A scoping review of side-dress nitrogen recommendation systems and their perspectives in precision agriculture

A scoping review of the relevant literature was carried out to identify the existing N recommendation systems, their temporal and geographical diffusion, and knowledge gaps. In total, 151 studies were identified and categorized. Seventy-six percent of N recommendation systems are empirical and based on spatialized vegetation indices (73% of them); 21% are based on mechanistic crop simulation models with limited use of spatialized data (26% of them); 3% are based on machine learning techniques with integration of spatialized and non-spatialized data. Recommendation systems started to appear worldwide in 2000; often they were applied in the same location where calibration had been carried out. Thirty percent of the studies use advanced recommendation techniques, such as sensor/approach fusion (44%), algorithm add-ons (30%), estimation of environmental benefits (13%), and multi-objective decisions (13%). Some limitations have been identified. Empirical systems need specific calibrations for each site, species and sensor, rarely using soil, vegetation and weather data together, while mechanistic systems need large input data sets, often non-spatialized. We conclude that N recommendation systems can be improved by better data and the integration of algorithms.