Commercial wheat fertilization based on nitrogen nutrition index and yield forecast

This work describes the practical application on commercial wheat plots of the methodology developed and evaluated in Albacete, Spain, in the framework of the project FATIMA (http://fatima-h2020.eu/). The application considers two different methodologies for the prescription of nitrogen management prior to the flowering season, based on the diagnosis of crop nitrogen status based on nitrogen nutrition index (NNI) maps and the yield forecast spatially distributed. The NNI is the ratio between the actual nitrogen concentration (Na) over the critical nitrogen concentration (Nc) for the crop analysed (Justes et al 1997). The nitrogen uptake was determined from relationship between Nc and biomass, where biomass was estimated by a crop growth model based on the water productivity. The Na was derived from the relationship between the amount of nitrogen in the canopy, estimated from spectral vegetation index based on the Red-edge and the biomass. The knowledge about the NNI allows fertilizing at critical moments throughout the wheat campaign. The NNI maps for the analysed plots, were obtained throughout wheat development to flowering, of eight dates in the study campaign. The yield forecast is calculated through the relationship between biomass and the harvest index. The spatially distributed yield relies in the use of management zone maps (MZM) based on temporal series of remote sensing data. The MZMs were calculated for pre-flowering state to estimate yield, and capture the within-field variability of wheat production. Thus, the classical N balance model is used to calculate the N requirements at pixel scale, varying the target yield according to the MZM. The practical application was made in wheat commercial plots in the study area, analysing the performance of the proposed nitrogen fertilization strategies. The results indicated the possible optimization of the N application, maintaining or increasing the wheat productivity and reaching the higher levels of protein content in the area.

Keywords: Remote sensing, wheat, biomass, nitrogen nutrition index (NNI), fertilization.