On the relative importance of climatic and non-climatic factors in crop yield models

Balsher Singh Sidhu (), Zia Mehrabi (), Milind Kandlikar () and Navin Ramankutty ()
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Balsher Singh Sidhu: The University of British Columbia
Zia Mehrabi: The University of Colorado Boulder
Milind Kandlikar: The University of British Columbia
Navin Ramankutty: The University of British Columbia

Climatic Change, 2022, vol. 173, issue 1, No 8, 21 pages

Abstract: Abstract Statistical crop models, using observational data, are widely used to analyze and predict the impact of climate change on crop yields. But choices in model building can drastically influence the outcomes. Using India as a case study, we built multiple crop models (rice, wheat, and pearl millet) with different climate variables: from the simplest ones containing just space and time dummy variables, to those with seasonal mean temperature and total precipitation, to highly complex ones that accounted for within-season climate variability. We observe minimal improvement in overall model performance with increasing model complexity using standard accuracy metrics like the root mean square error and adjusted R2, suggesting the simplest models, also the most parsimonious, are often the best. However, we find that simpler models, such as those including only seasonal climate variables, fail to fully capture impacts of climate change and extreme events as they can confound the influence of climate on crop yields with space and time. Automated model and variable selection based on parsimony principles can produce predictions that are not fit for purpose. Statistical models for estimating the impacts of climate change on crop yields should therefore be based on a conjunctive use of domain theory (for example plant physiology) with accuracy and performance metrics.

Keywords: Statistical crop models; Model selection; Crop yield; Relative importance; Climate change; India (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10584-022-03404-0

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