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Author |
Watson, J.; Challinor, A.J.; Fricker, T.E.; Ferro, C.A.T. |
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Title |
Comparing the effects of calibration and climate errors on a statistical crop model and a process-based crop model |
Type |
Journal Article |
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Year |
2015 |
Publication |
Climatic Change |
Abbreviated Journal |
Clim. Change |
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Volume |
132 |
Issue |
1 |
Pages |
93-109 |
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Keywords  |
maize; yield; ensemble; impacts; design; heat |
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Abstract |
Understanding the relationship between climate and crop productivity is a key component of projections of future food production, and hence assessments of food security. Climate models and crop yield datasets have errors, but the effects of these errors on regional scale crop models is not well categorized and understood. In this study we compare the effect of synthetic errors in temperature and precipitation observations on the hindcast skill of a process-based crop model and a statistical crop model. We find that errors in temperature data have a significantly stronger influence on both models than errors in precipitation. We also identify key differences in the responses of these models to different types of input data error. Statistical and process-based model responses differ depending on whether synthetic errors are overestimates or underestimates. We also investigate the impact of crop yield calibration data on model skill for both models, using datasets of yield at three different spatial scales. Whilst important for both models, the statistical model is more strongly influenced by crop yield scale than the process-based crop model. However, our results question the value of high resolution yield data for improving the skill of crop models; we find a focus on accuracy to be more likely to be valuable. For both crop models, and for all three spatial scales of yield calibration data, we found that model skill is greatest where growing area is above 10-15 %. Thus information on area harvested would appear to be a priority for data collection efforts. These results are important for three reasons. First, understanding how different crop models rely on different characteristics of temperature, precipitation and crop yield data allows us to match the model type to the available data. Second, we can prioritize where improvements in climate and crop yield data should be directed. Third, as better climate and crop yield data becomes available, we can predict how crop model skill should improve. |
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English |
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0165-0009 1573-1480 |
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CropM, ft_macsur |
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no |
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Call Number |
MA @ admin @ |
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4546 |
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Author |
Yang, H.; Dobbie, S.; Ramirez-Villegas, J.; Feng, K.; Challinor, A.J.; Chen, B.; Gao, Y.; Lee, L.; Yin, Y.; Sun, L.; Watson, J.; Koehler, A.-K.; Fan, T.; Ghosh, S. |
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Title |
Potential negative consequences of geoengineering on crop production: A study of Indian groundnut |
Type |
Journal Article |
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Year |
2016 |
Publication |
Geophysical Research Letters |
Abbreviated Journal |
Geophys. Res. Let. |
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Volume |
43 |
Issue |
22 |
Pages |
11786-11795 |
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Keywords |
Mangrove Tidal Creek; Land-Ocean Boundary; Carbon-Dioxide; Organic-Matter; River Estuary; European Estuaries; CO2 Fluxes; NE Coast; Water; Bay; fCO(2) (water); air-water CO2 flux; Hugli Estuary; Matla Estuary; Blue Carbon; source of CO2 |
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Abstract |
Geoengineering has been proposed to stabilize global temperature, but its impacts on crop production and stability are not fully understood. A few case studies suggest that certain crops are likely to benefit from solar dimming geoengineering, yet we show that geoengineering is projected to have detrimental effects for groundnut. Using an ensemble of crop-climate model simulations, we illustrate that groundnut yields in India undergo a statistically significant decrease of up to 20% as a result of solar dimming geoengineering relative to RCP4.5. It is somewhat reassuring, however, to find that after a sustained period of 50 years of geoengineering crop yields return to the nongeoengineered values within a few years once the intervention is ceased. |
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2017-01-20 |
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English |
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0094-8276 |
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Notes |
CropM, ft_MACSUR |
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Call Number |
MA @ admin @ |
Serial |
4936 |
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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. |
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Title |
Uncertainty of wheat water use: Simulated patterns and sensitivity to temperature and CO2 |
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Journal Article |
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Year |
2016 |
Publication |
Field Crops Research |
Abbreviated Journal |
Field Crops Research |
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Volume |
198 |
Issue |
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Pages |
80-92 |
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Keywords |
Multi-model simulation; Transpiration efficiency; Water use; Uncertainty; Sensitivity |
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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. |
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2016-10-31 |
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English |
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ISSN |
0378-4290 |
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Notes |
CropM, ft_macsur |
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no |
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Call Number |
MA @ admin @ |
Serial |
4786 |
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