Deutsch
 
Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

 
 
DownloadE-Mail
  Assessment of weather-yield relations of starchy maize at different scales in Peru to support the NDC implementation

Laudien, R., Schauberger, B., Gleixner, S., Gornott, C. (2020): Assessment of weather-yield relations of starchy maize at different scales in Peru to support the NDC implementation. - Agricultural and Forest Meteorology, 295, 108154.
https://doi.org/10.1016/j.agrformet.2020.108154

Item is

Dateien

einblenden: Dateien
ausblenden: Dateien
:
SI_laudien_et_al_10_09_20_formatted.pdf (Ergänzendes Material), 2MB
 
Datei-Permalink:
-
Name:
SI_laudien_et_al_10_09_20_formatted.pdf
Beschreibung:
-
Sichtbarkeit:
Privat
MIME-Typ / Prüfsumme:
application/pdf
Technische Metadaten:
Copyright Datum:
-
Copyright Info:
-
Lizenz:
-
:
manuscript_laudien_et_al_10_09_20_formatted.pdf (Preprint), 2MB
 
Datei-Permalink:
-
Name:
manuscript_laudien_et_al_10_09_20_formatted.pdf
Beschreibung:
-
Sichtbarkeit:
Privat (Embargo bis 2021-09-09)
MIME-Typ / Prüfsumme:
application/pdf
Technische Metadaten:
Copyright Datum:
-
Copyright Info:
-
Lizenz:
-

Externe Referenzen

einblenden:

Urheber

einblenden:
ausblenden:
 Urheber:
Laudien, Rahel1, Autor              
Schauberger, Bernhard1, Autor              
Gleixner, Stephanie1, Autor              
Gornott, Christoph1, Autor              
Affiliations:
1Potsdam Institute for Climate Impact Research, Potsdam, ou_persistent13              

Inhalt

einblenden:
ausblenden:
Schlagwörter: -
 Zusammenfassung: Climate change poses a substantial risk to agricultural production in Peru. Nationally Determined Contributions (NDCs) are currently developed and outline Peru's mitigation actions and adaptation plans to climate change in various sectors. To support the implementation of adaptation measures in the agricultural sector, information on weather-related risks for crop production and the effectiveness of adaptation options on the local scale are needed. We assess weather influences on starchy maize yields on different scales in Peru based on statistical crop models and a machine learning algorithm. The models explain 91% of yield variability (55% based on the cross-validation) on the regional scale. On the local scale, weather-related yield variation can be explained in some areas, but to a lower extent. Based on these models, we assess the effectiveness of adaptation measures which increase water availability to protect against negative impacts from dry weather conditions. The results show that a higher water availability of 77mm in the growing season would have regionally different effects, ranging from an increase of 20% to a decrease of 17% in maize yields. This large range underlines the importance of a local assessment of adaptation options. With this example, we illustrate how a statistical approach can support a risk-informed selection of adaptation measures at the local scale as suggested in Peru's NDC implementation plan.

Details

einblenden:
ausblenden:
Sprache(n):
 Datum: 2020-09-202020
 Publikationsstatus: Final veröffentlicht
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1016/j.agrformet.2020.108154
MDB-ID: yes - 3061
PIKDOMAIN: RD2 - Climate Resilience
Organisational keyword: RD2 - Climate Resilience
 Art des Abschluß: -

Veranstaltung

einblenden:

Entscheidung

einblenden:

Projektinformation

einblenden:

Quelle 1

einblenden:
ausblenden:
Titel: Agricultural and Forest Meteorology
Genre der Quelle: Zeitschrift, SCI, Scopus, p3
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 295 Artikelnummer: 108154 Start- / Endseite: - Identifikator: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals15