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Francioni, M. (2017). Soil CO2 emissions and C stock as ecosystem services: a comparison between transhumant and conventional farming systems. PhD. Ph.D. thesis, Università Politecnica delle Marche, .
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Fronzek, S., Pirttioja, N., Carter, T. R., Bindi, M., Hoffmann, H., Palosuo, T., et al. (2017). Classifying multi-model wheat yield impact response surfaces showing sensitivity to temperature and precipitation change (Vol. 10).
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°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 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, Figure 1) 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. The full manuscript of this study is currently under revision (Fronzek et al. 2017).
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Gabaldón-Leal, C., Ruiz-Ramos, M., de la Rosa, R., León, L., Belaj, A., Rodríguez, A., et al. (2017). Impact of changes in mean and extreme temperatures caused by climate change on olive flowering in southern Spain: IMPACT OF CLIMATE CHANGE ON OLIVE FLOWERING IN SOUTHERN SPAIN. Int. J. Climatol., , 867.
Abstract: Due to the severe increase projected in future temperatures and the great economic and social importance of olive growing for vast agricultural areas in the Mediterranean Basin, accurate climate change impact assessment on olive orchards is required. The aim of this study is to assess the flowering date and the impact of mean and extreme temperature events on olive flowering in southern Spain under baseline and future climate conditions. To that end, experimental data were obtained from ten olive genotypes: six well-known olive cultivars in the region, one cultivar, ‘Chiquitita’, obtained via conventional breeding, and three wild olives from the Canary Islands. A site-specific model calibration was conducted resulting in satisfactory performance with an average error of 2 days for flowering date estimation under baseline and future climate conditions, and a RMSE equal to 5.5 days in the validation process. The outputs from 12 regional climate models from the ENSEMBLES European project with a bias correction in temperature and precipitation were used. Results showed an advance in the olive flowering dates of about 17 days at the end of the 21st century compared with the baseline period (1981–2010), and an increase in the frequency of extreme events around the flowering period. A spatial analysis of results identified the areas in southern Spain that are most vulnerable to climate change impact caused by the lack of chilling hours accumulation (areas located on the Atlantic coast and the south-eastern coast) and by the occurrence of high temperatures during the flowering period (areas located in the north and north-eastern areas of the Andalusian region).
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Gobin, A., Kersebaum, K., Eitzinger, J., Trnka, M., Hlavinka, P., Takáč, J., et al. (2017). Variability in the Water Footprint of Arable Crop Production across European Regions. Water, 9(2), 93.
Abstract: Crop growth and yield are affected by water use during the season: the green water footprint (WF) accounts for rain water, the blue WF for irrigation and the grey WF for diluting agri-chemicals. We calibrated crop yield for FAO’s water balance model “Aquacrop” at field level. We collected weather, soil and crop inputs for 45 locations for the period 1992–2012. Calibrated model runs were conducted for wheat, barley, grain maize, oilseed rape, potato and sugar beet. The WF of cereals could be up to 20 times larger than the WF of tuber and root crops; the largest share was attributed to the green WF. The green and blue WF compared favourably with global benchmark values (R² = 0.64–0.80; d = 0.91–0.95). The variability in the WF of arable crops across different regions in Europe is mainly due to variability in crop yield (c̅v̅ = 45%) and to a lesser extent to variability in crop water use (c̅v̅ = 21%). The WF variability between countries (c̅v̅ = 14%) is lower than the variability between seasons (c̅v̅ = 22%) and between crops (c̅v̅ = 46%). Though modelled yields increased up to 50% under sprinkler irrigation, the water footprint still increased between 1% and 25%. Confronted with drainage and runoff, the grey WF tended to overestimate the contribution of nitrogen to the surface and groundwater. The results showed that the water footprint provides a measurable indicator that may support European water governance.
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Grosz, B., Dechow, R., Gebbert, S., Hoffmann, H., Zhao, G., Constantin, J., et al. (2017). The implication of input data aggregation on up-scaling soil organic carbon changes. Env. Model. Softw., 96, 361–377.
Abstract: In up-scaling studies, model input data aggregation is a common method to cope with deficient data availability and limit the computational effort. We analyzed model errors due to soil data aggregation for modeled SOC trends. For a region in North West Germany, gridded soil data of spatial resolutions between 1 km and 100 km has been derived by majority selection. This data was used to simulate changes in SOC for a period of 30 years by 7 biogeochemical models. Soil data aggregation strongly affected modeled SOC trends. Prediction errors of simulated SOC changes decreased with increasing spatial resolution of model output. Output data aggregation only marginally reduced differences of model outputs between models indicating that errors caused by deficient model structure are likely to persist even if requirements on the spatial resolution of model outputs are low. (C)2017 Elsevier Ltd. All rights reserved.
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