Journal article
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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Authors
Funding
Bibliographic Details
- Publisher:
- IOP Publishing Publisher's website
- Journal:
- Environmental Research Letters Journal website
- Volume:
- 15
- Issue:
- 4
- Article number:
- 44027
- Publication date:
- 2020-04-20
- Acceptance date:
- 2020-02-28
- DOI:
- EISSN:
-
1748-9326
- Pmid:
-
32395176
Item Description
- Language:
- English
- Keywords:
- Pubs id:
-
1105993
- Local pid:
- pubs:1105993
- Deposit date:
- 2020-07-31
Terms of use
- Copyright holder:
- Leng, G and Hall, JW
- Copyright date:
- 2020
- Rights statement:
- © 2020 The Author(s). Published by IOP Publishing Ltd. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
- Licence:
- CC Attribution (CC BY)
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