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.

Dataset Identification:

Resource Abstract:
description: 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.; abstract: 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.
Citation
Title 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.
creation  Date   2018-03-29T23:11:20.578069
Resource language: 
Processing environment:  
Back to top:

Resource distribution information

Digital Transfer Options
Linkage for online resource
name Dublin Core references URL
URL: https://doi.org/10.2737/RDS-2016-0029
protocol WWW:LINK-1.0-http--link
link function information
Description URL provided in Dublin Core references element.

Metadata data stamp:  2018-08-06T20:01:15Z
Resource Maintenance Information
maintenance or update frequency: 
notes: This metadata record was generated by an xslt transformation from a dc metadata record; Transform by Stephen M. Richard, based on a transform by Damian Ulbricht. Run on 2018-08-06T20:01:15Z
Metadata contact - pointOfContact
organisation Name  CINERGI Metadata catalog
Contact information
Address
electronic Mail Address cinergi@sdsc.edu
Metadata language  eng
Metadata character set encoding:   utf8
Metadata standard for this record:  ISO 19139 Geographic Information - Metadata - Implementation Specification
standard version:  2007
Metadata record identifier:  urn:dciso:metadataabout:dc41f40b-6d8e-4abe-8b11-b390e5c3966c

Metadata record format is ISO19139 XML (MD_Metadata)