| Home | [1–10] << 11 12 13 14 15 16 17 18 19 20 >> [21–30] |
|
Kersebaum, K. C. (2015). Simulating crop rotations and management across climatic zones in Europe – an intercomparison study using fifteen models (Vol. 5).
Abstract: Process based crop simulation models are widely used to assess crop production under current or future climate conditions. Most studies on climate impacts on crop growth are so far focussed on single crops and single-year simulations. However, it is known that the position of crops within a rotation can influence crop growth significantly due to carry-over effects between seasons. We compared crop models on crop rotation effects from five sites across Central Europe providing in total data of 301 cropping seasons and treatments. Treatments comprised irrigation, nitrogen (N) fertilisation, atmospheric [CO2], tillage, residue management, cover crops and soils. Crop rotations were simulated with 15 crop models as single-year simulations and/or continuous simulations over whole crop rotations in “restricted calibration” runs. Lower RMSE between observed and simulated crop yields were obtained for continuous runs as compared to single-year runs. Relatively low carry-over effects were observed due to equilibration of soil water over winter and high N fertilisation levels. Consistently, a sub-set of models applied to an additional rainfed Mediterranean site reproduced larger carry-over effects of soil water. Irrigation, N supply, cover crops and atmospheric [CO2] showed clearer effects than tillage and crop residue management. Model performance varied distinctly between crops showing the necessity to provide experimental data for model calibration also for less prominent crops. No Label
|
|
Hoveid, Ø. (2015). An economist’s wish list for soil and crop modelling (Vol. 5).
Abstract: A requirement for successful integration of soil, crop and economic models is a relevant interface of the three. Economic farming models deal with choice of crops, crop management during growing season and stock management after harvest. With detailed daily weather information the state of the soil might be simulated so that a suitable sowing date can be estimated. Moreover with rational beliefs with respect to future crop prices, and with a crop model which responds to management, the management during the growing season might be optimized with respect to choice of cultivar, fertilization and irrigation. So far, as reflected by Müller and Robertson (2014), predictions of future crop yields according to crop models take only to small extent such farmer responses into account, and might therefore overestimate the responses of crop harvests to climate.Comparison of soil, crop and economic simulations with observed weather and crop outcomes might lead to estimation/calibration of unobserved parameters in all models. Such exercises need generic soil, crop and economic models which do not leave modelling outcomes to the crop modeller’s or economist’s discretion. No Label
|
|
Hutchings, N. (2015). A comparison of farm-scale models to estimate greenhouse gas emissions from dairy farms in Europe (Vol. 5).
Abstract: Farm-scale models quantify the cycling of nitrogen (N) and carbon (C) so are powerful tools for assessing the impact of management-related decisions on greenhouse gas (GHG) emissions, especially on dairy cattle farms, where the internal cycling is particularly important. Farm models range in focus (economic, environmental) and the detail with which they represent C and N cycling. We compared four models from this range in terms of on-farm production and emissions of GHGs, using standardized scenarios. The models compared were SFarMod, DairyWise, FarmAC and HolosNor. The scenarios compared were based on two soil types (sandy clay versus heavy clay), two roughage systems (grass only versus grass and maize), and two climate types (Eindhoven versus Santander). Standard farm characteristics were; area (50 ha), milk yield (7000 kg/head/year), fertiliser (275 kg N and 150 kg N/ha/year for grass and maize, respectively). Potential yields for grass 10t dry matter (DM)/ha/year in both areas, maize 14 t DM/ha/ year in Eindhoven and 18t DM/ha/ year in Santander. The import of animal feed and the export/import manure and forages was minimized. Similar total farm direct GHG emissions for all models disguised a variation between models in the contribution of the different on-farm sources. There were large differences between models in the predictions of indirect GHG emission from nitrate leaching. Results could be explained by differences between models in the assumptions made and detail with which underlying processes were represented. We conclude that the choice of an appropriate farm model is highly dependent upon the role it should play and the context within which it will operate, so the current diversity of farm models will continue into the future. No Label
|
|
Holman, I. (2015). Identifying where future landuse allocation in Europe is robust to climate and socio-economic uncertainty (Vol. 5).
Abstract: The spatial distribution of future European landuse will be influenced by yield changes arising from climate change and changes in profitability as a consequence of socio-economic change (arising from changing food demand; prices; technology etc). To understand how these factors affect future land use allocation, a modelling system has been set up to predict agricultural land use across the EU under any scenario set of climate and socio- and techno-economic data. Metamodels of crop and forest yields, and optimal cropping and profit are derived from the outputs of the IMPEL, GOTILWA+, SFARMODand WaterGAP models. Profitability of each possible land use is modelled across the EU, assuming that use will change to the most profitable in the timescale being considered (2050). Land use in a grid is then allocated based on profit, with minimum profit thresholds set for intensive agriculture (arable or grassland), extensive agriculture, managed forest and finally unmanaged forest or unmanaged land. The European demand for food as a function of population, imports, food preferences and bioenergy, is a production constraint, as is irrigation water available. The model iterates prices until demand is satisfied (or cannot be met) and basin water usage for irrigation is not more than is available.This presentation describes the application of the modelling system across future climate change uncertainty space (as given by 60 combinations of downscaled 10’x10’ gridded climate outputs from 5 Global Climate Models, 3 climate sensitivities and 4 emissions scenario) under both baseline and four future socio-economic scenarios to identify those areas of Europe in which the spatial allocation of agricultural landcovers are robust to this uncertainty. No Label
|
|
Hoveid, Ø. (2015). A prototype stochastic dynamic equilibrium model of the global food system (Vol. 5).
Abstract: The risks of food consumption are primarily linked to those of food production due to stochastic weather. Other sources of risk are associated with break-down of food trade or transport for weather or political reasons. Hopefully, future cures against increased risk due to climate change may be found with new agricultural technologies, systems of storage from favorable to unfavorable periods, more flexible trade-arrangements between favorable and unfavorable places. However, in the short run one has to rely on the available technology, storage facilities and trade agreements. With a realistic model of the stochastic global food system, it should be possible to measure risks of certain extreme unfavorable events.A realistic case will have countries with different climate in different growing seasons. Markets will be open for trade at a number of points per year, in which decisions of production, storage, trade and consumption can be coordinated as a static equilibrium. Determinants of this equilibrium are the weather up to this date reflected in the state of crops, the available harvested stocks and the decision-maker’s preferences. With a global stochastic process of weather, a stochastic sequence of equilibria follows. No Label
|