Effective scheduling in large-scale computational grids is challenging because it requires tracking the dynamic state of the large number of distributed resources that comprise the grid. Classical distributed information dissemination approaches such as push, pull, and their combinations, are not well suited to the problem of resource tracking, where resources are redundant and full information about all resources everywhere is neither necessary nor desirable. Aggregated, partial, or probabilistic forwarding protocols result in more efficient (but incomplete) dissemination, while maintaining sufficient information to enable effective scheduling. However, a static approach to dissemination in which all information is treated identically, is ineffective in the presence of spatial and temporal non-uniformity of resources and demands. For example, a single forwarding probability for gossipping-based dissemination may result in unnecessarily high overhead in some areas of the grid. Moreover, the right forwarding probability values can change over time, with changes in offered load and node utilization. Adaptive protocols can adjust the aggressiveness with which information is disseminated, based on current grid conditions, and can in turn increase query satisfaction rates, reduce overhead, or both. This paper explores the characteristics and behavior of adaptive probabilistic and change-sensitive information forwarding protocols, identifying and addressing several issues and problems, and introducing dissemination protocols that are better able to reduce overhead and increase query satisfaction rates for a variety of grid conditions.