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  Wheat yields in Kazakhstan can successfully be forecasted using a statistical crop model

Romanovska, P., Schauberger, B., Gornott, C. (2023): Wheat yields in Kazakhstan can successfully be forecasted using a statistical crop model. - European Journal of Agronomy, 147, 126843.
https://doi.org/10.1016/j.eja.2023.126843

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 Urheber:
Romanovska, Paula1, Autor              
Schauberger, Bernhard1, Autor              
Gornott, Christoph1, Autor              
Affiliations:
1Potsdam Institute for Climate Impact Research, ou_persistent13              

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Schlagwörter: Yield forecast; Statistical crop model; Wheat; Kazakhstan; Out-of-sample validation
 Zusammenfassung: Wheat production in Kazakhstan is fundamentally contributing to food security in Central Asia and beyond. It gained even more importance after recent spikes in global food prices in 2022. Therefore, timely and reliable estimates of Kazakh wheat production are important for food security planning and management. In this study, we developed a statistical weather-driven crop model that can successfully hindcast wheat yields at the oblast level up to two months before the harvest. The hindcast of wheat yields for 1993 to 2021 produces a median R2 of 0.69 for the full model run and R2 values of 0.60 and 0.37 for two levels of out-of-sample validations, respectively. Based on these yield estimates we provide a robust hindcast of the total wheat production for Kazakhstan with R2 values between 0.86 and 0.73. We forecast total wheat production in Kazakhstan for 2022 to be 12.4 million tonnes and the average yield to be 0.96 tonnes per hectare, which is 5 % above the production and yield of 2021 (assuming equal areas). The statistical model is run with publicly available weather and yield data and requires low computational power, making it easily replicable. The forecast model can be used as a replenishment to currently applied forecasting methods supporting countries in Central Asia to meet their food demand.

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Sprache(n): eng - Englisch
 Datum: 2022-12-062023-04-122023-04-122023-07
 Publikationsstatus: Final veröffentlicht
 Seiten: 11
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: Organisational keyword: RD2 - Climate Resilience
PIKDOMAIN: RD2 - Climate Resilience
MDB-ID: No data to archive
Working Group: Adaptation in Agricultural Systems
Research topic keyword: Adaptation
Research topic keyword: Food & Agriculture
Regional keyword: Asia
OATYPE: Green Open Access
DOI: 10.1016/j.eja.2023.126843
 Art des Abschluß: -

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Titel: European Journal of Agronomy
Genre der Quelle: Zeitschrift, SCI, Scopus
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 147 Artikelnummer: 126843 Start- / Endseite: - Identifikator: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/european-journal-of-agronomy
Publisher: Elsevier