Reference:Reference-002583: Difference between revisions

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|PublicationClusterID=0
|PublicationClusterID=0
|MS_PublicationClusterID=3005029250
|MS_PublicationClusterID=3005029250
|Semantic_ID=0423b043d31ff52779f8876a6c38e8e300e0c0b1
|Semantic_ID=0423b043d31ff52779f8876a6c38e8e300e0c0b1 |CorpusID=213271282
|CorpusID=213271282
|PublicationWhatKindOf=a module application description
|PublicationWhatKindOf=a module application description
|PublicationNrofModels=a single module
|PublicationNrofModels=a single module

Latest revision as of 06:34, 9 October 2024



Author(s) Feng, Puyu; Wang, Bin; Liu, De Li; Waters, Cathy; Xiao, Dengpan; Shi, Lijie; Yu, Qiang;
BibType journalArticle
Title Dynamic wheat yield forecasts are improved by a hybrid approach using a biophysical model and machine learning technique
Editors
Year 2020-05-01
Journal Agricultural and Forest Meteorology
Booktitle
Volume 285
Pages 107922
URL https://linkinghub.elsevier.com/retrieve/pii/S0168192320300241
DOI 10.1016/j.agrformet.2020.107922
ISBN
Note Auto downloaded ref at: 2020-06-02


 
Feature reference No
PublicationClusterID 0
MS_PublicationClusterID 3005029250
Semantic_ID 0423b043d31ff52779f8876a6c38e8e300e0c0b1
Nr of citations 117
Sort of publication a module application description
Sort of model publication a single module
Is the CSDMS HPC used No
If HPC is used, for what project was it?:
Associated simulation movie if any:


Model(s) discussed: ApsimX