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Chapter 34 - IPM information technology

Published online by Cambridge University Press:  01 September 2010

Edward B. Radcliffe
Affiliation:
University of Minnesota
William D. Hutchison
Affiliation:
University of Minnesota
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Summary

Today as in no time in history, the management in IPM is supported by information technology. From automated temperature sensors in the field to the desktop computer to the internet website, technology assists us in making pest management decisions. It is therefore necessary to have a framework for thinking about the information flowing through the system and the ways the information is processed. In this chapter we'll focus on a concrete example, namely, that of pest data making its way from the scout or researcher to the decision maker. Along the way we'll encounter common concepts of information flow that are equally useful in other scenarios.

Information life cycle

Information is all around us, yet we must select which information we want to record. Information has a definite life cycle. It can be recorded, analyzed, summarized, interpreted, shared and finally archived or discarded (Fig. 34.1).

Observations in field or laboratory

The number of insects in a trap, the percent leaf area removed, the damage done to a root system – these are all measurements or observations that represent the foundation of IPM decision making. The grower, researcher or knowledge worker uses the observations or measurements taken to make a statement or recommendation based on analysis. In doing so, the worker draws on past experience, the work of others, and the analysis of many contextual variables such as weather, local landscape, parallel observations at other locations, etc.

Type
Chapter
Information
Integrated Pest Management
Concepts, Tactics, Strategies and Case Studies
, pp. 437 - 442
Publisher: Cambridge University Press
Print publication year: 2008

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References

Antoniou, G. & Harmelen, F. (2004). A Semantic Web Primer. Cambridge, MA: MIT Press.Google Scholar
Codd, E. F. (1990). The Relational Model for Database Management, v. 2. Reading, MA: Addison Wesley.Google Scholar
,Internet Archive (2007). Internet Archive. San Francisco, CA: Internet Archive. Available at www.archive.org/.Google Scholar
,National Information Standards Organization (2004). Understanding Metadata. Bethesda, MD: National Information Standards Organization. Available at www.niso.org/standards/resources/UnderstandingMetadata.pdf.Google Scholar
Rice, M. E. & Pope, R. E. (2007) Integrated Crop Management Newsletter. Ames, IA: Iowa State University. Available at www.ipm.iastate.edu/ipm/icm/2007/5-7/blackcutworm.html.Google Scholar

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