In West Africa, environmental conditions that are highly variable in space and time, in particular due to climate, impose heavily constrained choices on agricultural practices. At large scale, this variability is expressed by an annual rainfall distribution that ranges from more than 1200 mm in the Guinean Zone with double rainy season, to almost no rainfall in a single rainy season near the Sahara. This large spatial variability is also expressed at finer scales due to the stormy character of rainfall events. The length of the rainy seasons, the distribution of rainfall events and their intensity within the season all show that the variability is also temporally very large. During the course of the season, it is essential to take into account differences in agricultural practices while assessing potential productions at different spatial scales. The integration of the SARRA-H crop model in the spatial dynamics modelling platform Ocelet offers new opportunities for this assessment. Processes can be modelled at different spatial and temporal scales and multiple data sources, including vector and raster formats, can be managed efficiently. A working prototype has been developed and is being tested during the 2016 crop season at the AGRHYMET Regional Centre in Niamey, in the framework of its Food Security Early Warning System. The objective of this presentation is to show the crop monitoring capabilities of the spatialized version of the SARRA-H crop model that integrates common agricultural practices at the scale of West Africa.