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Rötter, R.P.; Palosuo, T.; Kersebaum, K.-C.; Angulo, C.; Bindi, M.; Ewert, F.; Ferrise, R.; Hlavinka, P.; Moriondo, M.; Olesen, J.E.; Takáč, J.; Trnka, M. |
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Title |
Simulation of spring barley yield in different climatic zones of Northern and Central Europe: A comparison of nine crop models |
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Journal Article |
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Year |
2012 |
Publication |
Field Crops Research |
Abbreviated Journal |
Field Crops Research |
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133 |
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23-36 |
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Keywords |
Climate; Crop growth simulation; Model comparison; Spring barley; Yield variability; Uncertainty; change impacts; nitrogen dynamics; high-temperature; soil-moisture; elevated co2; ceres-wheat; data set; growth; drought; sensitivity |
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► We compared nine crop simulation models for spring barley at seven sites in Europe. ► Applying crop models with restricted calibration leads to high uncertainties. ► Multi-crop model mean yield estimates were in good agreement with observations. ► The degree of uncertainty for simulated grain yield of barley was similar to winter wheat. ► We need more suitable data enabling us to verify different processes in the models. 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. |
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CropM, ftnotmacsur |
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4592 |
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Graef, F.; Sieber, S.; Mutabazi, K.; Asch, F.; Biesalski, H.K.; Bitegeko, J.; Bokelmann, W.; Bruentrup, M.; Dietrich, O.; Elly, N.; Fasse, A.; Germer, J.U.; Grote, U.; Herrmann, L.; Herrmann, R.; Hoffmann, H.; Kahimba, F.C.; Kaufmann, B.; Kersebaum, K.-C.; Kilembe, C.; Kimaro, A.; Kinabo, J.; König, B.; König, H.; Lana, M.; Levy, C.; Lyimo-Macha, J.; Makoko, B.; Mazoko, G.; Mbaga, S.H.; Mbogoro, W.; Milling, H.; Mtambo, K.; Mueller, J.; Mueller, C.; Mueller, K.; Nkonja, E.; Reif, C.; Ringler, C.; Ruvuga, S.; Schaefer, M.; Sikira, A.; Silayo, V.; Stahr, K.; Swai, E.; Tumbo, S.; Uckert, G. |
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Title |
Framework for participatory food security research in rural food value chains |
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Journal Article |
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2014 |
Publication |
Global Food Security |
Abbreviated Journal |
Global Food Security |
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3 |
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1 |
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8-15 |
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food security; food value chain; action research; tanzania; research framework |
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Enhancing food security for poor and vulnerable people requires adapting rural food systems to various driving factors. Food security-related research should apply participatory action research that considers the entire food value chain to ensure sustained success. This article presents a research framework that focusses on determining, prioritising, testing, adapting and disseminating food securing upgrading strategies across the multiple components of rural food value chains. These include natural resources, Food production, processing, markets, consumption and waste management. Scientists and policy makers jointly use tools developed for assessing potentials for enhancing regional food security at multiple spatial and temporal scales. The research is being conducted in Tanzania as a case study for Sub-Saharan countries and is done in close collaboration with local, regional and national stakeholders, encompassing all activities across all different food sectors. (C) 2014 Elsevier B.V. All rights reserved. |
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CropM, TradeM |
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MA @ admin @ |
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4523 |
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Schils, R.; Olesen, J.E.; Kersebaum, K.-C.; Rijk, B.; Oberforster, M.; Kalyada, V.; Khitrykau, M.; Gobin, A.; Kirchev, H.; Manolova, V.; Manolov, I.; Trnka, M.; Hlavinka, P.; Palosuo, T.; Peltonen-Sainio, P.; Jauhiainen, L.; Lorgeou, J.; Marrou, H.; Danalatos, N.; Archontoulis, S.; Fodor, N.; Spink, J.; Roggero, P.P.; Bassu, S.; Pulina, A.; Seehusen, T.; Uhlen, A.K.; Zylowska, K.; Nierobca, A.; Kozyra, J.; Silva, J.V.; Macas, B.M.; Coutinho, J.; Ion, V.; Takac, J.; Ines Minguez, M.; Eckersten, H.; Levy, L.; Herrera, J.M.; Hiltbrunner, J.; Kryvobok, O.; Kryvoshein, O.; Sylvester-Bradley, R.; Kindred, D.; Topp, C.F.E.; Boogaard, H.; de Groot, H.; Lesschen, J.P.; van Bussel, L.; Wolf, J.; Zijlstra, M.; van Loon, M.P.; van Ittersum, M.K. |
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Title |
Cereal yield gaps across Europe |
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Journal Article |
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2018 |
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European Journal of Agronomy |
Abbreviated Journal |
Europ. J. Agron. |
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101 |
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109-120 |
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Wheat, Barley, Grain maize, Crop modelling, Yield potential, Nitrogen; Nitrogen Use Efficiency; Sustainable Intensification; Climate-Change; Land-Use; Wheat; Soil; Agriculture; Impacts; Fertility; Emissions |
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Europe accounts for around 20% of the global cereal production and is a net exporter of ca. 15% of that production. Increasing global demand for cereals justifies questions as to where and by how much Europe’s production can be increased to meet future global market demands, and how much additional nitrogen (N) crops would require. The latter is important as environmental concern and legislation are equally important as production aims in Europe. Here, we used a country-by-country, bottom-up approach to establish statistical estimates of actual grain yield, and compare these to modelled estimates of potential yields for either irrigated or rainfed conditions. In this way, we identified the yield gaps and the opportunities for increased cereal production for wheat, barley and maize, which represent 90% of the cereals grown in Europe. The combined mean annual yield gap of wheat, barley, maize was 239 Mt, or 42% of the yield potential. The national yield gaps ranged between 10 and 70%, with small gaps in many north-western European countries, and large gaps in eastern and south-western Europe. Yield gaps for rainfed and irrigated maize were consistently lower than those of wheat and barley. If the yield gaps of maize, wheat and barley would be reduced from 42% to 20% of potential yields, this would increase annual cereal production by 128 Mt (39%). Potential for higher cereal production exists predominantly in Eastern Europe, and half of Europe’s potential increase is located in Ukraine, Romania and Poland. Unlocking the identified potential for production growth requires a substantial increase of the crop N uptake of 4.8 Mt. Across Europe, the average N uptake gaps, to achieve 80% of the yield potential, were 87, 77 and 43 kg N ha(-1) for wheat, barley and maize, respectively. Emphasis on increasing the N use efficiency is necessary to minimize the need for additional N inputs. Whether yield gap reduction is desirable and feasible is a matter of balancing Europe’s role in global food security, farm economic objectives and environmental targets. |
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2019-01-07 |
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1161-0301 |
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CropM, TradeM, ft_macsur |
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MA @ admin @ |
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5213 |
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Hlavinka, P.; Olesen, J.E.; Kersebaum, K.-C.; Trnka, M.; Pohankova, E.; Stella, T.; Ferrise, R.; Moriondo, M.; Hoogenbom, G.; Shelia, V.; Nendel, C.; Wimmerová, M.; Topaj, A.; Medvedev, S.; Ventrella, D.; Ruiz-Ramos, M.; Rodríguez Sánchez, A.; Takáč, J.; Patil, R.H.; Öztürk, I.; Hoffmann, M.; Gobin, A.; Rötter, R.P. |
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Modelling long term effects of cropping and managements systems on soil organic matter, C/N dynamics and crop growth |
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2017 |
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FACCE MACSUR Reports |
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10 |
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C1.3-D |
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While simulation of cropping systems over a few years might reflect well the short term effects of management and cultivation, long term effects on soil properties and their consequences for crop growth and matter fluxes are not captured. Especially the effect on soil carbon sequestration/depletion is addressed by this task. Simulations of an ensemble of crop models are performed as transient runs over a period of 120 year using observed weather from three stations in Czech Republic (1961-2010) and transient long time climate change scenarios (2011-2080) from five GCM of the CMIP5 ensemble to assess the effect of different cropping and management systems on carbon sequestration, matter fluxes and crop production in an integrative way. Two cropping systems are regarded comprising two times winter wheat, silage maize, spring barley and oilseed rape. Crop rotations differ regarding their organic input from crop residues, nitrogen fertilization and implementation of catch crops. Models are applied for two soil types with different water holding capacity. Cultivation and nutrient management is adapted using management rules related to weather and soil conditions. Data of phenology and crop yield from the region of the regarded crops were provided to calibrate the models for crops of the rotations. Twelve models were calibrated in this first step. For the transient long term runs results of four models were submitted so far. Outputs are crop yields, nitrogen uptake, soil water and mineral nitrogen contents, as well as water and nitrogen fluxes to the atmosphere and groundwater. Changes in the carbon stocks and the consequences for nitrogen mineralisation, N fertilization and emissions also considered. |
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MA @ admin @ |
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4976 |
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