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Volume 10, issue 4 | Copyright
Geosci. Model Dev., 10, 1679-1701, 2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Model evaluation paper 20 Apr 2017

Model evaluation paper | 20 Apr 2017

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 Silvia Caldararu et al.
  • 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 this paper, we present a generic process-based crop model (PeakN-crop v1.0) which we parametrise using a Bayesian model-fitting algorithm to three different sources: 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 improve 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 knowledge gaps, in terms of available data for model parametrisation, but our study can help indicate the necessary data collection to improve our predictions of crop yields and crop responses to environmental changes.

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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 in Europe and the US when we use commonly used public datasets to calibrate the model. Satellite measurements of crop greenness 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...