(Chaplin et al., 1995; Olieslagers et al., 1995). Finally, producers need confirmation that new methods of interpreting precision input information to develop recommendations for management changes are accurate and do improve economic and environmental performance over whole-field management methods (Booltink et al., 1996; Rawlins, 1996). Subfield fertilizer response relationships, economic pest control thresholds, and computer-based decision support systems derived from crop growth simulations need to be tested and evaluated under actual field conditions in a variety of circumstances (Heiniger, 1996).
Precision agriculture evaluation activities should be undertaken by both the public and private sectors. Organizations in both sectors should work together to avoid possible biases in evaluating the efficacy of the technologies.
Because of the site-specific nature of the farm fields that precision agriculture is designed to address, evaluation cannot be generalized and must be couched in terms of the specific resource conditions to which it is applied. Evaluations should compare precision agriculture systems with conventional, uniform management systems, and against each other, recognizing that precision agriculture enables changes beyond variable-rate application of inputs. Evaluation is part of an iterative continuous cycle of research, development, and deployment that is necessary for the technologies to evolve and improve.
Precision agriculture requires new approaches to research that are designed explicitly to improve understanding of the complex interactions between multiple factors affecting crop growth and farm decision making. USDA and land grant universities should give increased priority to such new approaches by reallocating personnel and budgets.
The most important research area of precision agriculture is development of theoretical and empirical knowledge to support improved crop models, farm management methods, and expert systems software. Much of the discussion of precision farming has revolved around measurement of variability through yield monitors, remote sensing, and digitized soil mapping. However, measurement means little if it does not result in better management. In this regard, precision agriculture is a systems approach to agricultural management, not dissimilar to the application of systems principles in other arenas since the 1950s, including environmental problems in the 1970s, IPM and sustainable agricultural systems in the 1980s, and watershed and ecosystem management initiatives in the 1990s. Precision agriculture is fundamentally an information technology that focuses people on the complexities and interrelatedness of agroecosystems in a holistic way. If systematic understanding of the cropping system is not captured in models, however, it is unlikely that the volumes of precision data on subfield variation can be meaningfully processed to provide improved management decisions. If such models