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Stand and natural fuel conditions were sampled in ponderosa pine forests in northern and central Arizona to develop predictive fuel depth and loading equations. Litter and duff depths can be estimated from measurements of stand density (basal area, stand density index). Although woody fuel loading did not correlate well with stand variables, correlations were found among loadings of different woody fuel size classes, so that results from a planar intersect tally of certain single woody fuel size classes may be used to predict the loadings in certain other size classes. The relatively low precision of estimates from these predictive equations can be substantially increased by applying them in a double sampling scheme. Making use of these predictive relationships, managers can devise simple, rapid, arid cost-effective fuel inventories that focus on the fuel category of interest. Fuel loads can be estimated at a desired precision with reduced investment of time and funds compared to a more comprehensive direct fuel inventory.