data from other production operations. Management strategies consistent with our definition of precision agriculture are currently practiced, and new strategies will be developed that address spatial and temporal variability at the scale of the whole field and larger. While this report focuses on subfield level precision agricultural practices, a discussion of two key larger-scale strategies follows.
Large amounts of spatially referenced data on individual fields are, or soon will be, generated by yield monitors, real-time and remote sensors, on-the-ground sampling and observation by producers and consultants. This site-specific information will have value for use within individual fields in ways discussed in Chapter 2, but will also have value when combined with data on the same variables collected for nearby fields. Seed, chemical, and machinery agribusiness, among others, are assisting growers in data collection and interpretation. In a number of cases, agribusiness is providing financial assistance so growers will share data with the agribusiness itself. Several companies have promoted a concept of data aggregation which permits growers as well as an agribusiness free access to participant's data. Still others have promoted concepts of data collection in which data could be purchased by third parties. Many growers have expressed opposition to any of their data being shared with others. However, most growers do agree that there is economic value in the learning that results from data sharing and that may increase the likelihood of vertical integration of agricultural operations. Though it is unlikely that a commercial interest will freely share information to which they have purchased rights and made further investments, other groups may see benefits from voluntary sharing. Grower clubs such as Practical Farmers of Iowa have been successful models of farmer-directed research in which land grant or private sector consultants act as facilitators in planning and implementing research trials. The idea is for a number of growers to implement similar practices of interest in their farm operation (i.e., row-spacing, herbicide dose and timing, cultivar selection) in statistically sound on-farm experiments (Stroup et al., 1993). In these clubs, data are openly shared to identify desirable practices in local growing areas. Imagine the same grower clubs now sharing spatially referenced data from experiments where growers agree to apply similar agronomic practices. The potential to create locally derived recommendations from locally collected data is a fascinating prospect. In effect, a version of this vision is in practice today with the private crop consultant. By working with numerous growers, the consultant is afforded the opportunity to observe how diverse recommendations can affect crop fitness, yield, and production efficiency in farming enterprises as small as several acres to those that extend over thousands of acres. Such an approach would require growers to openly share data with fellow producers.