Application of a satellite based approach to monitor rice nitrogen status and to support precision agriculture techniques

Nitrogen (N) fertilization plays a key role in rice productivity and environmental impact of rice-based cropping systems, as well as on farmers' income, representing one of the main cost items of rice farming. N use efficiency in rice paddies is often very low (about 30%) and operational tools and techniques able to increase N use efficiency can help farmers. Variable rate (VR) fertilization is considered a promising approach to face some of the criticalities involved with N use efficiency (Basso et al., 2016) by providing maps of N to be applied according to crop needs. To perform this action, it is necessary to assess the actual N nutritional status of the crop in relation to the phenological stage. Among the available approaches, Lemaire et al. (2008) proposed the use of N Nutritional Index (NNI) as a valuable indicator of crop condition. NNI is in fact the ratio between actual plant nitrogen content (PNC, %) and critical plant nitrogen concentration (Nc, %) as a function of crop biomass. However, the application of NNI in real case condition can be limited by the need of destructive field data. As a potential solution, it is possible to exploit earth-observation (EO) data for the indirect assessment of the crop variables. This approach is the base of the French system for wheat fertilization support FARMSTAR (Blondlot et al., 2005). Other authors implemented such approach by calibrating Vegetation Indices (VIs) maps, derived from satellite imagery, with field observation in order to create crop parameters maps (Huang et al., 2015). This approach resulted efficient but requires time-consuming field activities. New alternative approach was recently proposed to get field data, needed for NNI computation (LAI and PNC), in a quick and inexpensive way using sensors available on smartphones (Confalonieri et al., 2015). Starting from these experiences, we developed an operational workflow devoted to generate NNI maps by exploiting EO-based smart scouting to drive and optimize field measurements to collect relevant field data with smartphone apps (Nutini et al., 2018). The present contribution describes the fundamental steps of the method and its application in the 2018 rice season as a support for site-specific fertilization in precision farming contexts.