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IRMA - Integrated Regional Model Approach

is a statistical model approach to simulate interannual yield changes of agricultural crops by linking them to changes of weather parameters to assess the combined effects of regional climate change with respect to yields.

What does the model do?
IRMA (Integrated Regional Model Approach) is a statistical time series approach to estimate annual crop yield changes. The model uses regional data on county level to model yield changes based on county-specific parameter estimations. In a second step the yield changes or re-calculated yields are aggregated to the level of Federal States, river basins, agro-ecological zones or nations by average means or weighted means of agricultural area. Thus, the observation level is located above the modeling level. Region-specific climatic or economic yield-impacts are considered by modeling on regional level. Outliers are eliminated over the subsequent aggregation. The model is based on the production function approach. IRMA reproduces historical crop yields and is appropriate for short to mid-term predictions of future yields by out of sample forecast.

What are the next steps in the development of the model architecture?

County-specific parameters are used to capture regional yield-relevant influences. The aggregation eliminates outliers and farm individual management errors. The frequently in the literature used panel data models estimate directly temporal and spatial yield variation on only one parameter per (sub) nation. These models do not consider regional impacts in their parameters. Our time-series model in combination with one or two non-weather parameter as proxy (price indices or crop specific arable land) is able to predict interannual, future crop yields.

Who maintains it?

Frank Wechsung, Andrea Lüttger, Christoph Gornott

Key publications

Wechsung, F., Gerstengarbe, F.-W., Lasch, P. and Lüttger, A. (2008): Die Ertragsfähigkeit ostdeutscher Ackerflächen unter Klimawandel, PIK Report N°112, Potsdam, S. 98.

Lüttger, A., Gerstengarbe, F.-W., Gutsch, M., Hattermann, F., Lasch, P., Murawski, A., Petraschek, P., Suckow, F., Werner,  P.C. (2011): Klimawandel in der Region Havelland-Flämingl, PIK Report N°121, Potsdam

Futher publications

Hanus H. (1978): Vorhersage von Ernteerträgen aus Witterungsdaten in den Ländern der EG. In: eurostat - Agrarstatistische Studien,  N° 21, Luxembourg, ISBN: 92-825-0534-0.

Woodard, J. D. and Garcia, P. (2008): Weather Derivatives, Spatial Aggregation, and Systemic Risk: Implications for Reinsurance Hedging. In: Journal of Agricultural and Resource Economics (33), N° 1, URL: http://EconPapers.repec.org/RePEc:ags:jlaare:36705.

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