Assessment of soil and climate change data aggregation impact on crop yield simulation: from local to regional study in NRW, Germany

Abstract

The soil and historic climate data aggregated at different scale has been applied in crop models to quantify aggregation impact on yield simulation and other model output variables in past studies. Here we have evaluated the aggregation effect of soil and future climate data sets (12 GCM - RCP scenarios) on yield simulation for the period of 2040-2070 and 2070-2100. The climate change data sets for North Rhine-Westphalia (NRW), Germany were spatially aggregated from 25 km grid cells to 50 km and 100 km. In contrast, the soil data for NRW was aggregated from 1km by selecting dominant soil type within the respective coarser grid cells (25 km, 50 km and 100 km). We have applied SIMPLACE, a crop modelling platform to simulate crop yield for winter wheat under water limited condition for all climate change scenarios. We found the application of aggregated soil and climate change data at 50 km and 100 km resolutions have simulated yield (winter wheat) differences from -6 to 4 ton ha-1 compared to yield simulated due to input data at higher resolution (25 km). Therefore, the application of coarser data (50 and 100 km) has higher impact on simulated yield difference at local level. In contrast, at the regional level for entire NRW, with input data (soil and climate) aggregated at 50 and 100 km the mean absolute simulated yield differences (bias) compared to 25 km (highest resolution data) were less than 2 ton ha-1. In addition, we found the annual variability of simulated yield was not significantly influenced by input data aggregated at coarser resolution. However, the impact assessment of aggregated data on yield simulation for other crops and regions by ensemble crop models could provide different range of aggregation effects than this study