| Records |
| Author |
Eyshi Rezaei, E.; Webber, H.; Gaiser, T.; Naab, J.; Ewert, F. |
| Title |
Heat stress in cereals: Mechanisms and modelling |
Type |
Journal Article |
| Year |
2015 |
Publication |
European Journal of Agronomy |
Abbreviated Journal |
European Journal of Agronomy |
| Volume |
64 |
Issue |
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Pages |
98-113 |
| Keywords |
high temperature; heat stress; cereal yield; climate change impact; crop modelling; high-temperature stress; tropical maize hybrids; triticum-aestivum l; high-yielding rice; induced spikelet sterility; stem reserve mobilization; climate-change impacts; oryza-sativa l.; grain-yield; kernel set |
| Abstract |
Increased climate variability and higher mean temperatures are expected across many world regions, both of which will contribute to more frequent extreme high temperatures events. Empirical evidence increasingly shows that short episodes of high temperature experienced around flowering can have large negative impacts on cereal grain yields, a phenomenon increasingly referred to as heat stress. Crop models are currently the best tools available to investigate how crops will grow under future climatic conditions, though the need to include heat stress effects has been recognized only relatively recently. We reviewed literature on both how key crop physiological processes and the observed yields under production conditions are impacted by high temperatures occurring particularly in the flowering and grain filling phases for wheat, maize and rice. This state of the art in crop response to heat stress was then contrasted with generic approaches to simulate the impacts of high temperatures in crop growth models. We found that the observed impacts of heat stress on crop yield are the end result of the integration of many processes, not all of which will be affected by a “high temperature” regime. This complexity confirms an important role for crop models in systematizing the effects of high temperatures on many processes under a range of environments and realizations of crop phenology. Four generic approaches to simulate high temperature impacts on yield were identified: (1) empirical reduction of final yield, (2) empirical reduction in daily increment in harvest index, (3) empirical reduction in grain number, and (4) semi-deterministic models of sink and source limitation. Consideration of canopy temperature is suggested as a promising approach to concurrently account for heat and drought stress, which are likely to occur simultaneously. Improving crop models’ response to high temperature impacts on cereal yields will require experimental data representative of field production and should be designed to connect what is already known about physiological responses and observed yield impacts. (C) 2014 Elsevier B.V. All rights reserved. |
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2016-06-01 |
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English |
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1161-0301 |
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Review |
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CropM, ft_macsur |
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no |
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MA @ admin @ |
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4741 |
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Wolf, J.; Kanellopoulos, A.; Kros, J.; Webber, H.; Zhao, G.; Britz, W.; Reinds, G.J.; Ewert, F.; de Vries, W. |
| Title |
Combined analysis of climate, technological and price changes on future arable farming systems in Europe |
Type |
Journal Article |
| Year |
2015 |
Publication |
Agricultural Systems |
Abbreviated Journal |
Agricultural Systems |
| Volume |
140 |
Issue |
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Pages |
56-73 |
| Keywords |
agriculture; capri; climate change; environmental impact; farming system; fssim; integrated assessment; integrator; model linkage; n emission; price change; scenarios; simplace; technological change; crop simulation-models; agricultural land-use; integrated assessment; growth; strategies; nitrogen; soils; environment; scenarios; emissions |
| Abstract |
In this study, we compare the relative importance of climate change to technological, management, price and policy changes on European arable farming systems. This required linking four models: the SIMPLACE crop growth modelling framework to calculate future yields under climate change for arable crops; the CAPRI model to estimate impacts on global agricultural markets, specifically product prices; the bio-economic farm model FSSIM to calculate the future changes in cropping patterns and farm net income at the farm and regional level; and the environmental model INTEGRATOR to calculate nitrogen (N) uptake and losses to air and water. First, the four linked models were applied to analyse the effect of climate change only or a most likely baseline (i.e. B1) scenario for 2050 as well as for two alternative scenarios with, respectively, strong (i.e. A1-b1) and weak economic growth (B2) for five regions/countries across Europe (i.e. Denmark, Flevoland, Midi Pyrenees, Zachodniopomorsld and Andalucia). These analyses Were repeated but assuming in addition to climate change impacts, also the effects of changes in technology and management on crop yields, the effects of changes in prices and policies in 2050, and the effects of all factors together. The outcomes show that the effects of climate change to 2050 result in higher farm net incomes in the Northern and Northern-Central EU regions, in practically unchanged farm net incomes in the Central and Central-Southern EU regions, and in much lower farm net incomes in Southern EU regions compared to those in the base year. Climate change in combination with improved technology and farm management and/or with price changes towards 2050 results in a higher to much higher farm net incomes. Increases in farm net income for the B1 and A1-b1 scenarios are moderately stronger than those for the B2 scenario, due to the smaller increases in product prices and/or yields for the B2 scenario. Farm labour demand slightly to moderately increases towards 2050 as related to changes in cropping patterns. Changes in N2O emissions and N leaching compared to the base year are mainly caused by changes in total N inputs from the applied fertilizers and animal manure, which in turn are influenced by changes in crop yields and cropping patterns, whereas NH3 emissions are mainly determined by assumed improvements in manure application techniques. N emissions and N leaching strongly increase in Denmark and Zachodniopomorski, slightly decrease to moderately increase in Flevoland and Midi-Pyrenees, and strongly decrease in Andalucia, except for NH3 emissions which zero to moderately decrease in Flevoland and Denmark. (C) 2015 Elsevier Ltd. All tights reserved. |
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2015-10-12 |
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0308-521x |
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CropM |
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MA @ admin @ |
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4703 |
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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. |
| Title |
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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Pages |
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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no |
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MA @ admin @ |
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2765 |
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| Author |
Ewert, F.; Rötter, R.; Brüser, K. |
| Title |
CropM: Understanding and Modelling Impacts of Climate Change on Crop Production |
Type |
Conference Article |
| Year |
2015 |
Publication |
FACCE MACSUR Reports |
Abbreviated Journal |
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| Volume |
6 |
Issue |
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Pages |
SP6-2 |
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Key ambition:To developa shared comprehensive information system on the impacts of climate change on European crop production and food securityfirst shared pan-continental assessments and tools(Full) range of important crops and important crop rotationsImproved management and analysis of dataModel improvement (stresses and factors not yet accounted for)Advanced scaling methodsAdvanced link to farm and sector modelsComprehensive uncertainty assessment and reportingTo train integrative crop modelerData. for better understanding and modelling climate change impactEvaluation of data quality (platinum, gold, silver)Quantify data gaps for modellingEmpirical analysis of crop responses to past climate variability and changeObserved adaptation options and their efficacyEffect of extreme events (past analysis and projections)Climate change scenariosConcept for data management, data journalUncertaintyMethodology & protocols for uncertainty analysisMethodology for standardized model evaluationLocal-scale climate scenarios & uncertainties in climate projectionsBasic methodology for probabilistic assessment of CC impacts using impact response surfacesMethodology for probabilistic evaluation of alternative adaptation options Main aims in MACSUR2:Improve crop model to better capture extremesComplement knowledge from crop models with empirical crop-weather analysisConsider management variables in simulationsFull range of methods for analysing uncertainty in climate impact assessmentsEvaluate potential adaptation optionsContributing to cross-cutting issues and case studies.Further the links with other modelling activitiesLink local to European and global responses No Label |
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Brussels |
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Climate-change impacts on farming systems in the next decades: Why worry when you have CAP? A FACCE MACSUR workshop for policymakers |
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no |
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MA @ admin @ |
Serial |
2083 |
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Wallach, D.; Thorburn, P.; Asseng, S.; Challinor, A.J.; Ewert, F.; Jones, J.W.; Rötter, R.; Ruane, A. |
| Title |
Overview paper on comprehensive framework for assessment of error and uncertainty in crop model predictions |
Type |
Report |
| Year |
2016 |
Publication |
FACCE MACSUR Reports |
Abbreviated Journal |
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8 |
Issue |
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Pages |
C4.1-D |
| Keywords |
MACSUR_ACK; CropM |
| 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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