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Projekt Druckansicht

Mikroökonomische Analyse des kurzfristigen Managements in der Landwirtschaft und seine Interaktion mit dem Klimawandel

Fachliche Zuordnung Agrarökonomie, Agrarpolitik, Agrarsoziologie
Förderung Förderung von 2012 bis 2018
Projektkennung Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 193709899
 
Erstellungsjahr 2019

Zusammenfassung der Projektergebnisse

This project dealt with the short term management of agricultural crops under the influence of climate change. The question when to time the agricultural actions on the field is highly weather dependent and thus subject to gradual changes with changing climate. On the other side, the timing of the actions has crucial influence of the obtainable yields and on surface coverage of the fields and thus causes a feedback towards climate. In this project the model FARMACTOR was developed which models the day by day actions of farmers on the field by so called triggers, which are conditions regarding date, soil and plant properties. The model was able to reproduce the overall trend of observed timing shifts observed in the past and forecasts further shifts in the future. Coupling to crop growth model with the model EXPERT-N resulted in an analysis of future yield potential which may be harmed by climate stress, however may still increase, if breeding progress continues and if CO2 fertilization may be taken into account. Variation of yield is likely to increase, however the interplay with varying prices are blurring this clear picture for the variable profit. Empirical surveys among the farmers in the research area show that most respondents are aware of climate change, however many are still unsure whether the changes are to the advantage or disadvantage of agriculture. Heat waves, droughts and storms are of major concerns. In this project, robust rules have been developed that allow an easy introduction of the timing shifts of the field activities into the large coupled models BEMS and ALCM. This increases the validity of these models in contrast to a constant assumption of activity timing and closes the feedback cycle between climate and farming. Finally, a novel meta model technique was developed that captures the behavior of complex simulation models in terms of conditional probability tables in a Bayesian Network. This type of model requires low computational resources and can thus easily be applied in interactive modeling sessions with stakeholders. First test with students were promising.

Projektbezogene Publikationen (Auswahl)

 
 

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