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Zhao, G.; Hoffmann, H.; Van Bussel, L.; Enders, A.; Specka, X.; Sosa, C.; Yeluripati, J.; Tao, F.; Constantin, J.; Teixeira, E.; Grosz, B.; Doro, L.; Zhao, Z.; Nendel, C.; Raynal, H.; Eckersten, H.; Haas, E.; Kuhnert, M.; Lewan, E.; Bach, M.; Kersebaum, K.-C.; Roggero, P.P.; Rötter, R.; Wallach, D.; Krauss, G.; Siebert, S.; Gaiser, T.; Kiese, R.; Wang, E.; Ewert, F. |
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Title |
Weather data aggregation’s effects on simulation of cropping systems: a model, production system and crop comparison |
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Conference Article |
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2014 |
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Interactions of climate, soil and management practices in cropping systems can be simulated at different scales to provide information for decision making. Low resolution simulation need less effort, but important details could be lost through data aggregation effects (DAEs). This paper aims to provide a general method to assess the DAEs on weather data and the simulation of cropping systems, and further investigate how the DAEs vary with changing crop models, crops, variables and production systems. A 30-year continuous cropping system was simulated for winter wheat and silage maize and potential, water-limited and water-nitrogen-limited production situations. Climate data of 1 km resolution and aggregations to resolutions of 10 to 100 km was used as input for the simulations. The data aggregation narrowed the variation of weather data and DAEs increased with increasingly coarser spatial resolution, causing the loss of hot spots in simulated results. Spatial patterns were similar across different resolutions. Consistent with DAEs on weather data, the DAEs on simulated yield (0 to 1.2 t ha-1 for winter wheat and 0 to 1.7 t ha-1 for silage maize), evapotranspiration (3 to 45 mm yr-1 for winter wheat and 4 to 40 mm yr-1 for silage maize), and water use efficiency (0.02 to 0.25 kg m-3 for winter wheat and 0.04 to 0.4 kg m-3 for silage maize), increased with coarser spatial resolution. Thus, if spatial information is needed for local management decisions, higher resolution is needed to adequately capture the spatial heterogeneity or hot spots in the region. |
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FACCE MACSUR Mid-term Scientific Conference |
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3(S) Sassari, Italy |
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FACCE MACSUR Mid-term Scientific Conference, 2014-04-01 to 2014-04-04, Sassari, Italy |
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MA @ admin @ |
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5141 |
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Author |
Grosz, B.; Dechow, R.; Gebbert, S.; Hoffmann, H.; Zhao, G.; Constantin, J.; Raynal, H.; Wallach, D.; Coucheney, E.; Lewan, E.; Eckersten, H.; Specka, X.; Kersebaum, K.-C.; Nendel, C.; Kuhnert, M.; Yeluripati, J.; Haas, E.; Teixeira, E.; Bindi, M.; Trombi, G.; Moriondo, M.; Doro, L.; Roggero, P.P.; Zhao, Z.; Wang, E.; Tao, F.; Roetter, R.; Kassie, B.; Cammarano, D.; Asseng, S.; Weihermueller, L.; Siebert, S.; Gaiser, T.; Ewert, F. |
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Title |
The implication of input data aggregation on up-scaling soil organic carbon changes |
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Journal Article |
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Year |
2017 |
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Environmental Modelling & Software |
Abbreviated Journal |
Env. Model. Softw. |
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96 |
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361-377 |
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Biogeochemical model; Data aggregation; Up-scaling error; Soil organic carbon; DIFFERENT SPATIAL SCALES; NITROUS-OXIDE EMISSIONS; MODELING SYSTEM; DATA; RESOLUTION; CROP MODELS; CLIMATE; LONG; PRODUCTIVITY; CROPLANDS; DAYCENT |
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In up-scaling studies, model input data aggregation is a common method to cope with deficient data availability and limit the computational effort. We analyzed model errors due to soil data aggregation for modeled SOC trends. For a region in North West Germany, gridded soil data of spatial resolutions between 1 km and 100 km has been derived by majority selection. This data was used to simulate changes in SOC for a period of 30 years by 7 biogeochemical models. Soil data aggregation strongly affected modeled SOC trends. Prediction errors of simulated SOC changes decreased with increasing spatial resolution of model output. Output data aggregation only marginally reduced differences of model outputs between models indicating that errors caused by deficient model structure are likely to persist even if requirements on the spatial resolution of model outputs are low. (C)2017 Elsevier Ltd. All rights reserved. |
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2017-09-14 |
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1364-8152 |
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CropM, ft_macsur |
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MA @ admin @ |
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5176 |
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Liu, B.; Martre, P.; Ewert, F.; Porter, J.R.; Challinor, A.J.; Mueller, C.; Ruane, A.C.; Waha, K.; Thorburn, P.J.; Aggarwal, P.K.; Ahmed, M.; Balkovic, J.; Basso, B.; Biernath, C.; Bindi, M.; Cammarano, D.; De Sanctis, G.; Dumont, B.; Espadafor, M.; Rezaei, E.E.; Ferrise, R.; Garcia-Vila, M.; Gayler, S.; Gao, Y.; Horan, H.; Hoogenboom, G.; Izaurralde, R.C.; Jones, C.D.; Kassie, B.T.; Kersebaum, K.C.; Klein, C.; Koehler, A.-K.; Maiorano, A.; Minoli, S.; San Martin, M.M.; Kumar, S.N.; Nendel, C.; O’Leary, G.J.; Palosuo, T.; Priesack, E.; Ripoche, D.; Roetter, R.P.; Semenov, M.A.; Stockle, C.; Streck, T.; Supit, I.; Tao, F.; Van der Velde, M.; Wallach, D.; Wang, E.; Webber, H.; Wolf, J.; Xiao, L.; Zhang, Z.; Zhao, Z.; Zhu, Y.; Asseng, S. |
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Title |
Global wheat production with 1.5 and 2.0 degrees C above pre-industrial warming |
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Journal Article |
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Year |
2019 |
Publication |
Global Change Biology |
Abbreviated Journal |
Glob. Chang. Biol. |
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25 |
Issue |
4 |
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1428-1444 |
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Keywords |
1.5 degrees C warming; climate change; extreme low yields; food security; model ensemble; wheat production; Climate-Change; Crop Yield; Impacts; Co2; Adaptation; Responses; Models; Agriculture; Simulation; Growth |
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Efforts to limit global warming to below 2 degrees C in relation to the pre-industrial level are under way, in accordance with the 2015 Paris Agreement. However, most impact research on agriculture to date has focused on impacts of warming >2 degrees C on mean crop yields, and many previous studies did not focus sufficiently on extreme events and yield interannual variability. Here, with the latest climate scenarios from the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) project, we evaluated the impacts of the 2015 Paris Agreement range of global warming (1.5 and 2.0 degrees C warming above the pre-industrial period) on global wheat production and local yield variability. A multi-crop and multi-climate model ensemble over a global network of sites developed by the Agricultural Model Intercomparison and Improvement Project (AgMIP) for Wheat was used to represent major rainfed and irrigated wheat cropping systems. Results show that projected global wheat production will change by -2.3% to 7.0% under the 1.5 degrees C scenario and -2.4% to 10.5% under the 2.0 degrees C scenario, compared to a baseline of 1980-2010, when considering changes in local temperature, rainfall, and global atmospheric CO2 concentration, but no changes in management or wheat cultivars. The projected impact on wheat production varies spatially; a larger increase is projected for temperate high rainfall regions than for moderate hot low rainfall and irrigated regions. Grain yields in warmer regions are more likely to be reduced than in cooler regions. Despite mostly positive impacts on global average grain yields, the frequency of extremely low yields (bottom 5 percentile of baseline distribution) and yield inter-annual variability will increase under both warming scenarios for some of the hot growing locations, including locations from the second largest global wheat producer-India, which supplies more than 14% of global wheat. The projected global impact of warming <2 degrees C on wheat production is therefore not evenly distributed and will affect regional food security across the globe as well as food prices and trade. |
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2019-04-27 |
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1354-1013 |
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CropM, ft_macsur |
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MA @ admin @ |
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5219 |
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Cammarano, D.; Rötter, P.; Ewert, F.; Palosuo, T.; Bindi, M.; Kersebaum, K.C.; Olesen, J.E.; Trnka, M.; van Ittersum, M.K.; Janssen, S.; Rivington, M.; Semenov, M.; Wallach, D.; Porter, J.R.; Stewart, D.; Verhagen, J.; Angulo, C.; Gaiser, T.; Nendel, C.; Martre, P.; de Wit, A. |
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Challenges for Agro-Ecosystem Modelling in Climate Change Risk Assessment for major European Crops and Farming systems |
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Conference Article |
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2013 |
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555-564 |
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Impacts World 2013, International Conference on Climate Change Effects, Potsdam, Germany, 2013-05-27 to 2013-05-30 |
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MA @ admin @ |
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2765 |
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Wallach, D.; Thorburn, P.; Asseng, S.; Challinor, A.J.; Ewert, F.; Jones, J.W.; Rötter, R.; Ruane, A. |
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Overview paper on comprehensive framework for assessment of error and uncertainty in crop model predictions |
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Report |
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2016 |
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FACCE MACSUR Reports |
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8 |
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C4.1-D |
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MACSUR_ACK; CropM |
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Abstract |
Crop models are important tools for impact assessment of climate change, as well as for exploring management options under current climate. It is essential to evaluate the uncertainty associated with predictions of these models. Several ways of quantifying prediction uncertainty have been explored in the literature, but there have been no studies of how the different approaches are related to one another, and how they are related to some overall measure of prediction uncertainty. Here we show that all the different approaches can be related to two different viewpoints about the model; either the model is treated as a fixed predictor with some average error, or the model can be treated as a random variable with uncertainty in one or more of model structure, model inputs and model parameters. We discuss the differences, and show how mean squared error of prediction can be estimated in both cases. The results can be used to put uncertainty estimates into a more general framework and to relate different uncertainty estimates to one another and to overall prediction uncertainty. This should lead to a better understanding of crop model prediction uncertainty and the underlying causes of that uncertainty. This study was published as (Wallach et al. 2016) |
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MA @ office @ |
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2954 |
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