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Kersebaum, K. C., & Nendel, C. (2014). Site-specific impacts of climate change on wheat production across regions of Germany using different CO2 response functions. European Journal of Agronomy, 52, 22–32.
Abstract: Impact of climate change on crop growth, groundwater recharge and nitrogen leaching in winter wheat production in Germany was assessed using the agro-ecosystem model HERMES with a downscaled (WETTREG) climate change scenario A1B from the ECHAM5 global circulation model. Three alternative algorithms describing the impact of atmospheric CO2 concentration on crop growth (a simple Farquhar-type algorithm, a combined light-use efficiency – maximum assimilation approach and a simple scaling of the maximum assimilation rate) in combination with a Penman-Monteith approach which includes a simple stomata conduction model for evapotranspiration under changing CO2 concentrations were compared within the framework of the HERMES model. The effect of differences in regional climate change, site conditions and different CO2 algorithms on winter wheat yield, groundwater recharge and nitrogen leaching was assessed in 22 regional simulation case studies across Germany. Results indicate that the effects of climate change on wheat production will vary across Germany due to different regional expressions of climate change projection. Predicted yield changes between the reference period (1961-1990) and a future period (2021-2050) range from -0.4 t ha(-1), -0.8 t ha(-1) and -0.6 t ha(-1) at sites in southern Germany to +0.8 t ha(-1), +0.6 t ha(-1) and +0.8 t ha(-1) at coastal regions for the three CO2 algorithms, respectively. On average across all regions, a relative yield change of +0.9%, +3.0%, and +6.0%, respectively, was predicted for Germany. In contrast, a decrease of -11.6% was predicted without the consideration of a CO2 effect. However, simulated yield changes differed even within regions as site conditions had a strong influence on crop growth. Particularly, groundwater-affected sites showed a lower vulnerability to increasing drought risk. Groundwater recharge was estimated to change correspondingly to changes in precipitation. The consideration of the CO2 effect on transpiration in the model led to a prediction of higher rates of annual deep percolation (+16 mm on average across all sites), which was due to higher water-use efficiency of the crops. In contrast to groundwater recharge, simulated nitrogen leaching varied with the choice of the photosynthesis algorithm, predicting a slight reduction in most of the areas. The results underline the necessity of high-resolution data for model-based regional climate change impact assessment and development of adaptation measures. (C) 2013 Elsevier B.V. All rights reserved.
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Hoffmann, H., Zhao, G., van Bussel, L. G. J., Enders, A., Specka, X., Sosa, C., et al. (2015). Variability of effects of spatial climate data aggregation on regional yield simulation by crop models. Clim. Res., 65, 53–69.
Abstract: Field-scale crop models are often applied at spatial resolutions coarser than that of the arable field. However, little is known about the response of the models to spatially aggregated climate input data and why these responses can differ across models. Depending on the model, regional yield estimates from large-scale simulations may be biased, compared to simulations with high-resolution input data. We evaluated this so-called aggregation effect for 13 crop models for the region of North Rhine-Westphalia in Germany. The models were supplied with climate data of 1 km resolution and spatial aggregates of up to 100 km resolution raster. The models were used with 2 crops (winter wheat and silage maize) and 3 production situations (potential, water-limited and nitrogen-water-limited growth) to improve the understanding of errors in model simulations related to data aggregation and possible interactions with the model structure. The most important climate variables identified in determining the model-specific input data aggregation on simulated yields were mainly related to changes in radiation (wheat) and temperature (maize). Additionally, aggregation effects were systematic, regardless of the extent of the effect. Climate input data aggregation changed the mean simulated regional yield by up to 0.2 t ha(-1), whereas simulated yields from single years and models differed considerably, depending on the data aggregation. This implies that large-scale crop yield simulations are robust against climate data aggregation. However, large-scale simulations can be systematically biased when being evaluated at higher temporal or spatial resolution depending on the model and its parameterization.
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Kollas, C., Kersebaum, K. C., Nendel, C., Manevski, K., Müller, C., Palosuo, T., et al. (2015). Crop rotation modelling—A European model intercomparison. European Journal of Agronomy, 70, 98–111.
Abstract: • First model inter-comparison on crop rotations. • Continuous simulation of multi-year crop rotations yields outperformed single-year simulation. • Low accuracy of yield predictions in less commonly modelled crops such as potato, radish, grass vegetation. • Multi-model mean prediction was found to minimise the likely error arising from single-model predictions. • The representation of intermediate crops and carry-over effects in the models require further research efforts.
Diversification of crop rotations is considered an option to increase the resilience of European crop production under climate change. So far, however, many crop simulation studies have focused on predicting single crops in separate one-year simulations. Here, we compared the capability of fifteen crop growth simulation models to predict yields in crop rotations at five sites across Europe under minimal calibration. Crop rotations encompassed 301 seasons of ten crop types common to European agriculture and a diverse set of treatments (irrigation, fertilisation, CO2 concentration, soil types, tillage, residues, intermediate or catch crops). We found that the continuous simulation of multi-year crop rotations yielded results of slightly higher quality compared to the simulation of single years and single crops. Intermediate crops (oilseed radish and grass vegetation) were simulated less accurately than main crops (cereals). The majority of models performed better for the treatments of increased CO2 and nitrogen fertilisation than for irrigation and soil-related treatments. The yield simulation of the multi-model ensemble reduced the error compared to single-model simulations. The low degree of superiority of continuous simulations over single year simulation was caused by (a) insufficiently parameterised crops, which affect the performance of the following crop, and (b) the lack of growth-limiting water and/or nitrogen in the crop rotations under investigation. In order to achieve a sound representation of crop rotations, further research is required to synthesise existing knowledge of the physiology of intermediate crops and of carry-over effects from the preceding to the following crop, and to implement/improve the modelling of processes that condition these effects. |
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Graß, R., Thies, B., Kersebaum, K. - C., & Wachendorf, M. (2015). Simulating dry matter yield of two cropping systems with the simulation model HERMES to evaluate impact of future climate change. European Journal of Agronomy, 70, 1–10.
Abstract: Regionalized model calculations showed increased rainfall and temperatures in winter and less precipitation and higher temperatures in summer due to climate change effects in the future for numerous countries in the northern hemisphere. Furthermore, model simulations predicted enhanced weather variability with an increased risk of yield losses and reduced yield stability. Recently, double cropping systems (DCS) were suggested as an environmental friendly and productive adaptation strategy with increased yield stability. This paper reviews the potential benefit of four DCS (rye (Secale cereale L.) as first crop and maize (Zea mays L.), sunflower (Helianthus annuus L.), sorghum (Sorghum sudanense L. x Sorghum bicolor L.) and sudan grass (S. sudanense L.) as second crops) in comparison with four conventional sole cropping systems (SCS) (maize, sunflower, sorghum and sudan grass) with regard to dry matter (DM) yield and soil water under conditions of climate change. We used the agro-ecosystem model HERMES for simulating these variables until the year 2100. The investigated crops sunflower, sorghum and sudan grass were parameterised first for HERMES achieving a satisfying performance. Results showed always higher DM yields per year of DCS compared with SCS. This was mainly caused by yield increases of the first crop winter rye harvested at the stage of milk ripeness. As a winter hardy crop, rye will benefit from increased precipitation and higher temperatures during winter months as well as from extended growth periods with an earlier onset in spring and an increase of growing days. Furthermore, rye is able to use the increased winter humidity for its spring growth in an efficient way. By contrast, model simulations showed that summer crops will be affected by reduced precipitation and higher temperatures during summer month for periods from 2050 onwards with the consequence of reduced yields. This yield reduction was found for all summer crops both in conventional sole crop and in DCS. Preponed harvesting of first crop winter rye as a consequence of earlier onset of growth period in spring under prospective climatic conditions lead to yield decrease, which could not be equalised by preponed sowing of second crops and extension of their growth period. Hence, total annual yield of both crops together decreased. The modification of sowing and harvesting dates as an adaptation strategy requires further research with the use of more holistic simulation models. To summarize, DCS may provide a promising adaptation strategy to effects of climate change with a substantial stabilisation of crop yields.
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Kersebaum, K. C., Boote, K. J., Jorgenson, J. S., Nendel, C., Bindi, M., Frühauf, C., et al. (2015). Analysis and classification of data sets for calibration and validation of agro-ecosystem models. Env. Model. Softw., 72, 402–417.
Abstract: Experimental field data are used at different levels of complexity to calibrate, validate and improve agroecosystem models to enhance their reliability for regional impact assessment. A methodological framework and software are presented to evaluate and classify data sets into four classes regarding their suitability for different modelling purposes. Weighting of inputs and variables for testing was set from the aspect of crop modelling. The software allows users to adjust weights according to their specific requirements. Background information is given for the variables with respect to their relevance for modelling and possible uncertainties. Examples are given for data sets of the different classes. The framework helps to assemble high quality data bases, to select data from data bases according to modellers requirements and gives guidelines to experimentalists for experimental design and decide on the most effective measurements to improve the usefulness of their data for modelling, statistical analysis and data assimilation. (C) 2015 Elsevier Ltd. All rights reserved.
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