Abstract: We assessed the weather data aggregation effect (DAE) on the simulation of cropping systems for different crops, response variables, and production conditions. Using 13 process-based crop models and the ensemble mean, we simulated 30 yr continuous cropping systems for 2 crops (winter wheat and silage maize) under 3 production conditions for the state of North Rhine-Westphalia, Germany. The DAE was evaluated for 5 weather data resolutions (i.e. 1, 10, 25, 50, and 100 km) for 3 response variables including yield, growing season evapotranspiration, and water use efficiency. Five metrics, viz. the spatial bias (Delta), average absolute deviation (AAD), relative AAD, root mean squared error (RMSE), and relative RMSE, were used to evaluate the DAE on both the input weather data and simulated results. For weather data, we found that data aggregation narrowed the spatial variability but widened the., especially across mountainous areas. The DAE on loss of spatial heterogeneity and hotspots was stronger than on the average changes over the region. The DAE increased when coarsening the spatial resolution of the input weather data. The DAE varied considerably across different models, but changed only slightly for different production conditions and crops. We conclude that if spatially detailed information is essential for local management decision, higher resolution is desirable to adequately capture the spatial variability for heterogeneous regions. The required resolution depends on the choice of the model as well as the environmental condition of the study area.

Abstract: Many climate change studies have been carried out in different parts of the world to assess climate change vulnerability and adaptation capacity of agricultural crops for certain environments characterized from climatic, pedological and agronomical point of view. The objective of this study was to analyse the productive response of winter durum wheat and tomato to climate change and sowing/transplanting time in one of the most productive areas of Italy (i.e. Capitanata, Puglia), using CERES-Wheat and CROPGRO cropping system models. Three climatic datasets were used: i) a single dataset (50 km x 50 km) provided by the JRC European centre for the period 1975- 2005; two datasets from HadCM3 for the IPCC A2 GHG scenario for time slices with +2°C (centred over 2030-2060) and +5°C (centred over 2070-2099), respectively. All three datasets were used to generate synthetic climate series using a weather simulator (model LARS-WG). No negative yield effects of climate change were observed for winter durum wheat with delayed sowing (from 330 to 345 DOY) increasing the average dry matter grain yield under forecasted scenarios. Instead, the warmer temperatures were primarily shown to accelerate the phenology, resulting in decreased yield for tomato under the + 5°C future climate scenario. In general, under global temperature increase by 5°C, early transplanting times could minimize the negative impact of climate change on crop productivity but the intensity of this effect was not sufficient to restore the current production levels of tomato cultivated in southern Italy.

Abstract: A spatial resolution needs to be determined prior to using models to simulate crop yields at a regional scale, but a dilemma exists in compromising between different demands. A fine spatial resolution demands extensive computation load for input data assembly, model runs, and output analysis. A coarse spatial resolution could result in loss of spatial detail in variability. This paper studied the impact of spatial resolution, data aggregation and spatial heterogeneity of weather data on simulations of crop yields, thus providing guidelines for choosing a proper spatial resolution for simulations of crop yields at regional scale. Using a process-based crop model SIMPLACE (LINTUL2) and daily weather data at 1 km resolution we simulated a continuous rainfed winter wheat cropping system at the national scale of Germany. Then we aggregated the weather data to four resolutions from 10 to 100 km, repeated the simulation, compared them with the 1 km results, and correlated the difference with the intra-pixel heterogeneity quantified by an ensemble of four semivariogram models. Aggregation of weather data had small effects over regions with a flat terrain located in northern Germany, but large effects over southern regions with a complex topography. The spatial distribution of yield bias at different spatial resolutions was consistent with the intra-pixel spatial heterogeneity of the terrain and a log-log linear relationship between them was established. By using this relationship we demonstrated the way to optimize the model resolution to minimize both the number of simulation runs and the expected loss of spatial detail in variability due to aggregation effects. We concluded that a high spatial resolution is desired for regions with high spatial environmental heterogeneity, and vice versa. This calls for the development of multi-scale approaches in regional and global crop modeling. The obtained results require substantiation for other production situations, crops, output variables and for different crop models. (C) 2014 Elsevier B.V. All rights reserved.