Although many of the technologies making up precision agriculture are relatively mature (i.e., GPS, GIS, and remote sensing), there remains room for improvement in many technological areas directly related to agricultural applications. One of the most important of these is the development of local sensors that can be used on farm equipment to determine crop stage, soil conditions and chemistry, weed concentrations, presence of insects, and other variables important for crop growth. Public sector researchers should concentrate on the basic scientific principles that could underlie new sensor development and on the relationships between measurements from such new sensors and modeling of crop growth and yield. Private sector research and development is more appropriate for making the new sensors operational and marketable.

Another farm management problem of special importance is the determination of optimal sampling strategies. Some precision agriculture technologies function by permitting adjustment of farming practices (i.e., input application rates) to match variability in production conditions, such as soil nutrient levels or other aspects of soil quality. Determining the extent of variability is essential, not only at the subfield level, but at all spatial levels. Optimal sampling depends on trade-offs between potential savings in input expenditures, potential gains from increased yields due to improved management, and sampling costs (Hennessy et al., 1996).

Database and GIS systems include interpolation algorithms to predict data at intermediate points, but no existing research validates assumed projections under true agronomic variability in the presence of obvious measurement errors. Geospatial methods must be advanced and incorporated into GIS to facilitate accurate analysis and inference from collected precision agriculture data. The public sector should take the lead role in researching (a) the nature of variability within farm fields and at other spatial scales, (b) the required precision of compatible measurements that are to be included within GIS data sets, and (c) the fundamental geospatial analysis methods necessary for interlayer correlation analysis and inference.

Both the public and private sector have been involved in developing and disseminating standards for hardware, software, and data interpretation that could influence precision agriculture development and adoption. Such standards have been critical in the development of general computer technology (such as the ANSI, ASCII, and ISO 8211 data standards), and are emerging in GIS (such as the spatial data transfer standard and open GIS standards). Developing standards always involves a trade-off between ordering the chaos of individual systems and stifling creative breakthroughs in emerging technologies, and thus must be carefully managed.

From the perspective of the user, standardization would facilitate data interchange, particularly moving spatial data from one proprietary software package to another and to regional databases. Hardware interoperability would facilitate connection of technologies and equipment into a unified system (i.e., a VRT controller



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