Abstract: 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].

Abstract: In this study, we compare the relative importance of climate change to technological, management, price and policy changes on European arable farming systems. This required linking four models: the SIMPLACE crop growth modelling framework to calculate future yields under climate change for arable crops; the CAPRI model to estimate impacts on global agricultural markets, specifically product prices; the bio-economic farm model FSSIM to calculate the future changes in cropping patterns and farm net income at the farm and regional level; and the environmental model INTEGRATOR to calculate nitrogen (N) uptake and losses to air and water. First, the four linked models were applied to analyse the effect of climate change only or a most likely baseline (i.e. B1) scenario for 2050 as well as for two alternative scenarios with, respectively, strong (i.e. A1-b1) and weak economic growth (B2) for five regions/countries across Europe (i.e. Denmark, Flevoland, Midi Pyrenees, Zachodniopomorsld and Andalucia). These analyses Were repeated but assuming in addition to climate change impacts, also the effects of changes in technology and management on crop yields, the effects of changes in prices and policies in 2050, and the effects of all factors together. The outcomes show that the effects of climate change to 2050 result in higher farm net incomes in the Northern and Northern-Central EU regions, in practically unchanged farm net incomes in the Central and Central-Southern EU regions, and in much lower farm net incomes in Southern EU regions compared to those in the base year. Climate change in combination with improved technology and farm management and/or with price changes towards 2050 results in a higher to much higher farm net incomes. Increases in farm net income for the B1 and A1-b1 scenarios are moderately stronger than those for the B2 scenario, due to the smaller increases in product prices and/or yields for the B2 scenario. Farm labour demand slightly to moderately increases towards 2050 as related to changes in cropping patterns. Changes in N2O emissions and N leaching compared to the base year are mainly caused by changes in total N inputs from the applied fertilizers and animal manure, which in turn are influenced by changes in crop yields and cropping patterns, whereas NH3 emissions are mainly determined by assumed improvements in manure application techniques. N emissions and N leaching strongly increase in Denmark and Zachodniopomorski, slightly decrease to moderately increase in Flevoland and Midi-Pyrenees, and strongly decrease in Andalucia, except for NH3 emissions which zero to moderately decrease in Flevoland and Denmark. (C) 2015 Elsevier Ltd. All tights reserved.

Abstract: The models developed for simulating olive tree and grapevine yields were reviewed by focussing on the major limitations of these models for their application in a changing climate. Empirical models, which exploit the statistical relationship between climate and yield, and process based models, where crop behaviour is defined by a range of relationships describing the main plant processes, were considered. The results highlighted that the application of empirical models to future climatic conditions (i.e. future climate scenarios) is unreliable since important statistical approaches and predictors are still lacking. While process-based models have the potential for application in climate-change impact assessments, our analysis demonstrated how the simulation of many processes affected by warmer and CO2-enriched conditions may give rise to important biases. Conversely, some crop model improvements could be applied at this stage since specific sub-models accounting for the effect of elevated temperatures and CO2 concentration were already developed. (C) 2014 Elsevier Ltd. All rights reserved.