Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
doi:10.5194/gmd-2016-243
© Author(s) 2016. This work is distributed
under the Creative Commons Attribution 3.0 License.
Model evaluation paper
18 Oct 2016
Review status
A revision of this discussion paper was accepted for the journal Geoscientific Model Development (GMD) and is expected to appear here in due course.
The impacts of data constraints on the predictive performance of a general process-based crop model (PeakN-crop v1.0)
Silvia Caldararu1,a, Drew W. Purves1, and Matthew J. Smith1 1Microsoft Research, Cambridge, UK
aNow at Max Planck Institute for Biogeochemistry, Jena, Germany
Abstract. Improving international food security under a changing climate and increasing human population will be greatly aided by improving our ability to modify, understand and predict crop growth. What we predominantly have at our disposal are either process based models of crop physiology or statistical analyses of yield datasets, both of which suffer from various sources of error. In the current paper we present a generic process based crop model which we parametrise using a Bayesian model fitting algorithm to three different sources of data – space based vegetation indices, eddy covariance productivity measurements and regional crop yields. We show that the model parametrised without data, based on prior knowledge of the parameters can largely capture the observed behaviour but the data constrained model greatly improves both the model fit and reduces prediction uncertainty. We investigate the extent to which each dataset contributes to the model performance and show that while all data improves on the prior model fit, the satellite based data and crop yield estimates are particularly important for reducing model error and uncertainty. Despite these improvements, we conclude that there are still significant knowledge10 gaps, in terms of available data for model parametrisation, but our study can help indicate the necessary data collection steps for improvement in our predictions of crop yields and crop responses to environmental changes.

Citation: Caldararu, S., Purves, D. W., and Smith, M. J.: The impacts of data constraints on the predictive performance of a general process-based crop model (PeakN-crop v1.0), Geosci. Model Dev. Discuss., doi:10.5194/gmd-2016-243, in review, 2016.
Silvia Caldararu et al.
Silvia Caldararu et al.

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Short summary
We developed a new general model for predicting the growth and development of annual crops to help improve food security worldwide. We explore how accurately such a model can predict wheat and maize crop growth around Europe and the USA when we use commonly used public datasets to calibrate the model. Satellite measurements of crop greeness and ground measurements of carbon dioxide exchange improve prediction accuracy substantially whereas regional measurements of crop yields are less important.
We developed a new general model for predicting the growth and development of annual crops to...
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