Journal article
Identifying precipitation uncertainty in crop modelling using Bayesian total error analysis
- Abstract:
-
Precipitation is an important source of soil water, which is critical to crop growth, and is therefore an important input when modelling crop growth. Although advances are continually being made in predicting and recording precipitation, input uncertainty of precipitation data is likely to influence the robustness of parameter estimate and thus the predictive accuracy in soil water and crop modelling. In this study, we use the Bayesian total error analysis (BATEA) method for the water-oriente...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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Bibliographic Details
- Publisher:
- Elsevier
- Journal:
- European Journal of Agronomy More from this journal
- Volume:
- 101
- Pages:
- 248-258
- Publication date:
- 2018-10-30
- Acceptance date:
- 2018-10-15
- DOI:
- EISSN:
-
1873-7331
- ISSN:
-
1161-0301
Item Description
- Language:
-
English
- Keywords:
- Pubs id:
-
938435
- Local pid:
-
pubs:938435
- Deposit date:
-
2021-08-10
Terms of use
- Copyright holder:
- Huang et al.
- Copyright date:
- 2018
- Rights statement:
- © 2018 Published by Elsevier B.V.
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