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Schönhart, M., Schauppenlehner, T., Kuttner, M., Kirchner, M., & Schmid, E. (2016). Climate change impacts on farm production, landscape appearance, and the environment: Policy scenario results from an integrated field-farm-landscape model in Austria. Agricultural Systems, 145, 39–50.
Abstract: Climate change is among the major drivers of agricultural land use change and demands autonomous farm adaptation as well as public mitigation and adaptation policies. In this article, we present an integrated land use model (ILM) mainly combining a bio-physical model and a bio-economic farm model at field, farm and landscape levels. The ILM is applied to a cropland dominated landscape in Austria to analyze impacts of climate change and mitigation and adaptation policy scenarios on farm production as well as on the abiotic environment and biotic environment. Changes in aggregated total farm gross margins from three climate change scenarios for 2040 range between + 1% and + 5% without policy intervention” and compared to a reference situation under the current climate. Changes in aggregated gross margins are even higher if adaptation policies are in place. However, increasing productivity from climate change leads to deteriorating environmental conditions such as declining plant species richness and landscape appearance. It has to be balanced by mitigation and adaptation policies taking into account effects from the considerable spatial heterogeneity such as revealed by the ILM. (C) 2016 Elsevier Ltd. All rights reserved.
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Yin, X. G., Olesen, J. E., Wang, M., Öztürk, I., & Chen, F. (2016). Climate effects on crop yields in the Northeast Farming Region of China during 1961–2010. J. Agric. Sci., 154(07), 1190–1208.
Abstract: Crop production in the Northeast Farming Region of China (NFR) is affected considerably by variation in climatic conditions. Data on crop yield and weather conditions from a number of agro-meteorological stations in NFR were used in a mixed linear model to evaluate the impacts of climatic variables on the yield of maize (Zea mays L.), rice (Oryza sativa L.), soybean (Glycine max L. Merr.) and spring wheat (Triticum aestivum L.) in different crop growth phases. The crop growing season was divided into three growth phases based on the average crop phenological dates from records covering 1981 and 2010 at each station, comprising pre-flowering (from sowing to just prior to flowering), flowering (20 days around flowering) and post-flowering (10 days after flowering to maturity). The climatic variables were mean minimum temperature, thermal time (which is used to indicate changes in the length of growth cycles), average daily solar radiation, accumulated precipitation, aridity index (which is used to assess drought stress) and heat degree-days index (HDD) (which is used to indicate heat stress) were calculated for each growth phase and year. Over the 1961–2010 period, the minimum temperature increased significantly in each crop growth phase, the thermal time increased significantly in the pre-flowering phase of each crop and in the post-flowering phases of maize, rice and soybean, and HDD increased significantly in the pre-flowering phase of soybean and wheat. Average solar radiation decreased significantly in the pre-flowering phase of all four crops and in the flowering phase of soybean and wheat. Precipitation increased during the pre-flowering phase leading to less aridity, whereas reduced precipitation in the flowering and post-flowering phases enhanced aridity. Statistical analyses indicated that higher minimum temperature was beneficial for maize, rice and soybean yields, whereas increased temperature reduced wheat yield. Higher solar radiation in the pre-flowering phase was beneficial for maize yield, in the post-flowering phase for wheat yield, whereas higher solar radiation in the flowering phase reduced rice yield. Increased aridity in the pre-flowering and flowering phases severely reduced maize yield, higher aridity in the flowering and post-flowering phases reduced rice yield, and aridity in all growth phases reduced soybean and wheat yields. Higher HDD in all growth phases reduced maize and soybean yield and HDD in the pre-flowering phase reduced rice yield. Such effects suggest that projected future climate change may have marked effects on crop yield through effects of several climatic variables, calling for adaptation measures such as breeding and changes in crop, soil and agricultural water management.
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Mirschel, W., Barkusky, D., Hufnagel, J., Kersebaum, K. C., Nendel, C., Laacke, L., et al. (2016). Coherent multi-variable field data set of an intensive cropping system for agro-ecosystem modelling from Müncheberg, Germany. Open Data J. Agric. Res., 2(1), 1–10.
Abstract: A six-year (1993-1998) multivariable data set for a four-plot intensive crop rotation (sugar beet – winter wheat – winter barley – winter rye – catch crop) located at Leibniz Centre for Agricultural Landscape Research (ZALF) Experimental Station, Müncheberg, Germany, is documented in detail. The experiment targets crop response to water supply on sandy soils (Eutric Cambisol), applying rain-fed and irrigated treatments. Weather as well as soil and crop processes were intensively monitored and management actions were consistently recorded. The data set contains coherent data for soil (water, nitrogen contents), crop (ontogenesis, plant, tiller and ear numbers, above-ground and root biomasses, yield, carbon and nitrogen content in biomass and their fractions, sugar content in beet), weather (all standard meteorological variables) and management (soil tillage, sowing, fertilisation, irrigation, harvest). In addition, observation methods are briefly described. The data set is available via the Open Research Data Portal at ZALF Müncheberg and is published under doi:10.4228/ZALF.1992.271. The data set was used for model intercomparison within the crop modelling part (CropM) of the international FACCE MACSUR project.
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Ruiz-Ramos, M., Rodriguez, A., Dosio, A., Goodess, C. M., Harpham, C., Minguez, M. I., et al. (2016). Comparing correction methods of RCM outputs for improving crop impact projections in the Iberian Peninsula for 21st century. Clim. Change, 134(1-2), 283–297.
Abstract: Assessment of climate change impacts on crops in regions of complex orography such as the Iberian Peninsula (IP) requires climate model output which is able to describe accurately the observed climate. The high resolution of output provided by Regional Climate Models (RCMs) is expected to be a suitable tool to describe regional and local climatic features, although their simulation results may still present biases. For these reasons, we compared several post-processing methods to correct or reduce the biases of RCM simulations from the ENSEMBLES project for the IP. The bias-corrected datasets were also evaluated in terms of their applicability and consequences in improving the results of a crop model to simulate maize growth and development at two IP locations, using this crop as a reference for summer cropping systems in the region. The use of bias-corrected climate runs improved crop phenology and yield simulation overall and reduced the inter-model variability and thus the uncertainty. The number of observational stations underlying each reference observational dataset used to correct the bias affected the correction performance. Although no single technique showed to be the best one, some methods proved to be more adequate for small initial biases, while others were useful when initial biases were so large as to prevent data application for impact studies. An initial evaluation of the climate data, the bias correction/reduction method and the consequences for impact assessment would be needed to design the most robust, reduced uncertainty ensemble for a specific combination of location, crop, and crop management.
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Salo, T. J., Palosuo, T., Kersebaum, K. C., Nendel, C., Angulo, C., Ewert, F., et al. (2016). Comparing the performance of 11 crop simulation models in predicting yield response to nitrogen fertilization. J. Agric. Sci., 154(7), 1218–1240.
Abstract: Eleven widely used crop simulation models (APSIM, CERES, CROPSYST, COUP, DAISY, EPIC, FASSET, HERMES, MONICA, STICS and WOFOST) were tested using spring barley (Hordeum vulgare L.) data set under varying nitrogen (N) fertilizer rates from three experimental years in the boreal climate of Jokioinen, Finland. This is the largest standardized crop model inter-comparison under different levels of N supply to date. The models were calibrated using data from 2002 and 2008, of which 2008 included six N rates ranging from 0 to 150 kg N/ha. Calibration data consisted of weather, soil, phenology, leaf area index (LAI) and yield observations. The models were then tested against new data for 2009 and their performance was assessed and compared with both the two calibration years and the test year. For the calibration period, root mean square error between measurements and simulated grain dry matter yields ranged from 170 to 870 kg/ha. During the test year 2009, most models failed to accurately reproduce the observed low yield without N fertilizer as well as the steep yield response to N applications. The multi-model predictions were closer to observations than most single-model predictions, but multi-model mean could not correct systematic errors in model simulations. Variation in soil N mineralization and LAI development due to differences in weather not captured by the models most likely was the main reason for their unsatisfactory performance. This suggests the need for model improvement in soil N mineralization as a function of soil temperature and moisture. Furthermore, specific weather event impacts such as low temperatures after emergence in 2009, tending to enhance tillering, and a high precipitation event just before harvest in 2008, causing possible yield penalties, were not captured by any of the models compared in the current study.
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