Performance of STICS Model to Predict Biomass and N Uptake of Corn and Spring Wheat In Eastern Canada.

Better sustainability of agriculture could be achieved by improving nitrogen (N) use efficiency. Crop growth models could be used as tools to test the impact of climatic conditions on crop growth under several N management practices. STICS, a crop growth simulator developed in France, can assimilate leaf area index (LAI) from remote sensing to re-initialize input parameters such as seeding date and density. In this study, results of calibration/validation of N plant uptake are presented using data from 23 year-site experiments. The four sites were located between southern Ontario and western Quebec. Data were collected between 1993 and 2008. Application of N rate ranged from 0 to 150 kg N ha-1 for spring wheat and from 0 to 250 kg N ha-1 for corn. Results confirmed that a new critical N dilution curve was needed for spring wheat. Calibration clearly demonstrated that duration between emergence and maximum LAI, as well as the lifespan of the leaf surface, was reduced for new spring wheat cultivar. Moreover, maximum nitrate uptake rate by roots was much more efficient and fast as compared to winter wheat. For corn, the default N dilution curves implemented in STICS model were kept since it was found that corn grown in Canada follows these dilution curves. Under normal climate conditions, predicted biomass and N in plants were quite high and varied according to the amount of nitrogen applied in the field. Under dry and wet climate conditions, biomass and N in plants did not clearly vary according to the amount of applied nitrogen. Model predictions for LAI, biomass, N in plants and yield for spring wheat and corn were in reasonably good agreement with measurements. STICS predicted biomass and N in plants with a mean error and a root mean square error of less than 20%.