| Home | [1–10] << 11 12 13 14 15 16 17 18 19 20 >> [21–21] |
|
Zhao, G., Hoffmann, H., Van Bussel, L., Enders, A., Specka, X., Sosa, C., et al. (2014). Weather data aggregation’s effects on simulation of cropping systems: a model, production system and crop comparison. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: 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.
|
|
Grosz, B., Dechow, R., Gebbert, S., Hoffmann, H., Zhao, G., Constantin, J., et al. (2017). The implication of input data aggregation on up-scaling soil organic carbon changes. Env. Model. Softw., 96, 361–377.
Abstract: 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.
|
|
Webber, H., White, J. W., Kimball, B. A., Ewert, F., Asseng, S., Rezaei, E. E., et al. (2018). Physical robustness of canopy temperature models for crop heat stress simulation across environments and production conditions. Field Crops Research, 216, 75–88.
Abstract: Despite widespread application in studying climate change impacts, most crop models ignore complex interactions among air temperature, crop and soil water status, CO2 concentration and atmospheric conditions that influence crop canopy temperature. The current study extended previous studies by evaluating Tc simulations from nine crop models at six locations across environmental and production conditions. Each crop model implemented one of an empirical (EMP), an energy balance assuming neutral stability (EBN) or an energy balance correcting for atmospheric stability conditions (EBSC) approach to simulate Tc. Model performance in predicting Tc was evaluated for two experiments in continental North America with various water, nitrogen and CO2 treatments. An empirical model fit to one dataset had the best performance, followed by the EBSC models. Stability conditions explained much of the differences between modeling approaches. More accurate simulation of heat stress will likely require use of energy balance approaches that consider atmospheric stability conditions.
Keywords: Heat stress; Crop model improvement; Heat and drought interactions; Climate change impact assessments; Canopy temperature; Wheat; Air CO2 Enrichment; Elevated Carbon-Dioxide; Water-Use Efficiency; Climate-Change; Wheat Evapotranspiration; Stomatal Conductance; Multimodel Ensembles; Farming Systems; Drought-Stress; Spring Wheat
|
|
Liu, B., Martre, P., Ewert, F., Porter, J. R., Challinor, A. J., Mueller, C., et al. (2019). Global wheat production with 1.5 and 2.0 degrees C above pre-industrial warming. Glob. Chang. Biol., 25(4), 1428–1444.
Abstract: 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.
|
|
Constantin, J., Raynal, H., Casellas, E., Hoffman, H., Bindi, M., Doro, L., et al. (2019). Management and spatial resolution effects on yield and water balance at regional scale in crop models. Agricultural and Forest Meteorology, 275, 184–195.
Abstract: Due to the more frequent use of crop models at regional and national scale, the effects of spatial data input resolution have gained increased attention. However, little is known about the influence of variability in crop management on model outputs. A constant and uniform crop management is often considered over the simulated area and period. This study determines the influence of crop management adapted to climatic conditions and input data resolution on regional-scale outputs of crop models. For this purpose, winter wheat and maize were simulated over 30 years with spatially and temporally uniform management or adaptive management for North Rhine-Westphalia ((similar to)34 083 km(2)), Germany. Adaptive management to local climatic conditions was used for 1) sowing date, 2) N fertilization dates, 3) N amounts, and 4) crop cycle length. Therefore, the models were applied with four different management sets for each crop. Input data for climate, soil and management were selected at five resolutions, from 1 x 1 km to 100 x 100 km grid size. Overall, 11 crop models were used to predict regional mean crop yield, actual evapotranspiration, and drainage. Adaptive management had little effect (< 10% difference) on the 30-year mean of the three output variables for most models and did not depend on soil, climate, and management resolution. Nevertheless, the effect was substantial for certain models, up to 31% on yield, 27% on evapotranspiration, and 12% on drainage compared to the uniform management reference. In general, effects were stronger on yield than on evapotranspiration and drainage, which had little sensitivity to changes in management. Scaling effects were generally lower than management effects on yield and evapotranspiration as opposed to drainage. Despite this trend, sensitivity to management and scaling varied greatly among the models. At the annual scale, effects were stronger in certain years, particularly the management effect on yield. These results imply that depending on the model, the representation of management should be carefully chosen, particularly when simulating yields and for predictions on annual scale.
|