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International Journal of Ecology
Volume 2012 (2012), Article ID 756242, 13 pages
Research Article

Understanding Crop Response to Climate Variability with Complex Agroecosystem Models

1Environmental Health, Agriculture and Agri-Food Canada, Lethbridge Research Centre, P.O. Box 3000, Lethbridge, AB, Canada T1J 4B1
2M.Sc. Cooperative Internship Program, Department of Statistics, University of British Columbia, Vancouver British Columbia, Canada V6T 1Z2

Received 30 September 2011; Accepted 3 November 2011

Academic Editor: Pavlos Kassomenos

Copyright © 2012 Nathaniel K. Newlands et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Agroecosystem modeling studies often rely on relatively short time-series historical records for training/tuning empirical parameters and to predict long-term variation in crop production associated with trends in climate and hydrological forcing. While ecosystem models may exhibit similar prediction skill in validation studies, their sensitivity to climate variability can differ significantly. Such discrepancy often arises due to the need to tradeoff model complexity with data availability. We examine the sensitivity in predicting spring wheat crop productivity across agricultural sites with differing soil and climate conditions where long-term agronomic and climate records are available. We report significant changes in the model sensitivity accompanying changing climatic regime. If not corrected for, this can lead to substantial predictive error when simulating across time and space. Our findings lend further support for a hierarchical (componentwise) approach for reducing model complexity and improving prediction skill.