The Development of a Forest Module for POLYSYS Burton English, Daniel De La Torre Ugarte, Kim Jensen, Jamey Menard and Don Hodges USFS Forest Products Laboratory and Southern Research Station University of Tennessee, Office of Bioenergy Programs - Joint Meeting

Objectives  Create a forest subsector with the following characteristics Simulates forest supply and demand market Allows for land use shifts between agriculture and forest lands as well as dedicated energy cellulosic production sector Annual time step

Analytical Tool  POLYSYS: 305 crop production regions (ASD’s) National livestock production National demand market (Elasticity Driven)  (Sum of Production = Domestic Supply) Energy market is goal driven Solutions compared to a USDA Baseline

Cropland Base 2002 Agricultural Census

Economic Module Environmental Module POLYSYS Model

Extended Economic Baseline  2007 USDA Baseline (2007 to 2016): Ethanol use at 12 BGY by Crop yield increases for traditional crops. No changes in current tillage practices. Corn grain was the assumed feedstock for ethanol production through the year Conversion efficiency for corn ethanol grows to 3.0 gallons/bushel.

Change in Crop Area from the 8.6 BGY to 18 BGY Scenario 2016 Corn Soybeans Wheat Cotton

Environmental Submodule  Change in Land Use  Change in Input Expenditures Fertilizer application changes estimated based on changes in expenditures in N,P,K (Fert). Herbicides and insecticides application changes estimated based on changes in expenditures on herbicide and insecticides for the 8 major crops.  Change in Soil Erosion Estimated using NRCS ASD region KLSR and P estimates combined with an estimated C factor (incorporating tillage) based on 1997 NRI data Changes in land use by POLYSYS region.  Impact on Sedimentation Made using erosion estimates converted to 99 river basin’s and coupled with sediment delivery and deposit ratios used in MOSS II along with Ribaudo’s 1985 cost of erosion study with costs indexed to 2007.

Environmental Submodule  Changes in carbon sequestration Carbon emissions for each ethanol scenario were calculated using methods and estimated coefficients from the carbon lifecycle literature (West and Marland, 2002, Marland et al., 2003).  The estimated coefficients and the quantities of fuel and inputs in the budgets in POLYSYS were used to calculate total carbon emissions for each crop in each ASD. In addition, the carbon sequestered in agricultural soils for each crop and tillage practice was estimated using methods outlined by West et al. (2008).

Herbicide Use: Regional Changes Changes between 18 BGY and 12 BGY USDA Baseline Scenarios Changes between 18 BGY and 8.6 BGY Scenarios Changes between 18 BGY with CRP and 12 BGY USDA Baseline Scenarios

Erosion: Regional Changes Changes between 18 BGY and 12 BGY USDA Baseline Scenarios Changes between 18 BGY and 8.6 BGY Scenarios Changes between 18 BGY with CRP and 12 BGY USDA Baseline Scenarios Erosion increases nearly 4% when CRP lands are used

Carbon Emissions: Regional Changes Changes between 18 BGY and 12 BGY USDA Baseline Scenarios Changes between 18 BGY and 8.6 BGY Scenarios Changes between 18 BGY with CRP and 12 BGY USDA Baseline Scenarios

Carbon Sequestration: Regional Changes Changes between 18 BGY and 12 BGY USDA Baseline Scenarios Changes between 18 BGY and 8.6 BGY Scenarios Changes between 18 BGY with CRP and 12 BGY USDA Baseline Scenarios

Simulate Forest Supply and Demand Markets  Forest Supply Market -- LP OBJ maximize Net Present Value Activities or Decision Variables  Produce softwood, hardwood saw timber, pulp, and energy on private industrial and private non industrial forest lands.  Once land is harvested a decision on whether it reenters forest land, converts to dedicated energy, or agricultural sector is determined using economics and land retention constraints.  Decision is also made as to whether additional lands from Ag sector are converted to private non industrial lands and become forest.

Simulate Forest Supply and Demand Markets  Forest Supply Market -- LP Activities or Decision Variables  Yields for softwood, hardwoods, pulp and saw timber  Costs of production based on projected prices over time  Land for hardwoods is a mixture of dbh.  Land for softwoods designated as 15” with lands harvested < 5” to energy, Lands between 5” and 9” to pulp wood, Lands between 9” and 13” to pulp wood or saw timber, and Lands > 13” to saw timber.

Simulate Forest Supply and Demand Markets  Forest Supply Market – LP Constraints  Volume produced <= Volume demanded  Volume taken <= Volume available  Land harvested by site index <= Land Available by site index  Land harvested last year = land transferred to Ag + land planted to dedicated energy crop + land returned to < 5” dbh.  Annual volumes re-estimated based on growth functions

Simulate Forest Supply and Demand Markets  Forest Demand Allocated to RHS Based on national demands and current logging output  National Demands – Currently assumed to be RPA driven.  Regional Allocation – Based on IMPLAN information generated at the county level

Schematic of a Part of the LP

Allow for land use shifts  Not fully conceptualized

Environmental subcomponents to Forest Module  Not being considered at this time  First one probably should be carbon

Annual time step