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Precision Agriculture in the 21st Century: Geospatial and Information Technologies in Crop Management
today. This process is further enhanced by the dynamic nature of advances in information technology. A capability that is technically or economically unfeasible can become feasible as a result of a technological innovation occurring well outside the arena of agricultural technology development or agricultural research. Thus the process by which precision agriculture is adopted could be fragmented and discontinuous. Therefore, it is impossible to specify the precise dimensions and characteristics of the precision agriculture of the future.
Precision agriculture could materially affect on-farm decision-making processes that depend on implied knowledge gained by observation and experience. While its precise dimensions continue to evolve, the following features characterize most precision agriculture applications in use or under development:
Data capture tends to be electronic, automated, and relatively inexpensive.
Data capture can occur more frequently and in more detail.
Information, either captured as a part of field operations or purchased externally, can be considered separate input into the production operation. (It is also a feature of integrated pest management and sustainable agriculture concepts.)
Data interpretation and analysis can be more formal and analytical.
Scientific decision rules are applicable to actual farming operations.
Implementation of the response can be more timely and more site specific.
Performance of alternative management systems can be quantitatively evaluated.
The long time lags between input decision making, application of inputs, and observation of yields in crop production systems make it difficult to evaluate decision-making effectiveness. The chance for misinterpreting results is further heightened when inputs and outcomes are observed rather than measured. The difficulty of learning in such settings is not constrained or unique to farmers. Considerable research has documented that human decision making is more likely to suffer bias and misinterpretation when (1) feedback loops are long between the time the decision is made and the outcome occurs and (2) cause/effect linkages are not simple (Einhorn, 1980; Hogarth and Markridakis, 1981). These two characteristics apply to traditional crop production settings.
The uncertainties associated with the rapid evolution of information technologies and the dynamics of the process of adopting precision agriculture represented a significant challenge in the preparation of this report. However, these same uncertainties provided considerable excitement and a sense of mission for the project. Tomorrow's precision agriculture will be significantly affected by actions in the public and private sectors today.
The focus of this committee, therefore, was not on predicting a single future. Rather, members chose to recognize the uncertainties inherent in the future evolution of precision agriculture and to emphasize possible paths and the implications of those paths. Further, the study recommendations define key actions that