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Author  |
Ramirez-Villegas, J.; Watson, J.; Challinor, A.J. |
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Title |
Identifying traits for genotypic adaptation using crop models |
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Journal Article |
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Year |
2015 |
Publication |
Journal of Experimental Botany |
Abbreviated Journal |
J. Experim. Bot. |
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Volume |
66 |
Issue |
12 |
Pages |
3451-3462 |
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Keywords |
Adaptation, Physiological/*genetics; Crops, Agricultural/*genetics; Environment; Genotype; *Models, Theoretical; *Quantitative Trait, Heritable; Climate change; crop models; genotypic adaptation; ideotypes; impacts |
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Abstract |
Genotypic adaptation involves the incorporation of novel traits in crop varieties so as to enhance food productivity and stability and is expected to be one of the most important adaptation strategies to future climate change. Simulation modelling can provide the basis for evaluating the biophysical potential of crop traits for genotypic adaptation. This review focuses on the use of models for assessing the potential benefits of genotypic adaptation as a response strategy to projected climate change impacts. Some key crop responses to the environment, as well as the role of models and model ensembles for assessing impacts and adaptation, are first reviewed. Next, the review describes crop-climate models can help focus the development of future-adapted crop germplasm in breeding programmes. While recently published modelling studies have demonstrated the potential of genotypic adaptation strategies and ideotype design, it is argued that, for model-based studies of genotypic adaptation to be used in crop breeding, it is critical that modelled traits are better grounded in genetic and physiological knowledge. To this aim, two main goals need to be pursued in future studies: (i) a better understanding of plant processes that limit productivity under future climate change; and (ii) a coupling between genetic and crop growth models-perhaps at the expense of the number of traits analysed. Importantly, the latter may imply additional complexity (and likely uncertainty) in crop modelling studies. Hence, appropriately constraining processes and parameters in models and a shift from simply quantifying uncertainty to actually quantifying robustness towards modelling choices are two key aspects that need to be included into future crop model-based analyses of genotypic adaptation. |
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0022-0957 1460-2431 |
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Review |
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CropM, ftnotmacsur |
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Call Number |
MA @ admin @ |
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4645 |
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Author |
Watson, J.; Challinor, A. |
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Title |
The relative importance of rainfall, temperature and yield data for a regional-scale crop model |
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Journal Article |
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Year |
2013 |
Publication |
Agricultural and Forest Meteorology |
Abbreviated Journal |
Agricultural and Forest Meteorology |
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Volume |
170 |
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Pages |
47-57 |
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0168-1923 |
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CropM, ftnotmacsur, IPCC-AR5 |
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MA @ admin @ |
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4930 |
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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 |
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Journal Article |
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Year |
2015 |
Publication |
Climatic Change |
Abbreviated Journal |
Clim. Change |
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132 |
Issue |
1 |
Pages |
93-109 |
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Keywords |
maize; yield; ensemble; impacts; design; heat |
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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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0165-0009 1573-1480 |
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CropM, ft_macsur |
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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 |
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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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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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Series Volume |
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Edition |
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ISSN |
0094-8276 |
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Notes |
CropM, ft_MACSUR |
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Call Number |
MA @ admin @ |
Serial |
4936 |
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