A conceptual framework for simultaneous optimization of integrated climate scenario data and phenology models

Authors

Flavian Tschurr (ORCID: https://orcid.org/0000-0001-7986-1556)
Sven Kotlarski (ORCID: https://orcid.org/0000-0001-9542-6781)
Achim Walter
Lukas Roth
Pierre Martre

Institutions

Federal Office of Meteorology and Climatology MeteoSwiss (CH)
Université de Montpellier (FR)
Institut Agro Montpellier (FR)
ETH Zurich (CH)
Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR)
Laboratoire d'Ecophysiologie des Plantes sous Stress environnementaux (FR)
Institut Agro Montpelier (FR)

Publication Details

Journal: Agronomy for Sustainable Development
Published: 2026-03-12
DOI: https://doi.org/10.1007/s13593-026-01091-0
Primary Topic: Remote Sensing in Agriculture
Type: article
Field-Weighted Citation Impact: 0.00

Funders

National Science Foundation
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

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article

A conceptual framework for simultaneous optimization of integrated climate scenario data and phenology models

Flavian Tschurr, Sven Kotlarski, Achim Walter, Lukas Roth et al.

Mar 12, 2026

Agronomy for Sustainable Development

Remote Sensing in Agriculture

article

A conceptual framework for simultaneous optimization of integrated climate scenario data and phenology models

Flavian Tschurr, Sven Kotlarski, Achim Walter, Lukas Roth, Pierre Martre

March 12, 2026article en

Abstract

No abstract available for this paper.

Agronomy for Sustainable DevelopmentVol. 46(2)

Federal Office of Meteorology and Climatology MeteoSwiss (CH), Université de Montpellier (FR), Institut Agro Montpellier (FR), ETH Zurich (CH), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR), Laboratoire d'Ecophysiologie des Plantes sous Stress environnementaux (FR), Institut Agro Montpelier (FR)

National Science Foundation, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

Climate action

Openalex Percentile: Top 32%

Remote Sensing in Agriculture

DOI Open Access PDF OpenAlex

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