|
|
Tao, F., Roetter, R. P., Palosuo, T., Hernandez Diaz-Ambrona, C. G., Ines Minguez, M., Semenov, M. A., et al. (2018). Contribution of crop model structure, parameters and climate projections to uncertainty in climate change impact assessments. Glob. Chang. Biol., 24(3), 1291–1307.
Abstract: Climate change impact assessments are plagued with uncertainties from many sources, such as climate projections or the inadequacies in structure and parameters of the impact model. Previous studies tried to account for the uncertainty from one or two of these. Here, we developed a triple-ensemble probabilistic assessment using seven crop models, multiple sets of model parameters and eight contrasting climate projections together to comprehensively account for uncertainties from these three important sources. We demonstrated the approach in assessing climate change impact on barley growth and yield at Jokioinen, Finland in the Boreal climatic zone and Lleida, Spain in the Mediterranean climatic zone, for the 2050s. We further quantified and compared the contribution of crop model structure, crop model parameters and climate projections to the total variance of ensemble output using Analysis of Variance (ANOVA). Based on the triple-ensemble probabilistic assessment, the median of simulated yield change was -4% and +16%, and the probability of decreasing yield was 63% and 31% in the 2050s, at Jokioinen and Lleida, respectively, relative to 1981-2010. The contribution of crop model structure to the total variance of ensemble output was larger than that from downscaled climate projections and model parameters. The relative contribution of crop model parameters and downscaled climate projections to the total variance of ensemble output varied greatly among the seven crop models and between the two sites. The contribution of downscaled climate projections was on average larger than that of crop model parameters. This information on the uncertainty from different sources can be quite useful for model users to decide where to put the most effort when preparing or choosing models or parameters for impact analyses. We concluded that the triple-ensemble probabilistic approach that accounts for the uncertainties from multiple important sources provide more comprehensive information for quantifying uncertainties in climate change impact assessments as compared to the conventional approaches that are deterministic or only account for the uncertainties from one or two of the uncertainty sources.
|
|
|
|
Fronzek, S., Pirttioja, N., Carter, T. R., Bindi, M., Hoffmann, H., Palosuo, T., et al. (2018). Classifying multi-model wheat yield impact response surfaces showing sensitivity to temperature and precipitation change. Agric. Syst., 159, 209–224.
Abstract: Crop growth simulation models can differ greatly in their treatment of key processes and hence in their response to environmental conditions. Here, we used an ensemble of 26 process-based wheat models applied at sites across a European transect to compare their sensitivity to changes in temperature (-2 to +9 degrees C) and precipitation (-50 to +50%). Model results were analysed by plotting them as impact response surfaces (IRSs), classifying the IRS patterns of individual model simulations, describing these classes and analysing factors that may explain the major differences in model responses. The model ensemble was used to simulate yields of winter and spring wheat at four sites in Finland, Germany and Spain. Results were plotted as IRSs that show changes in yields relative to the baseline with respect to temperature and precipitation. IRSs of 30-year means and selected extreme years were classified using two approaches describing their pattern. The expert diagnostic approach (EDA) combines two aspects of IRS patterns: location of the maximum yield (nine classes) and strength of the yield response with respect to climate (four classes), resulting in a total of 36 combined classes defined using criteria pre-specified by experts. The statistical diagnostic approach (SDA) groups IRSs by comparing their pattern and magnitude, without attempting to interpret these features. It applies a hierarchical clustering method, grouping response patterns using a distance metric that combines the spatial correlation and Euclidian distance between IRS pairs. The two approaches were used to investigate whether different patterns of yield response could be related to different properties of the crop models, specifically their genealogy, calibration and process description. Although no single model property across a large model ensemble was found to explain the integrated yield response to temperature and precipitation perturbations, the application of the EDA and SDA approaches revealed their capability to distinguish: (i) stronger yield responses to precipitation for winter wheat than spring wheat; (ii) differing strengths of response to climate changes for years with anomalous weather conditions compared to period-average conditions; (iii) the influence of site conditions on yield patterns; (iv) similarities in IRS patterns among models with related genealogy; (v) similarities in IRS patterns for models with simpler process descriptions of root growth and water uptake compared to those with more complex descriptions; and (vi) a closer correspondence of IRS patterns in models using partitioning schemes to represent yield formation than in those using a harvest index. Such results can inform future crop modelling studies that seek to exploit the diversity of multi-model ensembles, by distinguishing ensemble members that span a wide range of responses as well as those that display implausible behaviour or strong mutual similarities.
|
|
|
|
Gabaldón-Leal, C., Lorite, I. J., Mínguez, M. I., Lizaso, J. I., Dosio, A., Sanchez, E., et al. (2015). Strategies for adapting maize to climate change and extreme temperatures in Andalusia, Spain. Clim. Res., 65, 159–173.
Abstract: Climate projections indicate that rising temperatures will affect summer crops in the southern Iberian Peninsula. The aim of this study was to obtain projections of the impacts of rising temperatures, and of higher frequency of extreme events on irrigated maize, and to evaluate some adaptation strategies. The study was conducted at several locations in Andalusia using the CERES-Maize crop model, previously calibrated/validated with local experimental datasets. The simulated climate consisted of projections from regional climate models from the ENSEMBLES project; these were corrected for daily temperature and precipitation with regard to the E-OBS observational dataset. These bias-corrected projections were used with the CERES-Maize model to generate future impacts. Crop model results showed a decrease in maize yield by the end of the 21st century from 6 to 20%, a decrease of up to 25% in irrigation water requirements, and an increase in irrigation water productivity of up to 22%, due to earlier maturity dates and stomatal closure caused by CO2 increase. When adaptation strategies combining earlier sowing dates and cultivar changes were considered, impacts were compensated, and maize yield increased up to 14%, compared with the baseline period (1981-2010), with similar reductions in crop irrigation water requirements. Effects of extreme maximum temperatures rose to 40% at the end of the 21st century, compared with the baseline. Adaptation resulted in an overall reduction in extreme T-max damages in all locations, with the exception of Granada, where losses were limited to 8%.
|
|
|
|
Gomara, I., Bellocchi, G., Martin, R., Rodriguez-Fonseca, B., & Ruiz-Ramos, M. (2020). Influence of climate variability on the potential forage production of a mown permanent grassland in the French Massif Central. Agricultural and Forest Meteorology, 280, 107768.
Abstract: Climate Services (CS) provide support to decision makers across socio-economic sectors. In the agricultural sector, one of the most important CS applications is to provide timely and accurate yield forecasts based on climate prediction. In this study, the Pasture Simulation model (PaSim) was used to simulate, for the period 1959–2015, the forage production of a mown grassland system (Laqueuille, Massif Central of France) under different management conditions, with meteorological inputs extracted from the SAFRAN atmospheric database. The aim was to generate purely climate-dependent timeseries of optimal forage production, a variable that was maximized by brighter and warmer weather conditions at the grassland. A long-term increase was observed in simulated forage yield, with the 1995–2015 average being 29% higher than the 1959–1979 average. Such increase seems consistent with observed rising trends in temperature and CO2, and multi-decadal changes in incident solar radiation. At interannual timescales, sea surface temperature anomalies of the Mediterranean (MED), Tropical North Atlantic (TNA), equatorial Pacific (El Niño Southern Oscillation) and the North Atlantic Oscillation (NAO) index were found robustly correlated with annual forage yield values. Relying only on climatic predictors, we developed a stepwise statistical multi-regression model with leave-one-out cross-validation. Under specific management conditions (e.g., three annual cuts) and from one to five months in advance, the generated model successfully provided a p-value<0.01 in correlation (t-test), a root mean square error percentage (%RMSE) of 14.6% and a 71.43% hit rate predicting above/below average years in terms of forage yield collection. This is the first modeling study on the possible role of large-scale oceanic–atmospheric teleconnections in driving forage production in Europe. As such, it provides a useful springboard to implement a grassland seasonal forecasting system in this continent.
|
|
|
|
Webber, H., Ewert, F., Olesen, J. E., Müller, C., Fronzek, S., Ruane, A. C., et al. (2018). Diverging importance of drought stress for maize and winter wheat in Europe. Nat. Comm., 9, 4249.
Abstract: Understanding the drivers of yield levels under climate change is required to support adaptation planning and respond to changing production risks. This study uses an ensemble of crop models applied on a spatial grid to quantify the contributions of various climatic drivers to past yield variability in grain maize and winter wheat of European cropping systems (1984-2009) and drivers of climate change impacts to 2050. Results reveal that for the current genotypes and mix of irrigated and rainfed production, climate change would lead to yield losses for grain maize and gains for winter wheat. Across Europe, on average heat stress does not increase for either crop in rainfed systems, while drought stress intensifies for maize only. In low-yielding years, drought stress persists as the main driver of losses for both crops, with elevated CO2 offering no yield benefit in these years.
|
|
