We assessed the effectiveness of different sampling strategies in linking fine fuel load and crown scorch of ashe (Juniperus ashei) and redberry juniper (J. pinchotii) for prescribed fires conducted in wet and dry periods of the growing season on the Edwards Plateau, Texas, USA. Our aim was to determine if spatial and temporal variation in crown scorch was best predicted by estimates of fuel load sampled with spatially explicit, multiscale sampling strategies or with traditional, simple random sampling of fuel load. We found that multiscale sampling of fuel load underneath and adjacent to juniper crowns was more effective than simple random sampling in predicting crown scorch for the 14 fires conducted in the wet period and the five conducted in the dry period. The type of sampling strategy employed was critical in relating fuel load to crown scorch during the wet period. Percent crown volume scorched ranged from 0% to 100% in these conditions. In contrast, the type of sampling strategy was less important in the dry period when crown scorch was >90% for all juniper trees. We use these findings to illustrate how a multiscale sampling design can increase prediction power, thereby improving our ability to provide resource professionals with critical values to target in management. Using such a strategy in this study revealed that fine fuel loading of 2 670 kg · ha^–1 were needed to scorch juniper trees 100% for the conditions present in the wet period, whereas only 1 280 kg · ha^–1 were needed in the dry period. To provide managers with this type of information, we suggest that researchers shift from simple, random sampling of fuels to alternate sampling designs where randomization is maintained in the designation of treatments or selection of observations (i.e., individual juniper trees) but where fuel is systematically sampled at the location of the observation of interest.