The impacts of data constraints on the predictive performance of a general process-based crop model (PeakN-crop v1.0)

Silvia Caldararu,Matthew J Smith,Drew W Purves

doi:10.5194/gmd-10-1679-2017

Abstract

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.

Highlights

Improving food security is one of the greatest challenges currently facing humanity (Schmidhuber and Tubiello, 2007; Rosegrant and Cline, 2003)
As expected, the predictive accuracy of both the wheat and maize models is improved by inferring their parameters; the root mean squared error and bias of the model predictions is reduced for predicting all empirical datasets compared to the prior model (Table 3)
We show that a process-based crop model (PeakN-crop v1.0) constrained using eddy covariance (EC) data, satellite fAPAR observations and regional yield estimates can improve model performance compared to the model run with prior parameter ranges and greatly reduces the uncertainty in model output

Summary

Introduction

Improving food security is one of the greatest challenges currently facing humanity (Schmidhuber and Tubiello, 2007; Rosegrant and Cline, 2003). The increasing and developing human population is driving up food demand and changing demand patterns. This is occurring alongside increasing anthropogenic threats to supply, such as climate change. A continual challenge when developing models is knowing the generality of their predictions, either applied to multiple crops or across different space scales and timescales (Rosenzweig et al, 2014). A challenge in developing models to help address the current food security crisis is identifying those that can be said to be generally useful over particular scales of application. We present a proof of concept that such an aim can be reached through using a process-based crop model (PeakN-crop v1.0), parametrised to available data using a model-fitting algorithm

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Conclusion