factor change as other conditions change. Furthermore, they are frequently designed to yield qualitative results or quantitative estimates of responses to changes in inputs or other variables over a range so limited as to preclude estimation of responses to the range of conditions found in production fields. As a result, standard research results are frequently of little value in designing spatial models intended for improved decision making.
Precision agriculture will necessitate a systems approach to experimental research. In this regard, precision agriculture is similar to the application of systems principles in sustainable agriculture and ecologically-based pest management strategies. What makes precision agriculture different is the capability to capture data on the production practices actually applied in fields and on the results achieved. Moreover, systems principles are needed to improve farm decision making, not for themselves alone. Research approaches from ecology and economics, in which multiple factors vary simultaneously and statistical methods are used to identify the effects of variations in individual variables, are likely to be more productive than traditional approaches. Crop science research for precision agriculture should be designed explicitly to produce results that can be used in economic or statistical decision models by decision makers. This research will also need to be interdisciplinary, drawing on expertise in a range of subject areas such as agronomy, plant science, genetics, soil science, entomology, meteorology, weed science, plant pathology, ecology, and economics.
The potential of precision agriculture is limited by the lack of appropriate measurement and analysis techniques for agronomically important factors. Public sector support is needed for the advancement of data acquisition and analysis methods, including sensing technologies, sampling methods, database systems, and geospatial methods.
A basic premise of precision agriculture is that more and better information can reduce the uncertainty producers face in decision making and the unmeasured variability in agronomic conditions. Measurement can reduce the uncertainty of decision making without changing the biological variability that occurs in crop production. While the use of information is not new to agriculture, the potential exists for a vast increase in the timeliness and amount of information if additional means of data collection and analysis become available. Only a few commercial sensors are available today. Efforts continue by both private companies and the public sector to develop real-time sensors for additional agricultural indexes. Current sampling and analytical techniques are not designed for managing small units or for in-field decision making. For example, nutrient assays that require soil sampling and physical/chemical analyses are slow and costly. Current mapping techniques are limited by a lack of understanding of the geostatistics necessary for displaying spatial variability of crops and soils.
New information technologies will be required to make the more detailed and timely decisions necessary for precision agriculture. Introduction of new sensing