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Wetterindexversicherungen werden in der Landwirt-schaft bisher nur verhalten eingesetzt. Als ein wesentlicher Grund hierfür werden Basisrisiken angesehen, die bei der Anwendung beim Landwirt verbleiben. Wetterindexversicherungen können allerdings auch für Unternehmen des Agribusiness interessant sein, die Erträge mehrerer landwirtschaftlicher Betriebe in sich aggregieren. Dieser Beitrag untersucht am Beispiel eines Zucker verarbeitenden Unternehmens die Bedeutung des Aggregationsniveaus von Ertrags-zeitreihen für das Risikoreduzierungspotenzial von Wetterindexversicherungen. Die Grundlage hierfür bilden die einzelbetrieblichen Zuckerertragszeitreihen von 40 Zuckerrüben produzierenden Betrieben sowie die aggregierte Zuckerertragszeitreihe aller rund 5 000 Zuckerrüben produzierenden Betriebe in Nord-deutschland, die ihre Zuckerrüben an das betreffende Zucker verarbeitende Unternehmen liefern. Diese hoch aggregierte Zuckerertragszeitreihe beschreibt gleichzeitig das Mengenrisiko des Zucker verarbeitenden Unternehmens. Unsere Ergebnisse belegen empirisch, dass eine Hedgingeffektivität von Wetterindexversicherungen auf unterschiedlichen Aggregationsniveaus vorhanden ist und das infolge des Aggregierens der Zuckerertragszeitreihen das Basisrisiko bei der Anwendung von Wetterindexversicherungen geschmälert und das Risikoreduzierungspotenzial gesteigert wird. Wenn das Risikoreduzierungspotenzial von Wetterindexversicherungen aus Wirksamkeitsanalysen abgeleitet wird, die mit Ertragszeitreihen einzelner landwirtschaftlicher Betriebe arbeiten, kann es für Unternehmen des Agribusiness daher unterschätzt werden. Die hier behandelte Fragestellung ist sowohl für Unternehmen des Agribusiness als auch für potenzielle Anbieter von Wetterindexversicherungen relevant. Weather index-based insurance is not sufficiently used in agriculture as of yet. Basis risks are considered to be a major reason for this. By the use of weather index-based insurance, basis risks remain with the farmer. However, weather index-based insurance can be interesting for agribusiness companies, specifically those which aggregate yields of several farms amongst themselves. This paper investigates the importance of the aggregation level of yield time series for the hedging effectiveness of weather index-based insurance following the example of a sugar processing company. This investigation is based on empirical sugar beet yield data from 40 sugar beet producing farms in Northern Germany. Furthermore, we work with the aggregated sugar yield time series of roughly 5,000 farms, which account for sugar beets used in the sugar processing company in question. At the same time, this highly aggregated yield time series describes the quantity risk of the sugar processing company. Our results empirically show that in consequence of aggregating the sugar yield time series, basis risk is diminished and risk reduction potential is raised through the use of weather index-based insurance. The risk reduction potential of weather index-based insurance can, therefore, be underestimated if it is derived from studies pertaining to yield time series at the individual farm level. The focus of the present study may be relevant for agribusiness companies, as well as for potential providers of weather derivatives.

GPS positioning devices are becoming a commodity sensor platform with the emergence and popularity of smartphones and ubiquitous networking. While the positioning capability has been exploited in location-based services, so has its spatiotemporal cousin, tracking, so far only been considered in costly and complex fleet management applications. The proposed project, SimpleFleet will make it easy for SMEs, both, from a technological and business perspective, to create (Mobile) Web-based fleet management applications. For this purpose, we build a large data pool comprising base data such as maps and traffic data from dedicated providers. A simple interface will provide a means to connect user-contributed data streams to this pool. In addition and to increase the market potential of the data pool, we also want to address the related geomarketing domain, which uses travel information in various geo-statistical analysis methods as well as visualizations of the data to be used in online and print publications. An algorithmic framework dubbed “TrafficIntelligence” that includes map-matching algorithms, vehicle routing services and a statistics package will utilize the collected data and provide value-added service. SMEs will be able to access the data and services by means of a Web-based API, a Software Development Kit (SDK) wrapping API access for specific languages and environments and Application Frameworks for rapid application development for target platforms such as Web (JavaScript), and iPhone and Android mobile platforms.