# Intra-Plot Variable N Fertilization in Winter Wheat through Machine Learning and Farmer Knowledge
## 4 Conclusions  
Combining machine learning techniques and a farmer's knowledge of his plot, it was possible to establish a fertilization map of a plot that allowed significant savings in fertilizer consumption.  
Of the 15 machine learning algorithms that were tested, RF performed best. This study corroborated the usefulness of free auxiliary information such as S2-derived vegetative indices and LiDAR-derived elevation. In addition, the predictive ability of this algorithm to predict the yield of an entire plot using the \"leave one out\" technique was tested. The collaboration between scientists and farmer resulted in a very positive exchange of information that allowed the farmer to change the fertilization strategy of the whole farm and the scientists to better understand how soil properties and plot history affect yield. All this information could be used to improve future models.  
A.U. worked in the following: Conceptualization, Methodology, Software, Data processing, Formal analysis original draft preparation, Visualization, Investigation, Interpretation. A.C. worked in the following: Methodology, Data acquisition, Results analysis. Resources. I.E. worked in the following: Data acquisition. A.A. worked in the following: Methodology, Writing--Reviewing and Editing, Supervision of Parameter computing, Funding acquisition, Project administration. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the BIKAINTEK grant of the Basque Government, Department of Economic Development, Sustainability and Environment. It was also supported by the NITRALDA Project founded by the Basque Government in the \"Strategy for the digitalization of the food value chain and gastronomy in the Basque Country\". It was additionally supported by the LIFE program thanks to the project AGROGESTOR (LIFE16 ENV/ES/287).  
Data available in a publicly accessible repository that does not issue DOIs. The Topographic Data can be found in [[https://www.geo.euskadi.eus/cartografia/DatosDescarga/](https://www.geo.euskadi.eus/cartografia/DatosDescarga/)]([https://www.geo.euskadi.eus/cartografia/DatosDescarga/](https://www.geo.euskadi.eus/cartografia/DatosDescarga/)), accessed on 23 August 2022. The satellite information data can be found in [[https://scihub.copernicus.eu/dhus/#/home](https://scihub.copernicus.eu/dhus/#/home)]([https://scihub.copernicus.eu/dhus/#/home](https://scihub.copernicus.eu/dhus/#/home)) accessed on 23 August 2022.  
The authors would like to thank [PERSON], from the Union de Agricultores y Ganaderos de Alava (UAGA) and [PERSON], a farmer in the GARLAN cooperative, for providing the possibility to carry out the research in their plots and giving us yield information. The HAZI Fundazioa is thanked for its support in the project. [PERSON] is thanked for his work in reading the latest version and the improvements proposed to make the text more understandable.  
The authors declare no conflict of interest. The funders had no role in the design of the study, in the collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.