A Framework For Managing Information In Precision Ag

A Framework For Managing Information In Precision Ag

Production decisions in precision ag rely on spatially accurate and timely information. This can come in many forms such as data, alerts, images, maps, tables, labels, commentaries, recommendations, reviews, standards and guidelines. The ever increasing flow of information has moved society ever faster into the information age. On the farm, this information flow can seem like a wave or, for the unprepared few, a tsunami.


To stem this flow, information must be managed in a way that it is readily accessible and easily incorporated in everyday decisionmaking. The management of information is both a necessity and a challenge. It is a necessity for pre-season strategic planning, in-season tactical support and ultimately for the long-term sustainability of a farming operation. It is a challenge because virtually every day some new source, device or program brings more information into the realm of farming.

Agriculture Production Flow Diagram

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Viewing information in a framework helps to understand its management. A suggested framework is depicted as a flow diagram in this story. The two main sections of the diagram are the “production” space and the downstream “marketing.” Information flowing from the production space passes through processers, shippers, buyers and lastly to consumers as part of the food supply chain. The production space itself is centered on precision ag applications, which are driven by field data and alerts. Surrounding the precision ag applications are off-site monitoring, on-site monitoring, off-site modeling, grower-consultant management decisions, back-office inventory and sales and regulatory reporting.

Reading The Diagram

Starting at the top of the diagram, on-site monitoring consists of four information source examples: Weather stations, field sensors, equipment tools and scouting devices. Each source is physically located on a farm or involved in sampling the soil or a canopy. The form of each source’s information is data, either by measurement or observation.

Collectively, the four on-site monitoring sources provide field data for a fixed point (weather station), an array of fixed points (field sensors) and variable points located by GPS (equipment tools and scouting devices). Employing user-defined thresholds, field data can be in the form of alerts. Processed through a soil laboratory, field samples can be in the form of recommendations. Field data from on-site monitoring is unique among the other production space information flows because it is owned by a grower. Like management decisions that use it and precision ag applications based on it, field data is private and can only be shared with the permission of a grower.

Off-site monitoring sources represent information collected and processed remotely outside a farm. There are three examples of off-site monitoring sources: Weather, soil and imagery. Like on-site monitoring, the form of information for these sources is data. Weather data could be historical records or forecasts from governments via the Internet or be part of online subscriptions from private industry. Soil data are available from public and private databases. Imagery is available from public and private entities.

However, because it is collected with different techniques, spatial resolutions and domains, imagery, whether available from a government or a company, is increasingly being provided by 30 parties to a grower because of registration and calibration issues. Information from off-site sources must be delivered electronically to a grower and processed with a program in order to be useful for precision agriculture applications.

Off-site modeling sources provide information in the form of model output. Three examples could be models for crops, pests and water. Model output is the most sophisticated form of information because it needs minimal interpretation to be used in management decisions and can be readily input into precision ag applications. Unlike monitoring sources, off-site models can generate new information by ingesting data from those and other sources.

The on-site monitoring, off-site monitoring and off-site modeling complement each other as sources of data. Off-site monitoring data can be used to quality control on-site monitoring data. On-site monitoring data can be employed to locally calibrate large-scale model output. Together, the on-site and off-site data flows provide comprehensive sources of information for production decisionmaking.

The framework displayed in the diagram provides not only an understanding of information flow from various sources to precision ag applications, but can also serve as a tool for planning farm operations. A grower and his/her management team can begin identifying what information is necessary for a specific production decision. They can also schedule where, when and to whom this information needs to be delivered for that decision during a growing season.