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Comparing the effects of calibration and climate errors on a statistical crop model and a process-based crop model

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  • James Watson
  • Andrew Challinor
  • Thomas Fricker
  • Christopher Ferro

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. Copyright The Author(s) 2015

Suggested Citation

  • James Watson & Andrew Challinor & Thomas Fricker & Christopher Ferro, 2015. "Comparing the effects of calibration and climate errors on a statistical crop model and a process-based crop model," Climatic Change, Springer, vol. 132(1), pages 93-109, September.
    • Handle: RePEc:spr:climat:v:132:y:2015:i:1:p:93-109
    DOI: 10.1007/s10584-014-1264-3
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    References listed on IDEAS

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    1. Hansen, J. W. & Jones, J. W., 2000. "Scaling-up crop models for climate variability applications," Agricultural Systems, Elsevier, vol. 65(1), pages 43-72, July.
    2. David B. Lobell & Graeme L. Hammer & Greg McLean & Carlos Messina & Michael J. Roberts & Wolfram Schlenker, 2013. "The critical role of extreme heat for maize production in the United States," Nature Climate Change, Nature, vol. 3(5), pages 497-501, May.
    3. A. J. Challinor & J. Watson & D. B. Lobell & S. M. Howden & D. R. Smith & N. Chhetri, 2014. "A meta-analysis of crop yield under climate change and adaptation," Nature Climate Change, Nature, vol. 4(4), pages 287-291, April.
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    1. Singh, Kuntal & McClean, Colin J. & Büker, Patrick & Hartley, Sue E. & Hill, Jane K., 2017. "Mapping regional risks from climate change for rainfed rice cultivation in India," Agricultural Systems, Elsevier, vol. 156(C), pages 76-84.

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