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.

With the goal of creating a generally applicable database for agroecological research data, we developed and described a data model. The objective of the present work was to determine the practical usefulness of a database implemented from that data model. We used a commercially available relational database management system and mapped the entities of the logical data structure to tables, entity attributes to table columns, and entity relationships to foreign keys. The attributes of the entity representing measurement values were distributed over two tables, both to reduce the size of the database and to reduce the response time of queries involving this entity. We found that loading data into the database was the most significant hurdle to its use, and so developed a set of stored procedures that function as a data input language. The input language and other methods were used to load the data from an intensively monitored, multisite, multiyear experiment into the database. The database was used to manage, explore, and analyze the data from these experiments, as well as to share the data between collaborators and with others in an effective way. We conclude that a database implemented from the previously designed data model is a practical and extremely useful tool for research.