Deutsch
 
Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

 
 
DownloadE-Mail
  Choosing multiple linear regressions for weather-based crop yield prediction with ABSOLUT v1.2 applied to the districts of Germany

Conradt, T. (2022): Choosing multiple linear regressions for weather-based crop yield prediction with ABSOLUT v1.2 applied to the districts of Germany. - International Journal of Biometeorology, 66, 11, 2287-2300.
https://doi.org/10.1007/s00484-022-02356-5

Item is

Dateien

einblenden: Dateien
ausblenden: Dateien
:
27363oa.pdf (Verlagsversion), 6MB
Name:
27363oa.pdf
Beschreibung:
-
Sichtbarkeit:
Öffentlich
MIME-Typ / Prüfsumme:
application/pdf / [MD5]
Technische Metadaten:
Copyright Datum:
-
Copyright Info:
-
:
absolut_directions_for_use_v1.2.pdf (Ergänzendes Material), 117KB
Name:
absolut_directions_for_use_v1.2.pdf
Beschreibung:
-
Sichtbarkeit:
Öffentlich
MIME-Typ / Prüfsumme:
application/pdf / [MD5]
Technische Metadaten:
Copyright Datum:
-
Copyright Info:
-

Externe Referenzen

einblenden:
ausblenden:
externe Referenz:
https://doi.org/10.5281/zenodo.5625774 (Ergänzendes Material)
Beschreibung:
These are data for running the ABSOLUT R programs published separately on Zenodo: 10.5281/zenodo.4468608
externe Referenz:
https://doi.org/10.5281/zenodo.5789350 (Ergänzendes Material)
Beschreibung:
These are the five R programs of the ABSOLUT crop yield model, version 1.2.

Urheber

einblenden:
ausblenden:
 Urheber:
Conradt, Tobias1, Autor              
Affiliations:
1Potsdam Institute for Climate Impact Research, ou_persistent13              

Inhalt

einblenden:
ausblenden:
Schlagwörter: -
 Zusammenfassung: ABSOLUT v1.2 is an adaptive algorithm that uses correlations between time-aggregated weather variables and crop yields for yield prediction. In contrast to conventional regression-based yield prediction methods, a very broad range of possible input features and their combinations are exhaustively tested for maximum explanatory power. Weather variables such as temperature, precipitation, and sunshine duration are aggregated over different seasonal time periods preceding the harvest to 45 potential input features per original variable. In a first step, this large set of features is reduced to those aggregates very probably holding explanatory power for observed yields. The second, computationally demanding step evaluates predictions for all districts with all of their possible combinations. Step three selects those combinations of weather features that showed the highest predictive power across districts. Finally, the district-specific best performing regressions among these are used for actual prediction, and the results are spatially aggregated. To evaluate the new approach, ABSOLUT v1.2 is applied to predict the yields of silage maize, winter wheat, and other major crops in Germany based on two decades of data from about 300 districts. It turned out to be absolutely crucial to not only make out-of-sample predictions (solely based on data excluding the target year to predict) but to also consequently separate training and testing years in the process of feature selection. Otherwise, the prediction accuracy would be over-estimated by far. The question arises whether performances claimed for other statistical modelling examples are often upward-biased through input variable selection disregarding the out-of-sample principle.

Details

einblenden:
ausblenden:
Sprache(n): eng - Englisch
 Datum: 2022-08-222022-09-032022-11
 Publikationsstatus: Final veröffentlicht
 Seiten: 14
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: MDB-ID: yes - 3455
PIKDOMAIN: RD2 - Climate Resilience
Organisational keyword: RD2 - Climate Resilience
Working Group: Hydroclimatic Risks
Research topic keyword: Climate impacts
Research topic keyword: Food & Agriculture
Research topic keyword: Weather
Regional keyword: Germany
Model / method: Machine Learning
Model / method: Open Source Software
Model / method: Quantitative Methods
Model / method: Research Software Engineering (RSE)
DOI: 10.1007/s00484-022-02356-5
OATYPE: Hybrid - DEAL Springer Nature
 Art des Abschluß: -

Veranstaltung

einblenden:

Entscheidung

einblenden:

Projektinformation

einblenden:

Quelle 1

einblenden:
ausblenden:
Titel: International Journal of Biometeorology
Genre der Quelle: Zeitschrift, SCI, Scopus, p3
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 66 (11) Artikelnummer: - Start- / Endseite: 2287 - 2300 Identifikator: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/190926
Publisher: Springer