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Schönhart, M., Mitter, H., Schmid, E., Heinrich, G., & Gobiet, A. (2014). Integrated analysis of climate change impacts and adaptation measures in Austrian agriculture. German Journal of Agricultural Economics, 63(3), 156–176.
Abstract: An integrated modelling framework (IMF) has been developed and applied to analyse climate change impacts and the effectiveness of adaptation measures in Austrian agriculture. The IMF couples the crop rotation model CropRota, the bio-physical process model EPIC and the bottom-up economic land use model PASMA at regional level (NUTS-3) considering agri-environmental indicators. Four contrasting regional climate model (RCM) simulations represent climate change until 2050. The RCM simulations are applied to a baseline and three adaptation and policy scenarios. Climate change increases crop productivity on national average in the IMF. Changes in average gross margins at national level range from 0% to + 5% between the baseline and the three adaptation and policy scenarios. The impacts at NUTS-3 level range from -5% to + 7% between the baseline and the three adaptation and policy scenarios. Adaptation measures such as planting of winter cover crops, reduced tillage and irrigation are effective in reducing yield losses, increasing revenues, or in improving environmental states under climate change. Future research should account for extreme weather events in order to analyse whether average productivity gains at the aggregated level suffice to cover costs from expected higher climate variability.
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Ramirez-Villegas, J., Watson, J., & Challinor, A. J. (2015). Identifying traits for genotypic adaptation using crop models. J. Experim. Bot., 66(12), 3451–3462.
Abstract: Genotypic adaptation involves the incorporation of novel traits in crop varieties so as to enhance food productivity and stability and is expected to be one of the most important adaptation strategies to future climate change. Simulation modelling can provide the basis for evaluating the biophysical potential of crop traits for genotypic adaptation. This review focuses on the use of models for assessing the potential benefits of genotypic adaptation as a response strategy to projected climate change impacts. Some key crop responses to the environment, as well as the role of models and model ensembles for assessing impacts and adaptation, are first reviewed. Next, the review describes crop-climate models can help focus the development of future-adapted crop germplasm in breeding programmes. While recently published modelling studies have demonstrated the potential of genotypic adaptation strategies and ideotype design, it is argued that, for model-based studies of genotypic adaptation to be used in crop breeding, it is critical that modelled traits are better grounded in genetic and physiological knowledge. To this aim, two main goals need to be pursued in future studies: (i) a better understanding of plant processes that limit productivity under future climate change; and (ii) a coupling between genetic and crop growth models-perhaps at the expense of the number of traits analysed. Importantly, the latter may imply additional complexity (and likely uncertainty) in crop modelling studies. Hence, appropriately constraining processes and parameters in models and a shift from simply quantifying uncertainty to actually quantifying robustness towards modelling choices are two key aspects that need to be included into future crop model-based analyses of genotypic adaptation.
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Montesino-San Martín, M., Olesen, J. E., & Porter, J. R. (2014). A genotype, environment and management (GxExM) analysis of adaptation in winter wheat to climate change in Denmark. Agricultural and Forest Meteorology, 187, 1–13.
Abstract: Wheat yields in Europe have shown stagnating trends during the last two decades, partly attributed to climate change. Such developments challenge the needs for increased production, in particular at higher latitudes, to meet increasing global demands and expected productivity reductions at lower latitudes. Climate change projections from three General Circulation Models or GCMs (UKMO-HadGEM1, INM-GM3.0 and CSIRO-Mk3.1) for the A1FI SIZES emission scenario for 2000 to 2100 were downscaled at a northern latitude location (Foulum, Denmark) using LARS-WG5.3. The scenarios accounted for changes in temperature, precipitation and atmospheric CO2 concentration. In addition, three temperature-variability scenarios were included assuming different levels of decreased temperature variability in winter and increased in summer. Crop yield was simulated for the different climate change scenarios by a calibrated version of AFRCWHEAT2 to model several combinations of genotypes (varying in crop growth, development and tolerance to water and nitrogen scarcity) and management (sowing dates and nitrogen fertilization rate). The simulations showed a slight improvement of grain yields (0.3-1.2 Mg ha(-1)) in the medium-term (2030-2050), but not enough to cope with expected increases in demand for food and feed. Optimum management added up to 1.8 Mg ha(-1). Genetic modifications regarding winter wheat crop development exhibit the greatest sensitivity to climate and larger potential for improvement (+3.8 Mg ha(-1)). The results consistently points towards need for cultivars with a longer reproductive phases (2.9-7.5% per 1 degrees C) and lower photoperiod sensitivities. Due to the positive synergies between several genotypic characteristics, multiple-target breeding programmes would be necessary, possibly assisted by model-based assessments of optimal phenotypic characteristics.
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Nendel, C., Kersebaum, K. C., Mirschel, W., & Wenkel, K. O. (2014). Testing farm management options as climate change adaptation strategies using the MONICA model. European Journal of Agronomy, 52, 47–56.
Abstract: Adaptation of agriculture to climate change will be driven at the farm level in first place. The MONICA model was employed in four different modelling exercises for demonstration and testing different management options for farmers in Germany to adjust their production system. 30-Year simulations were run for the periods 1996-2025 and 2056-2085 using future climate data generated by a statistical method on the basis of measured data from 1961 to 2000 and the A1B scenario of the IPCC (2007a). Crop rotation designs that are expected to become possible in the future due to a prolonged vegetation period and at the same time shortened cereal growth period were tested for their likely success. The model suggested that a spring barley succeeding a winter barley may be successfully grown in the second half of the century, allowing for a larger yields by intensification of the cropping cycle. Growing a winter wheat after a sugar beet may lead to future problems as late sowing makes the winter wheat grow into periods prone to drought. Irrigation is projected to considerably improve and stabilise the yields of late cereals and of shallow rooting crops (maize and pea) on sandy soils in the continental climate part of Germany, but not in the humid West. Nitrogen fertiliser management needs to be adjusted to increasing or decreasing yield expectations and for decreasing soil moisture. On soils containing sufficient amounts of Moisture and soil organic matter, enhanced mineralisation is expected to compensate for a greater N demand. (C) 2012 Elsevier B.V. All rights reserved.
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Hlavinka, P., Trnka, M., Kersebaum, K. C., Cermák, P., Pohanková, E., Orság, M., et al. (2014). Modelling of yields and soil nitrogen dynamics for crop rotations by HERMES under different climate and soil conditions in the Czech Republic. J. Agric. Sci., 152(02), 188–204.
Abstract: The crop growth model HERMES was used to model crop rotation cycles at 12 experimental sites in the Czech Republic. A wide range of crops (spring and winter barley, winter wheat, maize, potatoes, sugar beet, winter rape, oats, alfalfa and grass), cultivated between 1981 and 2009 under various soil and climatic conditions, were included. The model was able to estimate the yields of field crop rotations at a reasonable level, with an index of agreement (IA) ranging from 0.82 to 0.96 for the calibration database (the median coefficient of determination (R-2) was 0.71), while IA for verification varied from 0.62 to 0.93 (median R-2 was 0.78). Grass yields were also estimated at a reasonable level of accuracy. The estimates were less accurate for the above-ground biomass at harvest (the medians for IA were 0.76 and 0.72 for calibration and verification, respectively, and analogous medians of R-2 were 0.50 and 0.49). The soil mineral nitrogen (N) content under the field crops was simulated with good precision, with the IA ranging from 0.49 to 0.74 for calibration and from 0.43 to 0.68 for verification. Generally, the soil mineral N was underestimated, and more accurate results were achieved at locations with intensive fertilization. Simulated yields, soil N, water and organic carbon (C) contents were compared with long-term field measurements at Ne. mc. ice, located within the fertile Moravian lowland. At this station, all of the observed parameters were reproduced with a reasonable level of accuracy. In the case of the organic C content, HERMES reproduced a decrease ranging from c. 85 to 77 tonnes (t)/ha (for the 0-0.3 m soil layer) between the years 1980 and 2007. In spite of its relatively simple approach and restricted input data, HERMES was proven to be robust across various conditions, which is a precondition for its future use for both theoretical and practical purposes.
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