|
|
Grosz, B., & Dechow, R. (2014). Comparison of measured and modelled soil organic carbon for a northern European long-term experiment site. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: Soil organic carbon is a key variable with regard to soil fertility influencing yield and yield security of agricultural crop production by regulating water budget and nutrient cycling. Those services might become even more relevant with respect to climate change. The sensitivity of crop yields on soil organic carbon content is influenced by site-specific conditions. To assess future vulnerability of yield security with respect to soil organic carbon contents in European croplands soil-crop models must consider the interaction of SOC and crop growth. Long term experiments that include treatments which lead to variable soil organic carbon contents can provide information on those relationships. Because the effect of soil fertility functions supported by SOC depends on a range of natural and anthropogenic factors we used various long term experiments in Sweden and Germany to evaluate the model CENTURY4.6. Thereafter we examined the impact of SOC on crop yields on site level by scenario runs modifying initial SOC levels and weather conditions. Preliminary results show differences in the modeled and observed soil organic carbon values for a range of observed long term experiments. The difference between modelled and measured of SOC stocks is up to 30% after 56 years. Overall, The use of the default values and setting were not appropriate to derive acceptable results, so the adjustment of some model parameter are required.
|
|
|
|
Hoffmann, H., Zhao, G., Asseng, S., Bindi, M., Biernath, C., Constantin, J., et al. (2016). Impact of spatial soil and climate input data aggregation on regional yield simulations. PLoS One, 11(4), e0151782.
Abstract: We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by <15% when aggregating only soil data. The relative mean absolute error (rMAE) of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations.
|
|
