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Adaptive management is an approach to management that emphasizes structured learning through decision making based upon the realities that knowledge is incomplete, much of what we think we know is actually wrong, and managers and policymakers must act despite uncertainty regarding best management and management outcomes. AM requires iterative decisions based on information resulting from management and serves to build knowledge and improve management over time in a goal-oriented and structured process. Natural resource management is frequently highly uncertain; ecosystem managers can make better decisions in the future if they can learn. Although AM may be "common sense", there continues to be confusion regarding what actually constitutes AM. This misunderstanding is largely based upon the belief that AM is what management has always been, trial-and-error attempts to improve management outcomes. But unlike a trial-and-error approach, AM has explicit structure, including a careful description of objectives, identification of alternative management approaches, identification of hypotheses of problem causation, prediction of the consequences of implementing management alternatives, procedures for collection and analysis of monitoring data, and a mechanism for updating the management approach as learning occurs. Rangeland management in particular shows promise for application of AM, having a tradition of modeling system dynamics (e.g. state-and-transition models), identifiable spatial management units (e.g., pastures), clear management objectives (e.g., maintain forage production), and reducible uncertainties related to management impacts. We present the techniques and challenges of AM informed with two examples from rangeland ecosystems.