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Predictive drivers and transferability of multi-scale machine learning based crop yield prediction under drought across European and Asian climates
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In
Agricultural water management 327, Seiten/Artikel-Nr.:110254
2026
Online
DOI: 10.1016/j.agwat.2026.110254
DOI: 10.18154/RWTH-2026-03209
URL: https://publications.rwth-aachen.de/record/1031957/files/1031957.pdf
Einrichtungen
- Lehrstuhl und Institut für Wasserbau und Wasserwirtschaft (314410)
- Juniorprofessur für Data-driven Computing in Civil Engineering (316830)
Thematische Einordnung (Klassifikation)
DDC: 640
OpenAccess: 
PDF
Dokumenttyp
Journal Article
Format
online, print
Sprache
English
Anmerkung
Peer reviewed article
Externe Identnummern
SCOPUS: SCOPUS:2-s2.0-105031065589
WOS Core Collection: WOS:001706190800001
OpenAlex: W7131620096
Interne Identnummern
RWTH-2026-03209
Datensatz-ID: 1031957
Beteiligte Länder
Germany, India
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; Article Processing Charges ; BIOSIS Previews ; Biological Abstracts ; Clarivate Analytics Master Journal List ; Current Contents - Agriculture, Biology and Environmental Sciences ; DOAJ Seal ; Ebsco Academic Search ; Essential Science Indicators ; Fees ; IF >= 5 ; JCR ; Nationallizenz
; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection
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The record appears in these collections: |
| Typ | Amount | VAT | Currency | Share | Status | Cost centre |
| APC | 2296.00 | 160.72 | EUR | 96.35 % | (DEAL) | 021000-314410 |
| Other | 87.00 | 6.09 | EUR | 3.65 % | (DEAL) | 021000-314410 |
| Sum | 2383.00 | 166.81 | EUR | |||
| Total | 2549.81 |
PDF