Final spatial and tabular data from a process-based model (3-PG) used to predict and map hybrid poplar biomass productivity in Minnesota and Wisconsin, USA

Hybrid poplars have demonstrated high biomass productivity in the North Central USA as short rotation woody crops (SRWCs). However, our ability to quantitatively predict productivity for sites that are not currently in SRWCs is limited. In this study, the Physiological Processes Predicting Growth (3-PG) model was (1) assigned parameters for hybrid poplars using species-specific physiological data and allometric relationships from previously published studies, (2) calibrated for the North Central region using previously-published biomass data from eight plantations along with site-specific climate and soils data, (3) validated against previously published biomass data from four other plantations using linear regression of actual versus predicted total aboveground dry biomass, (4) evaluated for sensitivity of the model to manipulation of the parameter for age at full canopy cover (fullCanAge) and the fertility rating growth modifier, and (5) combined with soil and climate data layers to produce a map of predicted biomass productivity for the states of Minnesota and Wisconsin. This package contains the polygon feature layer and tabular data that correspond to 'Using a process-based model (3-PG) to predict and map hybrid poplar biomass productivity in Minnesota and Wisconsin, USA.' (Headlee et al. 2013). The tabular data for mean annual biomass for hybrid poplar including the STATSGO soil and NARR climate values were used to generate the biomass values. The WTAvg_DM values represent the overall predicted biomass productivity for hybrid poplars.

• ISO-19139 metadata
• ArcGIS Hub Dataset
• ArcGIS GeoService