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| Titel |
Soil Moisture as an Estimator for Crop Yield in Germany |
| VerfasserIn |
Michael Peichl, Volker Meyer, Luis Samaniego, Stephan Thober |
| Konferenz |
EGU General Assembly 2015
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 17 (2015) |
| Datensatznummer |
250106729
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| Publikation (Nr.) |
 EGU/EGU2015-6410.pdf |
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| Zusammenfassung |
| Annual crop yield depends on various factors such as soil properties, management decisions,
and meteorological conditions. Unfavorable weather conditions, e.g. droughts, have the
potential to drastically diminish crop yield in rain-fed agriculture. For example, the drought
in 2003 caused direct losses of 1.5 billion EUR only in Germany. Predicting crop yields
allows to mitigate negative effects of weather extremes which are assumed to occur more
often in the future due to climate change.
A standard approach in economics is to predict the impact of climate change on
agriculture as a function of temperature and precipitation. This approach has been developed
further using concepts like growing degree days. Other econometric models use nonlinear
functions of heat or vapor pressure deficit. However, none of these approaches uses soil
moisture to predict crop yield. We hypothesize that soil moisture is a better indicator to
explain stress on plant growth than estimations based on precipitation and temperature. This
is the case because the latter variables do not explicitly account for the available water
content in the root zone, which is the primary source of water supply for plant
growth.
In this study, a reduced form panel approach is applied to estimate a multivariate
econometric production function for the years 1999 to 2010. Annual crop yield data of
various crops on the administrative district level serve as depending variables. The
explanatory variable of major interest is the Soil Moisture Index (SMI), which quantifies
anomalies in root zone soil moisture. The SMI is computed by the mesoscale Hydrological
Model (mHM, www.ufz.de/mhm). The index represents the monthly soil water quantile at a 4
km2 grid resolution covering entire Germany. A reduced model approach is suitable
because the SMI is the result of a stochastic weather process and therefore can
be considered exogenous. For the ease of interpretation a linear functionality is
preferred. Meteorological, phenological, geological, agronomic, and socio-economic
variables are also considered to extend the model in order to reveal the proper causal
relation.
First results show that dry as well as wet extremes of SMI have a negative impact on crop
yield for winter wheat. This indicates that soil moisture has at least a limiting affect on crop
production. |
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