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  Global evaluation of a semiempirical model for yield anomalies and application to within-season yield forecasting

Schauberger, B., Gornott, C., Wechsung, F. (2017): Global evaluation of a semiempirical model for yield anomalies and application to within-season yield forecasting. - Global Change Biology, 23, 11, 4750-4764.
https://doi.org/10.1111/gcb.13738

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 ???ViewItemFull_lblCreators???:
Schauberger, Bernhard1, ???ENUM_CREATORROLE_AUTHOR???                 
Gornott, Christoph1, ???ENUM_CREATORROLE_AUTHOR???                 
Wechsung, Frank1, ???ENUM_CREATORROLE_AUTHOR???                 
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1Potsdam Institute for Climate Impact Research, ou_persistent13              

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 ???ViewItemFull_lblAbstract???: Quantifying the influence of weather on yield variability is decisive for agricultural management under current and future climate anomalies. We extended an existing semiempirical modeling scheme that allows for such quantification. Yield anomalies, measured as interannual differences, were modeled for maize, soybeans, and wheat in the United States and 32 other main producer countries. We used two yield data sets, one derived from reported yields and the other from a global yield data set deduced from remote sensing. We assessed the capacity of the model to forecast yields within the growing season. In the United States, our model can explain at least two‐thirds (63%–81%) of observed yield anomalies. Its out‐of‐sample performance (34%–55%) suggests a robust yield projection capacity when applied to unknown weather. Out‐of‐sample performance is lower when using remote sensing‐derived yield data. The share of weather‐driven yield fluctuation varies spatially, and estimated coefficients agree with expectations. Globally, the explained variance in yield anomalies based on the remote sensing data set is similar to the United States (71%–84%). But the out‐of‐sample performance is lower (15%–42%). The performance discrepancy is likely due to shortcomings of the remote sensing yield data as it diminishes when using reported yield anomalies instead. Our model allows for robust forecasting of yields up to 2 months before harvest for several main producer countries. An additional experiment suggests moderate yield losses under mean warming, assuming no major changes in temperature extremes. We conclude that our model can detect weather influences on yield anomalies and project yields with unknown weather. It requires only monthly input data and has a low computational demand. Its within‐season yield forecasting capacity provides a basis for practical applications like local adaptation planning. Our study underlines high‐quality yield monitoring and statistics as critical prerequisites to guide adaptation under climate change.

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 ???ViewItemFull_lblDates???: 2017
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 ???ViewItemFull_lblIdentifiers???: ???ENUM_IDENTIFIERTYPE_DOI???: 10.1111/gcb.13738
???ENUM_IDENTIFIERTYPE_PIKDOMAIN???: Climate Impacts & Vulnerabilities - Research Domain II
???ENUM_IDENTIFIERTYPE_EDOC???: 7613
???ENUM_IDENTIFIERTYPE_RESEARCHTK???: Food & Agriculture
???ENUM_IDENTIFIERTYPE_RESEARCHTK???: Climate impacts
???ENUM_IDENTIFIERTYPE_RESEARCHTK???: Extremes
???ENUM_IDENTIFIERTYPE_MODELMETHOD???: Machine Learning
???ENUM_IDENTIFIERTYPE_REGIONALK???: Global
???ENUM_IDENTIFIERTYPE_ORGANISATIONALK???: RD2 - Climate Resilience
???ENUM_IDENTIFIERTYPE_WORKINGGROUP???: Adaptation in Agricultural Systems
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???ViewItemFull_lblSourceTitle???: Global Change Biology
???ViewItemFull_lblSourceGenre???: ???ENUM_GENRE_JOURNAL???, SCI, Scopus, p3
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