Intra-Plot Variable N Fertilization in Winter Wheat through Machine Learning and Farmer Knowledge

The variable fertilization rate (VFR) technique has demonstrated its ability to reduce nutrient losses by adapting the fertilizer dose to crop needs. However, transferring this technology to farms is not easy. This study aimed to make a variable fertilization map in a commercial plot where there is no data from a yield monitor, combining machine learning techniques and farmer's knowledge. In addition to the normalized difference vegetation index (NDVI) obtained from Sentinel-2 and a digital elevation model (DEM), information captured by a yield monitor in 2019 was used to train and validate models. Among the 15 algorithms trained, the best result was obtained by the random forest (RF), with an RMSE of 496 and R-2 of 0.90. Using the leave one out technique, the capacity to predict an entire plot was tested. Finally, the RF algorithm was tested on a 12-hectare wheat plot where no yield data were available. The novelty of this work lies in the collaborative work developed between farmers and researchers to implement the VRF technique in plots where precise yield data do not exist and in the leave one out validation. The collaboration between scientists and farmers 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.

Automated summary

This study combines machine learning techniques and farmer's knowledge to create a variable fertilization map in a commercial plot without yield monitor data. The random forest algorithm achieved the best results in predicting the entire plot using normalized difference vegetation index and digital elevation model data. The collaboration between scientists and farmers led to improved fertilization strategies and a better understanding of the effects of soil properties and plot history on yield.