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  Robustly forecasting maize yields in Tanzania based on climatic predictors

Laudien, R., Schauberger, B., Makowski, D., Gornott, C. (2020): Robustly forecasting maize yields in Tanzania based on climatic predictors. - Scientific Reports, 10, 19650.
https://doi.org/10.1038/s41598-020-76315-8

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 Creators:
Laudien, Rahel1, Author              
Schauberger, Bernhard1, Author              
Makowski, David2, Author
Gornott, Christoph1, Author              
Affiliations:
1Potsdam Institute for Climate Impact Research, ou_persistent13              
2External Organizations, ou_persistent22              

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 Abstract: Seasonal yield forecasts are important to support agricultural development programs and can contribute to improved food security in developing countries. Despite their importance, no operational forecasting system on sub-national level is yet in place in Tanzania. We develop a statistical maize yield forecast based on regional yield statistics in Tanzania and climatic predictors, covering the period 2009–2019. We forecast both yield anomalies and absolute yields at the sub-national scale about 6 weeks before the harvest. The forecasted yield anomalies (absolute yields) have a median Nash–Sutcliffe efficiency coefficient of 0.72 (0.79) in the out-of-sample cross validation, which corresponds to a median root mean squared error of 0.13 t/ha for absolute yields. In addition, we perform an out-of-sample variable selection and produce completely independent yield forecasts for the harvest year 2019. Our study is potentially applicable to other countries with short time series of yield data and inaccessible or low quality weather data due to the usage of only global climate data and a strict and transparent assessment of the forecasting skill.

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 Dates: 2020-06-262020-10-272020-11-12
 Publication Status: Finally published
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 Rev. Type: Peer
 Identifiers: MDB-ID: yes - 3060
PIKDOMAIN: RD2 - Climate Resilience
Organisational keyword: RD2 - Climate Resilience
DOI: 10.1038/s41598-020-76315-8
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Project name : EPICC and ClimSec
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Title: Scientific Reports
Source Genre: Journal, SCI, Scopus, p3, OA
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Pages: - Volume / Issue: 10 Sequence Number: 19650 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals2_395
Publisher: Nature