that season with the crop but also pointed out that, in some cases, it was not representative of most years. For example, the river next to one parcel had overflowed, flooding the crop. The NDVI showed that production in that area was low, but the farmer knew that it was usually the most productive area as it had deep and fertile soils. Therefore, the use of vegetation indices such as the NDVI can be of value for the design of fertilizer management zones, but, if fertilization is to be adjusted using a yield map from the previous season, it is essential to ensure that it is representative of the average yield of the plot and its variability.