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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Claims 5
Claim · model-written summary
Temperature data errors influenced both crop models significantly more than precipitation errors.
Verbatim
e0“errors in temperature data have a significantly stronger influence on both models than errors in precipitation”
t0“a process-based crop model and a statistical crop model”
Claim · model-written summary
Statistical and process-based model responses differed according to whether synthetic errors overestimated or underestimated inputs.
Not reportedDirection
Verbatim
e0“Statistical and process-based model responses differ depending on whether synthetic errors are overestimates or underestimates”
t0“a process-based crop model and a statistical crop model”
Claim · model-written summary
Crop yield calibration scale influenced the statistical model more strongly than the process-based model.
Verbatim
e0“the statistical model is more strongly influenced by crop yield scale than the process-based crop model”
Claim · model-written summary
Model skill was greatest where growing area exceeded 10-15% across both models and all three calibration scales.
Verbatim
e0“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 %”
Claim · model-written summary
The authors prioritize accurate yield data and harvested area information for improving crop models.
Verbatim
e0“we find a focus on accuracy to be more likely to be valuable”
e1“information on area harvested would appear to be a priority for data collection efforts”
t0“a process-based crop model and a statistical crop model”