| Records |
| Author |
Asseng, S.; Ewert, F.; Rosenzweig, C.; Jones, J.W.; Hatfield, J.L.; Ruane, A.C.; Boote, K.J.; Thorburn, P.J.; Rötter, R.P.; Cammarano, D.; Brisson, N.; Basso, B.; Martre, P.; Aggarwal, P.K.; Angulo, C.; Bertuzzi, P.; Biernath, C.; Challinor, A.J.; Doltra, J.; Gayler, S.; Goldberg, R.; Grant, R.; Heng, L.; Hooker, J.; Hunt, L.A.; Ingwersen, J.; Izaurralde, R.C.; Kersebaum, K.C.; Müller, C.; Naresh Kumar, S.; Nendel, C.; O’Leary, G.; Olesen, J.E.; Osborne, T.M.; Palosuo, T.; Priesack, E.; Ripoche, D.; Semenov, M.A.; Shcherbak, I.; Steduto, P.; Stöckle, C.; Stratonovitch, P.; Streck, T.; Supit, I.; Tao, F.; Travasso, M.; Waha, K.; Wallach, D.; White, J.W.; Williams, J.R.; Wolf, J. |
| Title |
Uncertainty in simulating wheat yields under climate change |
Type |
Journal Article |
| Year |
2013 |
Publication |
Nature Climate Change |
Abbreviated Journal |
Nat. Clim. Change |
| Volume |
3 |
Issue |
9 |
Pages |
827-832 |
| Keywords |
crop production; models; food; co2; temperature; projections; adaptation; scenarios; ensemble; impacts |
Abstract  |
Projections of climate change impacts on crop yields are inherently uncertain(1). Uncertainty is often quantified when projecting future greenhouse gas emissions and their influence on climate(2). However, multi-model uncertainty analysis of crop responses to climate change is rare because systematic and objective comparisons among process-based crop simulation models(1,3) are difficult(4). Here we present the largest standardized model intercomparison for climate change impacts so far. We found that individual crop models are able to simulate measured wheat grain yields accurately under a range of environments, particularly if the input information is sufficient. However, simulated climate change impacts vary across models owing to differences in model structures and parameter values. A greater proportion of the uncertainty in climate change impact projections was due to variations among crop models than to variations among downscaled general circulation models. Uncertainties in simulated impacts increased with CO2 concentrations and associated warming. These impact uncertainties can be reduced by improving temperature and CO2 relationships in models and better quantified through use of multi-model ensembles. Less uncertainty in describing how climate change may affect agricultural productivity will aid adaptation strategy development and policymaking. |
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| Language |
English |
Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
1758-678x |
ISBN |
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Medium |
Article |
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Expedition |
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Conference |
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| Notes |
CropM, ftnotmacsur, IPCC-AR5 |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4599 |
| Permanent link to this record |
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| Author |
Martre, P. |
| Title |
Reducing uncertainty in prediction of wheat performance under climate change |
Type |
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| Year |
2015 |
Publication |
FACCE MACSUR Reports |
Abbreviated Journal |
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| Volume |
5 |
Issue |
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Pages |
Sp5-38 |
| Keywords |
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| Abstract |
Projections of climate change impacts on crop performances are inherently uncertain. However, multimodel uncertainty analysis of crop responses is rare because systematic and objective comparisons among process-based crop simulation models are difficult. Here we report on the Agricultural Model Intercomparison and Improvement Project ensemble of 30 wheat models tested using both crop and climate observed data in diverse environments, including infra-red heating field experiments, for their accuracy in simulating multiple crop growth, N economy and yield variables. The relative error averaged over models in reproducing observations was 24-38% for the different end-of-season variables. Clusters of wheat models organized by their correlations with temperature, precipitation, and solar radiation revealed common characteristics of climatic responses; however, models are rarely in the same cluster when comparing across sites. We also found that the amount of information used for calibration has only a minor effect on model ensemble climatic responses, but can be large for any single model. When simulating impacts assuming a mid-century A2 emissions scenario for climate projections from 16 downscaled general circulation models and 26 wheat models, a greater proportion of the uncertainty in climate change impact projections was due to variations among wheat models rather than to variations among climate models. Uncertainties in simulated impacts increased with atmospheric [CO2] and associated warming. Extrapolating the model ensemble temperature response (at current atmospheric [CO2]) indicated that warming is already reducing yields at a majority of wheat-growing locations. Finally, only a very weak relationship was found between the models’ sensitivities to interannual temperature variability and their response to long-term warming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs. In conclusion, uncertainties in prediction of climate change impacts on crop performance can be reduced by improving temperature and CO2 relationships in models and are better quantified through use of impact ensembles. No Label |
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Conference |
MACSUR Science Conference 2015 »Integrated Climate Risk Assessment in Agriculture & Food«, 8–9+10 April 2015, Reading, UK |
| Notes |
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Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
2153 |
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| Author |
Rötter, R.P. |
| Title |
Cross-cutting uncertainties in MACSUR impact projections |
Type |
Conference Article |
| Year |
2014 |
Publication |
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Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Keywords |
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| Abstract |
Projections into the future, such as climate change impact projections on crop production for a given region, or, on global food prices and trade are inherently uncertain. Uncertainty does not fall within a single discipline but is dealt with by a wide variety of disciplines, themes and problem domains. Model uncertainty pertaining to the impact modelling chain from climate via crop and livestock to economic and trade modelling is only part of the overall uncertainty*. There is also scenario uncertainty and many other known and unknown “unknowns”1 to be considered in efforts such as MACSUR and its themes (CropM, LiveM, TradeM) to advance model-based integrated assessment of climate change risk assessment for agriculture and food security. Propagation of uncertainties along the climate change impact modelling chain has been portrayed as “uncertainty cascade” 2. We will present different basic approaches for evaluating uncertainty in models. So far, studies addressing quantification and reporting of uncertainties in impact projections still largely focus on two major sources, i.e. the shares originating from climate modelling and from crop modelling. However, a more comprehensive treatment of uncertainty and how it is reported is urgently needed. |
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Abbreviated Series Title |
FACCE MACSUR Mid-term Scientific Conference |
| Series Volume |
3(S) Sassari, Italy |
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Conference |
FACCE MACSUR Mid-term Scientific Conference, 2014-04-01 to 2014-04-04, Sassari, Italy |
| Notes |
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Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
5069 |
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| Author |
Cammarano, D.; Rötter, R.P.; Asseng, S.; Ewert, F.; Wallach, D.; Martre, P.; Hatfield, J.L.; Jones, J.W.; Rosenzweig, C.; Ruane, A.C.; Boote, K.J.; Thorburn, P.J.; Kersebaum, K.C.; Aggarwal, P.K.; Angulo, C.; Basso, B.; Bertuzzi, P.; Biernath, C.; Brisson, N.; Challinor, A.J.; Doltra, J.; Gayler, S.; Goldberg, R.; Heng, L.; Hooker, J.E.; Hunt, L.A.; Ingwersen, J.; Izaurralde, R.C.; Müller, C.; Kumar, S.N.; Nendel, C.; O’Leary, G.; Olesen, J.E.; Osborne, T.M.; Priesack, E.; Ripoche, D.; Steduto, P.; Stöckle, C.O.; Stratonovitch, P.; Streck, T.; Supit, I.; Tao, F.; Travasso, M.; Waha, K.; White, J.W.; Wolf, J. |
| Title |
Uncertainty of wheat water use: Simulated patterns and sensitivity to temperature and CO2 |
Type |
Journal Article |
| Year |
2016 |
Publication |
Field Crops Research |
Abbreviated Journal |
Field Crops Research |
| Volume |
198 |
Issue |
|
Pages |
80-92 |
| Keywords |
Multi-model simulation; Transpiration efficiency; Water use; Uncertainty; Sensitivity |
| Abstract |
Projected global warming and population growth will reduce future water availability for agriculture. Thus, it is essential to increase the efficiency in using water to ensure crop productivity. Quantifying crop water use (WU; i.e. actual evapotranspiration) is a critical step towards this goal. Here, sixteen wheat simulation models were used to quantify sources of model uncertainty and to estimate the relative changes and variability between models for simulated WU, water use efficiency (WUE, WU per unit of grain dry mass produced), transpiration efficiency (Teff, transpiration per kg of unit of grain yield dry mass produced), grain yield, crop transpiration and soil evaporation at increased temperatures and elevated atmospheric carbon dioxide concentrations ([CO2]). The greatest uncertainty in simulating water use, potential evapotranspiration, crop transpiration and soil evaporation was due to differences in how crop transpiration was modelled and accounted for 50% of the total variability among models. The simulation results for the sensitivity to temperature indicated that crop WU will decline with increasing temperature due to reduced growing seasons. The uncertainties in simulated crop WU, and in particularly due to uncertainties in simulating crop transpiration, were greater under conditions of increased temperatures and with high temperatures in combination with elevated atmospheric [CO2] concentrations. Hence the simulation of crop WU, and in particularly crop transpiration under higher temperature, needs to be improved and evaluated with field measurements before models can be used to simulate climate change impacts on future crop water demand. |
| Address |
2016-10-31 |
| Corporate Author |
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Thesis |
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Place of Publication |
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| Language |
English |
Summary Language |
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Original Title |
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Series Title |
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Abbreviated Series Title |
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Series Issue |
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Edition |
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| ISSN |
0378-4290 |
ISBN |
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Medium |
Article |
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Conference |
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| Notes |
CropM, ft_macsur |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4786 |
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| Author |
Knox, J. |
| Title |
Meta-analysis of recent scientific evidence on climate impacts and uncertainty on crop yields in Europe |
Type |
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| Year |
2015 |
Publication |
FACCE MACSUR Reports |
Abbreviated Journal |
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| Volume |
5 |
Issue |
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Pages |
Sp5-30 |
| Keywords |
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| Abstract |
Projected changes in temperature, rainfall and soil moisture could threaten agricultural land use and crop productivity in Europe, with major consequences for food security (Daccache et al., 2014). We assessed the projected impacts of climate change on the yield of seven crops (viz wheat, barley, maize, potato, sugar beet, rice and rye) in Europe using a systematic review (SR) and meta-analysis of data reported in 67 original publications from an initial screening of 1424 studies. Whilst similar studies exist for Africa and South Asia (Roudier et al., 2011; Knox et al., 2012), surprisingly, no such comparable synthesis has been undertaken for Europe. Our study focussed on the biophysical impacts of climate change on productivity (i.e. yield per unit area) and did not consider ‘food production’ as this is dependent on many ‘non-biophysical’ factors, such as international trade policy and world markets. The data relate to the projected mean yield variations for each crop type, for all crop models, all GCM models and all time slices.For Europe, most studies projected a positive impact on yield; the reported increases largely being due to rising atmospheric CO2 concentrations enhancing both productivity and resource use efficiencies. Overall, a mean yield increase of +14% was identified, but with large differences between individual crops (e.g. wheat +22%; potato +12%) and regions (e.g. northern Europe +17%; southern Europe +7%). It is important to note that projected yield data were not available for all crops in all regions, so lack of a significant response may in part be due to the absence, or limited number of studies for certain crops and/or regions. Furthermore, the results include all reported yield projections, for all time slices, for all GCM combinations (whether single or ensemble) and for all crop modelling approaches (whether based on simple statistical trends or more complex biophysical modelling approaches). This highlights the magnitude of variability that exists when all possible sources of uncertainty are included. Further statistical analyses were conducted to disaggregate the data by time slice, climate and crop model to identify which factors were likely to contribute most to yield variations and uncertainty.The SR showed that evidence of climate change impacts on crop yield in Europe is extensive for wheat, maize, sugar beet and potato but very limited for barley, rice and rye. Interpreting the reported yield observations was compounded by ‘effect modifiers’ or reasons for heterogeneity. These included different emission scenarios and climate ensembles, implicit assumptions regarding crop varieties, the agricultural systems studied, and assumed levels of mechanization and crop husbandry. Despite its limitations, the SR helps identify where further research should be targeted and regions where adaptation will be most needed. It confirms that climate change is likely to increase productivity of Europe’s major agricultural cropping systems, with more favourable impacts in northern and central Europe. No Label |
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Conference |
MACSUR Science Conference 2015 »Integrated Climate Risk Assessment in Agriculture & Food«, 8–9+10 April 2015, Reading, UK |
| Notes |
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Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
2145 |
| Permanent link to this record |
