through the quantity and quality of harvested product. Interactions occurring among these agronomic factors affect crop performance; for instance, weather conditions favoring growth of an insect vector can lead to outbreaks of viral diseases, resulting in damaged crops. A systems approach to agronomic management considers multiple interactions occurring in an agroecosystem (National Research Council, 1989, 1996b). Manipulating agroecosystems to achieve greater productivity depends on an understanding of the relationships among agronomic factors. The potential for generating large, detailed data sets presents a challenge for agricultural scientists who are developing tools to further the understanding of those interactions and their effects on yield. Agricultural scientists can use data sets generated under experimental conditions, as well as those generated by producers in attempting to understand the interactions in cropping systems. The adoption of precision agricultural management is most likely to occur for those factors and interactions for which there is enough understanding to accurately predict the outcomes and economic value of actions to manipulate the crop in its agroecosystem.
Mapping spatial yield patterns is a logical step in visualizing field variability. However, a yield measurement in itself cannot explain the cause of variation. Information is more valuable when causal relationships can be determined between various data sets describing a field. Yield maps can be superimposed on maps of other data collected from the same location. The analysis of these data layers with a GIS and other analysis tools may reveal spatial relationships among agronomic components contributing to yield variation (Skotnikov and Robert, 1996).
The most significant impact of precision agriculture on crop production systems is likely to be on how management decisions are made and on the time-space scales that are addressed, not on actual production practices. Precision agriculture techniques may increase efficiency of input use by allowing the producer to manage the crop on both a spatial and temporal basis with prescriptive rather than prophylactic treatments. The management of a crop production system involves many decisions, all of which are interrelated and ultimately affect profit. Crop production is subject to uncertainty due both to stochastic processes (primarily weather) and to unmeasured variability in agronomic conditions (i.e., soil fertility). Precision management tools may improve decisions related to site conditions, thereby reducing this aspect of uncertainty in the management system. However, the performance of precision agriculture depends on the interaction between site conditions and stochastic factors. Stochastic factors such as weather often have a greater impact on yield variability than variations in soil productivity. For example, a study comparing variable-rate and uniform application of superphosphate on narrow leafed lupine (Cook et al., 1996) found that variable-rate application based on nutrient response curves estimated using data from a single