Author
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JONES, DAVID - UNIV OF NEBRASKA |
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BARNES, EDWARD |
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Submitted to: Transactions of the ASAE
Publication Type: Peer Reviewed Journal Publication Acceptance Date: 3/13/2000 Publication Date: N/A Citation: N/A Interpretive Summary: Many agricultural producers are starting to adopt a new management practice called precision farming in which crop needs are determined at a very fine spatial resolution (sometimes as fine as every 40 inches). However, it is very difficult for a farmer to make management decisions at this resolution. In this study, an approach was demonstrated that combines remotely sensed images, a crop simulation model, and a decision model to reduce extensive amounts of information into a single chart. The chart provides a ranking of the possible management alternatives and a measure of the uncertainty associated with each alternative. The results of this study form the framework from which consultants, farm managers, or software companies can develop new tools to provide decision support to a precision crop management system. Technical Abstract: Precision crop management (PCM) seeks to optimize crop production for profitability, sustainability, and environmental protection at fine spatial and temporal resolutions. The implementation of a management system based on this definition is not a simple task. A framework for incorporating a variety of data sources and models cable of providing information for such a management system is proposed in this study. The framework is composed of remotely sensed data, methods to interpret these data in terms of physical crop or soil conditions, a crop simulation model, and a decision model based on fuzzy composite programing. A simple example is presented using this approach to evaluate two irrigation methods in a cotton production system. |
