(analysis of stereo pairs of aerial photographs) with new techniques using interoferometric radar or by continuous three-dimensional coordinate measurements with in-field equipment. Precise recommendations can be made to the extent that the relationships are understood between soil properties and surface morphology (i.e., slope, slope length, aspect, curvature, landscape position, catchment area, and drainage) derived from digital elevation or digital terrain models.
Crop models do not offer a panacea for problem solving; they are limited in their ability to simulate various parts of a biological system. Most of the crop and pest models available or developed to date were not designed to be used for managing spatial and temporal variation. It is not clear whether a predictive model, an explanatory model, or a hybrid approach will be more appropriate for precision agriculture. Alternatively, data mining and other techniques may be used to extract valuable information from large amounts of stored data. However, crop modeling is currently an important tool for gaining a theoretical understanding of a crop production system.
Decision support systems (DSS) are used in agriculture for tactical, strategic, and policy-level decision support. Because producers are continually faced with making tactical decisions, such tools are becoming increasingly useful on the farm. However, few DSS are in general use by agricultural producers today, in part due to difficulty in use and limited information provided—from their point of view. They have been used to aid in decisions that are complicated by large amounts of information and data. A simple conceptual diagram of a DSS is shown in Figure 1-2 (Petersen et al., 1993). Data collected by a consultant, obtained through a weather forecasting service, or acquired through a sensing operation are analyzed and linked with appropriate decision rules that identify actions to assist in producer decision making.
DSS rules are not developed to make a single recommendation but rather to provide decision makers with choices; decision support systems should be seen as sources of valuable tactical information. As is the case for crop modeling and current management recommendations, DSS have been developed for whole fields, and subfield variation has been largely ignored. Although subfield tactical decisions have been practiced by producers for many years (i.e., rouging, spot-spraying or rope-wicking residual weeds, or spot-treating chinch bugs in sorghum), most management practices are implemented for whole fields.
The relationship between the scale of an operation and the resolution and variability of sample data used in a DSS is important. To demonstrate this point, consider the appropriateness of using DSS in two sites with widely differing characteristics. The variation in the assessed attribute used in the DSS is high at one site and low at the other. The DSS may be adequate for wholefield decisions at the site with low variability but not appropriate for the site with high variability.