Providing Precision Farming Education Through Conferences and Workshops

In response to rapidly growing interest in precision farming, universities and others have developed numerous educational programs. Many of these events are multi-day conferences and workshops as these venues provide the time necessary for attendees to learn about the many technologies, analysis approaches, and management strategies that make up precision farming. The Washington State University Western Precision Agriculture Conference, the Assiniboine Community College Precision Agriculture Conference and the University of Nebraska–Lincoln Crop Modeling for Environment-Specific Management Workshop exemplify these types of educational programs. The Western Precision Agriculture Conference uses a traditional format where the audience primarily listens to presentations. The Assiniboine Community College Precision Agriculture Conference provides a mixture of presentations and hands-on sessions where attendees actually use precision farming tools or develop site-specific management plans. Finally, those who attend the University of Nebraska–Lincoln Crop Modeling for Environment-Specific Management Workshop complete exercises related to each topic and presentation. Because a conference or workshop brings many experts together for a short period of time, organizers of all three events have tried to capture conference content for later use in other educational programs. Their approaches to this include videotaping interviews of conference speakers and assembling software and data used during the conference on compact disk. Given the multidisciplinary nature of precision farming, conferences and workshops that utilize multiple expert presenters such as those discussed in this paper are among the best sources of precision farming education.     

A Data Generator for Evaluating Spatial Issues in Precision Agriculture

With the rapid rise in site-specific data collection, many research efforts have been directed towards finding optimal sampling and analysis procedures. However, the absence of widely available high quality precision agriculture data sets makes it difficult to compare results from separate experiments and to assess the optimality and applicability of procedures. To provide a tool for spatial data experimentation, we have developed a spatial data generator that allows users to produce data layers with given spatial properties and a response variable (e.g. crop yield) dependent upon user specified functions. Differences in response functions within fields can be simulated by assigning different models to regions in coordinate-(x and y) or feature space (multidimensional space of attributes that may have an influence on response). Noise, either unexplained variance or sensor error, can be added to all spatial layers. Sampling and interpolation error is modeled by sampling a continuous data layer and interpolating values at unsampled locations. The program has been successfully tested for up to 15000 grid points, 10 features and 5 models. As an illustration of the potential uses of generated data, the effect of sampling density and kriging interpolation on neural network prediction of crop yield was assessed. Yield prediction accuracy was highly related (correlation coefficient 0.98) to the accuracy of the interpolated layers indicating that unless data are sampled at very high densities relative to their geostatistical properties, one should not attempt to build highly accurate regression functions using interpolated data. By allowing users to generate large amounts of data with controlled complexity and features, the spatial data generator should facilitate the development of improved sampling and analysis procedures for spatial data.     

Web-Based Educational Programs in Precision Agriculture

Establishing a set of electronic multimedia educational programs will expedite the understanding and implementation of key precision agriculture technologies. Several universities and private sector partners have joined together to accomplish this task and to make the material relevant to a diverse audience. Sections include base subject information, grower experiences, potential applications, guidance on how to determine the value of application techniques, and links to additional information. The learning modules (provided in a multimedia format) include information on Global Positioning Systems (GPS), Variable Rate Technologies (VRT), Remote Sensing (RS), sampling and scouting techniques, yield and quality monitoring, and Geographic Information Systems (GIS). The programs of the University of Georgia (UGA) and of Washington State University (WSU) are highlighted. A discussion of the fundamentals of electronic publishing is included. The design of a Internet-era educational program is crucial not only so that the material is conveyed in an efficient and effective way, but also so that ongoing maintenance is minimized.