Using Different Spatial Scales of Climate Data for Regional Climate Impact Assessment: Effect on Crop Modeling Analysis

The high vulnerability of the agricultural sector to climate conditions causes serious concern regarding climate change impacts on crop development and production, particularly in vulnerable areas like the Mediterranean Basin. Crop simulation models are the most common tools applied for the assessment of such impacts on crop development and yields, both at local and regional scales. However, the use of these models in regional impact studies requires spatial input data for weather, soil, management, etc, whose resolution could affect simulation results. Indeed, the uncertainty in projecting climate change impacts on crop phenology and yield at the regional scale is affected not only by the uncertainty related to climate models and scenarios, but also by the downscaling methods and the resolution of climate data. The aim of this study was the evaluation of the effects of spatial resolutions of climate projections in estimating maturity date and grain yield for different varieties of durum wheat, common wheat and maize in Italy. The simulations were carried out using the CSM-CERES-Wheat and CSM-CERES-Maize crop models included in the DSSAT-CSM (Decision Support System for Agrotechnology Transfer - Cropping System Model) software, parameterized and evaluated in different experimental sites located in Italy. Dynamically downscaled climate data at different resolutions and different RCP scenarios were used as input in the crop models. A spatial platform, DSSAT-CSM based, developed in R programming language was applied to perform the simulation of maturity date and grain yield for durum wheat, common wheat and maize in each grid cell. Results, analyzed at the national and regional level, will be discussed.