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Predicting spatial and temporal variability in crop yields: an inter-comparison of machine learning, regression and process-based models

Alternative title:
Letter
Abstract:

Pervious assessments of crop yield response to climate change are mainly aided with either process-based models or statistical models, with a focus on predicting the changes in average yields, whilst there is growing interest in yield variability and extremes. In this study, we simulate US maize yield using process-based models, traditional regression model and a machine-learning algorithm, and importantly, identify the weakness and strength of each method in simulating the average, variabili...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1088/1748-9326/ab7b24

Authors


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Institution:
University of Oxford
Division:
SSD
Department:
SOGE
Sub department:
Environmental Change Institute
Role:
Author
More by this author
Institution:
University of Oxford
Division:
SSD
Department:
SOGE
Sub department:
Environmental Change Institute
Role:
Author
ORCID:
0000-0002-2024-9191
More from this funder
Name:
Wellcome Trust
Grant:
205212/Z/16/Z
Publisher:
IOP Publishing
Journal:
Environmental Research Letters More from this journal
Volume:
15
Issue:
4
Article number:
44027
Publication date:
2020-04-20
Acceptance date:
2020-02-28
DOI:
EISSN:
1748-9326
Pmid:
32395176
Language:
English
Keywords:
Pubs id:
1105993
Local pid:
pubs:1105993
Deposit date:
2020-07-31

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