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A Database for Agroecological Research Data: I. Data Model

DLO-Inst. for Agrobiology and Soil Fertility, P.O. Box 14, 6700 AA Wageningen, The Netherlands
Escuela de Agric. de la Región Tropical Húmeda (EARTH), Las Mercédes, Costa Rica

Scott D. Krieger and John V. Carlis

Dep. of Computer Science, Univ. of Minnesota, 4-192 EE/CSci Building, 200 Union St. SE, Minneapolis, MN 55455

John M. Baker

USDA-ARS, Univ. of Minnesota,, 439 Borlaug Hall, 1991 Upper Buford Circle, St. Paul, MN 55108

^* Corresponding author (f.k.vanevert{at}ab.dlo.nl)

Data from agroecological experiments are typically stored in a collection of minimally documented computer files, with additional information entered into field or lab books. As a result of this fragmentation of data and lack of proper documentation, information may be lost and data manipulation is generally cumbersome, error-prone, and hard to automate. Modern database technology has the potential to resolve these issues. Storing experiment data in a database solves the problem of fragmentation because all data are in the database; the problem of documentation is solved by making the relations between different items of information explicit during the design of the database; and the problem of manipulation is solved by the powerful query languages available with modern database management systems. As a first step in the construction of a generally applicable database for use in agroecological research, we used a formal method to design a data model that explicitly describes the types of information (‘entities’) one may want to remember about experiments and the relationships between these entities. The data model described here consists of 40 entities and 54 relationships. The entities are classified in five categories: (i) experiments, including statistical design; (ii) objects on which measurements are made; (iii) measurement protocol and equipment; (iv) measurements; and (v) field operations. We describe in detail how the information from several common types of measurements is stored using the proposed data model and conclude that the data model adequately describes the information that scientists in agroecological disciplines need to remember about their experiments.

Received for publication March 30, 1998.

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