Rangeland managers often must decide whether to suppress dicotyledonous weed populations with expensive and time-consuming management strategies. Often, the underlying goal of weed suppression efforts is to increase production of native forage plants. Many managers suppress weeds only when they feel the unwanted plants are substantially impacting their forage base. Currently, intuition and guesswork are used to determine whether weed impacts are severe enough to warrant action. We believe scientific impact assessments could be more effective than these casual approaches to decision making. Scientific approaches will necessitate data on weed abundances because the severity of a weed's impact is highly correlated with its abundance. The need for weed abundance data poses major obstacles because gathering these data with readily available techniques is time consuming. Most managers cannot or will not spend a lot of time gathering vegetation data. In this paper, we explore a rapidly measured index (<2 minutes per sample location) that is highly correlated with weed (i.e., leafy spurge Euphorbia esula L.) abundance per unit area. This index is based on the light attenuation leafy spurge causes. After measuring light attenuation in plots planted to leafy spurge and grasses, we developed a probabilistic model that predicts leafy spurge impacts on forage production. Data from experiments where herbicides suppressed leafy spurge provided an opportunity to evaluate prediction accuracy of the model. In each case herbicide experiment data fell within the range of values (i.e., credibility intervals) the model predicted, even though the model development experiments were separated from the herbicide experiments by several hundred kilometers in space and 4 years in time. Therefore, we conclude that the model successfully accounts for spatial and temporal variation. We believe light attenuation could help natural resource managers quickly quantify some kinds of weed impacts.