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Hidy, D., Barcza, Z., Haszpra, L., Churkina, G., Pintér, K., & Nagy, Z. (2012). Development of the Biome-BGC model for simulation of managed herbaceous ecosystems. Ecol. Model., 226, 99–119.
Abstract: Apart from measurements, numerical models are the most convenient instruments to analyze the carbon and water balance of terrestrial ecosystems and their interactions with changing environmental conditions. The process-based Biome-BGC model is widely used to simulate the storage and flux of water, carbon, and nitrogen within the vegetation, litter, and soil of unmanaged terrestrial ecosystems. Considering herbaceous vegetation related simulations with Biome-BGC, soil moisture and growing season control on ecosystem functioning is inaccurate due to the simple soil hydrology and plant phenology representation within the model. Consequently, Biome-BGC has limited applicability in herbaceous ecosystems because (1) they are usually managed; (2) they are sensitive to soil processes, most of all hydrology; and (3) their carbon balance is closely connected with the growing season length. Our aim was to improve the applicability of Biome-BGC for managed herbaceous ecosystems by implementing several new modules, including management. A new index (heatsum growing season index) was defined to accurately estimate the first and the final days of the growing season. Instead of a simple bucket soil sub-model, a multilayer soil sub-model was implemented, which can handle the processes of runoff, diffusion and percolation. A new module was implemented to simulate the ecophysiological effect of drought stress on plant mortality. Mowing and grazing modules were integrated in order to quantify the functioning of managed ecosystems. After modifications, the Biome-BGC model was calibrated and validated using eddy covariance-based measurement data collected in Hungarian managed grassland ecosystems. Model calibration was performed based on the Bayes theorem. As a result of these developments and calibration, the performance of the model was substantially improved. Comparison with measurement-based estimate showed that the start and the end of the growing season are now predicted with an average accuracy of 5 and 4 days instead of 46 and 85 days as in the original model. Regarding the different sites and modeled fluxes (gross primary production, total ecosystem respiration, evapotranspiration), relative errors were between 18-60% using the original model and 10-18% using the developed model; squares of the correlation coefficients were between 0.02-0.49 using the original model and 0.50-0.81 using the developed model. (c) 2011 Elsevier B.V. All rights reserved.
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Dono, G., Raffaele, C., Luca, G., & Roggero, P. P. (2014). Income Impacts of Climate Change: Irrigated Farming in the Mediterranean and Expected Changes in Probability of Favorable and Adverse Weather Conditions. German Journal of Agricultural Economics, 63(3), 177–186.
Abstract: EU rural development policy (RDP) regulation 1305/2013 aims to protect farmers’ incomes from ongoing change of climate variability (CCV), and the increase in frequency of adverse climatic events. An income stabilization tool (IST) is provided to compensate drastic drops in income, including those caused by climatic events. The present study examines some aspect of its application focussing on Mediterranean irrigation area where frequent water shortages may generate significant income reductions in the current climate conditions, and may be further exacerbated by climate change. This enhanced loss of income in the future would occur due to a change in climate variability. This change would appreciably reduce the probability of weather conditions that are favourable for irrigation, but would not significantly increase either the probability of unfavourable weather conditions or the magnitude of their impact. As the IST and other insurance tools that protect against adversity and catastrophic events are only activated under extreme conditions, farmers may not consider them to be suitable in dealing with the new climate regime. This would leave a portion of the financial resources allocated by the RDP unused, resulting in less support for climate change adaptation.
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Höglind, M., Thorsen, S. M., & Semenov, M. A. (2013). Assessing uncertainties in impact of climate change on grass production in Northern Europe using ensembles of global climate models. Agricultural and Forest Meteorology, 170, 103–113.
Abstract: Forage-based dairy and livestock production is the backbone of agriculture in Northern Europe in economic terms. Changes in growing conditions that affect forage grass yield may have great economic consequences. This study assessed the impact of climate change on two grass species, timothy and ryegrass, at 14 locations in Northern Europe (Iceland, Scandinavia, Baltic countries) in a near-future scenario (2040-2065) compared with the baseline period 1960-1990. Local-scale climate scenarios were based on the CMIP3 multi-model ensembles of 15 global climate models in order to quantify the uncertainty in the impacts relating to highly uncertain projections of future climate. Potential yield of timothy, the most important perennial forage grass in Northern Europe, was simulated under the assumption of optimal overwintering conditions and current CO2 level, in order to obtain an estimate of the effect of changes in summer climate per se. The risk of frost and ice damage during winter was also assessed. The simulation results demonstrated that potential grass yield will increase throughout the study area, mainly as a result of increased growing temperatures. The yield response to climate change was slightly larger in irrigated than non-irrigated conditions (14% and 11%, respectively), due to larger water deficit for the 2050 scenario. However, a geo-climatic gradient was evident, with the largest predicted yield response at western locations. A geo-climatic gradient was also revealed with respect to potential frost damage, which was predicted to increase during winter in some areas east of the Baltic Sea for timothy, and for a larger number of locations both east and west of the Baltic Sea for perennial ryegrass. The risk of frost damage in spring was predicted to increase mainly in western parts of the study area. If frost damage to perennial ryegrass increases during winter, the expected increase in winter temperature due to global warming may not necessarily improve overwintering conditions, so the growing zone may not necessarily expand to the north and east of the study area by 2050. The uncertainty in impacts was frequently, but not consistently, greater in western than eastern locations. (C) 2012 Elsevier B.V. All rights reserved.
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Siebert, S., Ewert, F., Rezaei, E. E., Kage, H., & Grass, R. (2014). Impact of heat stress on crop yield-on the importance of considering canopy temperature. Environ. Res. Lett., 9(4).
Abstract: Increasing crop productivity while simultaneously reducing the environmental footprint of crop production is considered a major challenge for the coming decades. Even short episodes of heat stress can reduce crop yield considerably causing low resource use efficiency. Studies on the impact of heat stress on crop yields over larger regions generally rely on temperatures measured by standard weather stations at 2 m height. Canopy temperatures measured in this study in field plots of rye were up to 7 degrees C higher than air temperature measured at typical weather station height with the differences in temperatures controlled by soil moisture contents. Relationships between heat stress and grain number derived from controlled environment studies were only confirmed under field conditions when canopy temperature was used to calculate stress thermal time. By using hourly mean temperatures measured by 78 weather stations located across Germany for the period 1994-2009 it is estimated, that mean yield declines in wheat due to heat stress during flowering were 0.7% when temperatures are measured at 2 m height, but yield declines increase to 22% for temperatures measured at the ground. These results suggest that canopy temperature should be simulated or estimated to reduce uncertainty in assessing heat stress impacts on crop yield.
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Rötter, R. P., Palosuo, T., Kersebaum, K. C., Angulo, C., Bindi, M., Ewert, F., et al. (2012). Simulation of spring barley yield in different climatic zones of Northern and Central Europe: A comparison of nine crop models. Field Crops Research, 133, 23–36.
Abstract: In this study, the performance of nine widely used and accessible crop growth simulation models (APES-ACE, CROPSYST, DAISY, DSSAT-CERES, FASSET, HERMES, MONICA, STICS and WOFOST) was compared during 44 growing seasons of spring barley (Hordeum vulgare L) at seven sites in Northern and Central Europe. The aims of this model comparison were to examine how different process-based crop models perform at multiple sites across Europe when applied with minimal information for model calibration of spring barley at field scale, whether individual models perform better than the multi-model mean, and what the uncertainty ranges are in simulated grain yields. The reasons for differences among the models and how results for barley compare to winter wheat are discussed. Regarding yield estimation, best performing based on the root mean square error (RMSE) were models HERMES, MONICA and WOFOST with lowest values of 1124, 1282 and 1325 (kg ha(-1)), respectively. Applying the index of agreement (IA), models WOFOST, DAISY and HERMES scored best having highest values (0.632, 0.631 and 0.585, respectively). Most models systematically underestimated yields, whereby CROPSYST showed the highest deviation as indicated by the mean bias error (MBE) (-1159 kg ha(-1)). While the wide range of simulated yields across all sites and years shows the high uncertainties in model estimates with only restricted calibration, mean predictions from the nine models agreed well with observations. Results of this paper also show that models that were more accurate in predicting phenology were not necessarily the ones better estimating grain yields. Total above-ground biomass estimates often did not follow the patterns of grain yield estimates and, thus, harvest indices were also different. Estimates of soil moisture dynamics varied greatly. In comparison, even though the growing cycle for winter wheat is several months longer than for spring barley, using RMSE and IA as indicators, models performed slightly, but not significantly, better in predicting wheat yields. Errors in reproducing crop phenology were similar, which in conjunction with the shorter growth cycle of barley has higher effects on accuracy in yield prediction. (C) 2012 Elsevier B.V. All rights reserved.
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