14 research outputs found
A model for incorporating patient and stakeholder voices in a learning health care network: Washington State's Comparative Effectiveness Research Translation Network
Source rock evaluation of subsurface Devonian–Carboniferous succession based on palyno-organic facies analysis in Faghur Basin, North Western Desert of Egypt: a division of the North Africa Paleozoic Basins
Soil nitrogen mineralisation simulated by crop models across different environments and the consequences for model improvment
Soil nitrogen mineralisation simulated by crop models across different environments and the consequences for model improvment. iCROPM2016 International Crop Modelling Symposiu
A Definitive Bankruptcy and Related Subject Bibliography: From the Earliest Times to 1899 in Chronological Order
A statistical analysis of three ensembles of crop model responses to temperature and CO2 concentration
Ensembles of process-based crop models are increasingly used to simulate crop growth for scenarios of temperature and/or precipitation changes corresponding to different projections of atmospheric CO2 concentrations. This approach generates large datasets with thousands of simulated crop yield data. Such datasets potentially provide new information but it is difficult to summarize them in a useful way due to their structural complexities. An associated issue is that it is not straightforward to compare crops and to interpolate the results to alternative climate scenarios not initially included in the simulation protocols. Here we demonstrate that statistical models based on random-coefficient regressions are able to emulate ensembles of process-based crop models. An important advantage of the proposed statistical models is that they can interpolate between temperature levels and between CO2 concentration levels, and can thus be used to calculate temperature and [CO2] thresholds leading to yield loss or yield gain, without rerunning the original complex crop models. Our approach is illustrated with three yield datasets simulated by 19 maize models, 26 wheat models, and 13 rice models. Several statistical models are fitted to these datasets, and are then used to analyze the variability of the yield response to [CO2] and temperature. Based on our results, we show that, for wheat, a [CO2] increase is likely to outweigh the negative effect of a temperature increase of +2 degrees C in the considered sites. Compared to wheat, required levels of [CO2] increase are much higher for maize, and intermediate for rice. For all crops, uncertainties in simulating climate change impacts increase more with temperature than with elevated [CO2]. (C) 2015 Elsevier B.V. All rights reserved