Decision-analysis approach to brush management planning: ramifications for integrated range resources management

Abstract

Simulation, optimization, and other modeling paradigms for systems ecology and economics have not been broadly applied to development of models for range resource management in real-world settings. The lag in emergence of applicable management models may be attributed to the lack of a conceptual context for their application. Recent appreciation of the decision-analysis approach to natural resource management and the general availability of high-speed computing capabilities have provided viable bases for using increasingly sophisticated analytical tools to solve management problems. Decision models may be used to generate proforma contrasts of selected management alternatives for multi-enterprise firms and implementation protocols for the selected management program(s). Such models, operating from a computer-managed information base, become decision-support systems (DSS) for approaching specific management problems; Integrated Brush Management Systems (IBMS) is one example. These DSS are proposed at the first step toward creating comprehensive decision-making models for total resource management (i.e. Integrated Range Resource Management or Integrated Range Resource Analysis). The next generation of models will link qualitative information and rules-of-thumb (heuristics) with hard (experimentally derived) data. These knowledge-based or expert systems, one facet of the growing field of artificial intelligence, hold great promise as vehicles for achieving Integrated Range Resource management. Bringing Integrated Range Resource Management Systems to fruition can be expedited by interdisciplinary research and educational programs for potential user groups.