# Intra-Plot Variable N Fertilization in Winter Wheat through Machine Learning and Farmer Knowledge
## 1 Introduction  
Farmers and agronomic advisors, together with scientists, must be at the center of the innovation process if the social, ecological, and economic objectives set by the EU are to be achieved. Collaborative work between these groups would allow the development of innovative solutions. Farmers are custodians of local knowledge and practices [1], and experimentation is part of their daily routine due to their need to improve the quality and productivity of their crops [2], even if, normally, a proper measurement is not made. On the other hand, scientists, when conducting agronomic trials, spend many hours measuring and controlling variables of interest in small experimental fields, and for this purpose, they use a wide variety of sensors. In this way, the effects of the tested factors can be correctly measured and thus demonstrate their relevance. Additionally, these trials are often not representative of commercial plots, as they are not able to consider spatial variability adequately. In many cases, the differences generated by plot variability outweigh the effect of the treatment itself [3]. Thus, the methodology being used by scientists has been one of the reasons for the lack of interaction between the two groups. Collaborative work between scientists, capturing and interpreting the data, and farmers with a deep knowledge of their plots is therefore essential. Unfortunately, there is a lack of flow of the knowledge generated at both levels, the professional and the scientific, and the interaction between the two is not always productive [4]. Because of this, the on-farm experimentation (OFE) strategy is presented as an alternative to solve the existing problems between the two groups through advancement to more modern and collaborative farming [5]. In summary, OFE is a globaltrend that recognizes the collaborative work between farmers and scientists as a pathway to develop new innovative ways to solve the challenges facing modern agriculture [6]. Precision agriculture is a management strategy that considers temporal and spatial variability to improve the sustainability of agricultural production, and its philosophy is in line with that of OFE. Adequate fertilization of crops is one of the greatest challenges faced in modern agriculture. More specifically, a good fertilizer strategy will have to consider potential yield variability and adjust the applied dose accordingly [7], applying more fertilizer in the more productive areas and less in the less productive ones. Since crop fertilizer demand is related to crop yield [8], it is essential to adjust the N fertilizer supply to crop needs. A lower than required dose reduces production, as does an excessively high dose, since it produces physiological damage [9]. Additionally, nitrogen fertilizers applied in excessive quantities cause N losses, mainly through ammonia volatilization, denitrification, and leaching [10]. However, the most common practice consists of homogeneous fertilization for the whole plot, without considering the different requirements of the crop that depend on the intrinsic variability of the plot. Accurate and early yield estimation is a good way to adapt fertilization to plant requirements. This is because the yield and therefore the amount of N needed varies from year to year. Thus, if it is possible to estimate the yield early, before applying the last N dose, the fertilizer contribution can be decided for each campaign based on the needs of each plot or even the area of the plot. Therefore, in order to be useful, yield estimation must be done before the application of the cover crop fertilizer. In the study area, this is carried out at the beginning of stem elongation (GS 30) [11] for cereal cultivation. Thus, through variable-rate fertilization (VRF), precision farming enables the adjustment of fertilizer application to crop needs [12]. The first step to applying variable rate fertilization consists of delineating yield zones.