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 categorised. Seventy-six percent of N recommendation systems are empirical and based on spatialised 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 the integration of spatialised and non-spatialised data. Recommendation systems appeared worldwide in 2000; they were often applied in the exact 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%). However, some limitations have been identified. For example, 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-spatialised. We conclude that N recommendation systems can be improved by better data and the integration of algorithms.

Automated summary

The scoping review identified that majority of N recommendation systems are empirical and based on spatialised vegetation indices, with limited use of mechanistic crop simulation models and machine learning techniques. Although recommendation systems appeared worldwide in 2000, there are limitations that can be improved with better data and algorithm integration.