Understanding roundwood production in the United States at fine spatial and temporal scales is needed to support a range of analyses for decision making. Currently, estimates of county-level roundwood production are available at various time intervals for different regions of the country and for different products. Here we present our reasoning for moving to an annual timber products monitoring program and further present a comparison of sample designs to facilitate an annual program without increased effort. We found that both probability proportional to size and stratified simple random sampling designs were viable options, but the stratified simple random sampling design provided more flexibility. This flexibility was deemed important to target emerging markets and to enable sampling with certainty of specific firms. Our results lay the foundations for moving to an annual timber products output monitoring design in support of market, sustainability, and policy analyses as well as projections.

The commercial forest sector in the US includes forest landowners and forest products manufacturers, as well as numerous service providers along the supply chain. Landowners (and contractors working for them) manage forestland in part for roundwood production, and manufacturers purchase roundwood as raw material for forest products including building products, paper products, wood pellets, and others. Both types of organizations need forest resource data for applications such as strategic planning, support for certification of sustainable forestry, analysis of timber supply, and assessment of forest carbon, biodiversity, or other ecosystem services. The geographic areas of interest vary widely but typically focus upon ownership blocks or manufacturing facilities and are frequently small enough that estimates from national forest inventory data have insufficient precision. Small area estimation (SAE) has proven potential to combine field data from the national forest inventory with abundant sources of remotely sensed or other resource data to provide needed information with improved precision. Successful implementation of SAE by this sector will require cooperation and collaboration among federal and state government agencies and academic institutions and will require increased funding to improve data collection, data accessibility, and further develop and implement the needed technologies.

An analysis by Sterman et al (2018 Environ. Res. Lett. 13 015007) suggests that use of wood for bioenergy production results in a worse climate outcome than from using coal. However, many of the assumptions on which their primary wood bioenergy scenario is based are not realistic and therefore are not informative. Assumptions of uncharacteristically long rotations for southern pine plantations, no utilization of wood for longer-duration products, and a single harvest over 100 years understate the carbon performance of current forest management practices. We provide references that support realistic modeling of forest carbon dynamics that are reflective of current practice and therefore more informative.