Comparing the effects of calibration and climate errors on a statistical crop model and a process-based crop model
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 cate...
| Autores principales: | , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Springer
2015
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/76592 |
| _version_ | 1855525539526213632 |
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| author | Watson, J. Challinor, Andrew J. Fricker TE Ferro CAT |
| author_browse | Challinor, Andrew J. Ferro CAT Fricker TE Watson, J. |
| author_facet | Watson, J. Challinor, Andrew J. Fricker TE Ferro CAT |
| author_sort | Watson, J. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | 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. |
| format | Journal Article |
| id | CGSpace76592 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2015 |
| publishDateRange | 2015 |
| publishDateSort | 2015 |
| publisher | Springer |
| publisherStr | Springer |
| record_format | dspace |
| spelling | CGSpace765922025-12-16T02:07:59Z Comparing the effects of calibration and climate errors on a statistical crop model and a process-based crop model Watson, J. Challinor, Andrew J. Fricker TE Ferro CAT climate change agriculture food security 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. 2015-09 2016-08-25T11:51:28Z 2016-08-25T11:51:28Z Journal Article https://hdl.handle.net/10568/76592 en Open Access application/pdf Springer Watson J, Challinor AJ, Fricker TE, Ferro CAT. 2015. Comparing the effects of calibration and climate errors on a statistical crop model and a process-based crop model. Climatic Change 132(1):93-109. |
| spellingShingle | climate change agriculture food security Watson, J. Challinor, Andrew J. Fricker TE Ferro CAT Comparing the effects of calibration and climate errors on a statistical crop model and a process-based crop model |
| title | Comparing the effects of calibration and climate errors on a statistical crop model and a process-based crop model |
| title_full | Comparing the effects of calibration and climate errors on a statistical crop model and a process-based crop model |
| title_fullStr | Comparing the effects of calibration and climate errors on a statistical crop model and a process-based crop model |
| title_full_unstemmed | Comparing the effects of calibration and climate errors on a statistical crop model and a process-based crop model |
| title_short | Comparing the effects of calibration and climate errors on a statistical crop model and a process-based crop model |
| title_sort | comparing the effects of calibration and climate errors on a statistical crop model and a process based crop model |
| topic | climate change agriculture food security |
| url | https://hdl.handle.net/10568/76592 |
| work_keys_str_mv | AT watsonj comparingtheeffectsofcalibrationandclimateerrorsonastatisticalcropmodelandaprocessbasedcropmodel AT challinorandrewj comparingtheeffectsofcalibrationandclimateerrorsonastatisticalcropmodelandaprocessbasedcropmodel AT frickerte comparingtheeffectsofcalibrationandclimateerrorsonastatisticalcropmodelandaprocessbasedcropmodel AT ferrocat comparingtheeffectsofcalibrationandclimateerrorsonastatisticalcropmodelandaprocessbasedcropmodel |