The relative importance of rainfall, temperature and yield data for a regional-scale crop model

Watson, James and Challinor, Andrew (2013) The relative importance of rainfall, temperature and yield data for a regional-scale crop model. Agricultural and Forest Meteorology, 170 47-57. doi:10.1016/j.agrformet.2012.08.001

Author Watson, James
Challinor, Andrew
Title The relative importance of rainfall, temperature and yield data for a regional-scale crop model
Journal name Agricultural and Forest Meteorology   Check publisher's open access policy
ISSN 0168-1923
Publication date 2013-03-15
Year available 2013
Sub-type Article (original research)
DOI 10.1016/j.agrformet.2012.08.001
Open Access Status
Volume 170
Start page 47
End page 57
Total pages 11
Place of publication Amsterdam, The Netherlands
Publisher Elsevier
Language eng
Abstract When projecting future crop production, the skill of regional scale (>100. km resolution) crop models is limited by the spatial and temporal accuracy of the calibration and weather data used. The skill of climate models in reproducing surface properties such as mean temperature and rainfall patterns is of critical importance for the simulation of crop yield. However, the impact of input data errors on the skill of regional scale crop models has not been systematically quantified. We evaluate the impact of specific data error scenarios on the skill of regional scale hindcasts of groundnut yield in the Gujarat region of India, using observed input data with the GLAM crop model. Two methods were employed to introduce error into rainfall, temperature and crop yield inputs at seasonal and climatological timescales: (1) random temporal resequencing, and (2) biasing values.We find that, because the study region is rainfall limited, errors in rainfall data have the most significant impact on model skill overall. More generally, we find that errors in inter-annual variability of seasonal temperature and precipitation cause the greatest crop model error. Errors in the crop yield data used for calibration increased root mean square error by up to 143%. Given that cropping systems are subject both to a changing climate and to ongoing efforts to reduce the yield gap, both potential and actual crop productivity at the regional scale need to be measured.We identify three key endeavours that can improve the ability to assess future crop productivity at the regional scale: (i) increasingly accurate representation of inter-annual climate variability in climate models; (ii) similar studies with other crop models to identify their relative strengths in dealing with different types of climate model error; (iii) the development of techniques to assess potential and actual yields, with associated confidence ranges, at the regional scale.
Keyword Climate models
Climate variability
Crop models
Crop yield
Data quality
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: Queensland Alliance for Agriculture and Food Innovation
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Citation counts: TR Web of Science Citation Count  Cited 13 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 12 times in Scopus Article | Citations
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Created: Tue, 10 Mar 2015, 11:50:25 EST by James Watson on behalf of Centre for Plant Science