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Can crop-climate models be accurate and precise? A case study for wheat production in Denmark

Can crop-climate models be accurate and precise? A case study for wheat production in Denmark

Martín, Manuel Montesino-San, Olesen, Jørgen E. and Porter, John R. (2015) Can crop-climate models be accurate and precise? A case study for wheat production in Denmark. Agricultural and Forest Meteorology, 202. pp. 51-60. ISSN 0168-1923 (doi:10.1016/j.agrformet.2014.11.003)

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Abstract

Crop models, used to make projections of climate change impacts, differ greatly in structural detail. Complexity of model structure has generic effects on uncertainty and error propagation in climate change impact assessments. We applied Bayesian calibration to three distinctly different empirical and mechanistic wheat models to assess how differences in the extent of process understanding in models affects uncertainties in projected impact. Predictive power of the models was tested via both accuracy (bias) and precision (or tightness of grouping) of yield projections for extrapolated weather conditions. Yields predicted by the mechanistic model were generally more accurate than the empirical models for extrapolated conditions. This trend does not hold for all extrapolations; mechanistic and empirical models responded differently due to their sensitivities to distinct weather features. However, higher accuracy comes at the cost of precision of the mechanistic model to embrace all observations within given boundaries. The approaches showed complementarity in sensitivity to weather variables and in accuracy for different extrapolation domains. Their differences in model precision and accuracy make them suitable for generic model ensembles for near-term agricultural impact assessments of climate change.

Item Type: Article
Additional Information: [1] Acknowledgements (funding): The study was carried out within the Centre for Regional Change in the Earth System (CRES) under contract no: DSF-EnMi 09-066868 funded by the Danish Strategic Research Council.
Uncontrolled Keywords: uncertainty, model intercomparison, Bayesian approach, climate change, wheat, Denmark
Subjects: S Agriculture > SB Plant culture
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > Natural Resources Institute
Faculty of Engineering & Science > Natural Resources Institute > Ecosystem Services Research Group
Related URLs:
Last Modified: 23 May 2017 14:04
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
URI: http://gala.gre.ac.uk/id/eprint/12887

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