Climate Change, Carbon and Forests

Carbon Inventory (and Potential Carbon Management Strategies) for the McDonald-Dunn Research Forests

Principal Investigator: Dr. Temesgen Hailemariam
Graduate Student: Catherine Carlisle

Because of increasing concern about climate change induced by atmospheric accumulation of greenhouse gases, the quantification of carbon stored within ecosystems has risen high on global scientific agendas. Forests provide natural mitigation strategies, which drive the uptake of atmospheric carbon dioxide through photosynthesis. In the United States, forest land offsets 11% of nationwide greenhouse gas emissions through net sequestration of carbon within woody biomass (Smith et al., 2019).

Carbon stocks within individual trees can be conceptually divided into two main pools: aboveground biomass and belowground biomass. Of these two, aboveground biomass stores a significantly larger proportion of carbon than belowground (Xiao et al., 2003). Thus, significant effort has been devoted to developing accurate aboveground biomass estimation methods in recent years. These efforts are further motivated by the fact that management decisions aimed at optimizing net sequestration can manipulate carbon stocks of aboveground biomass.

Oregon’s forests’ carbon storage and sequestration potential, especially here in the Coast Range, have accentuated the need for accurate estimation of current carbon stocks. Forest inventory data and sets of carbon conversion factors allow for plot-level calculation of carbon storage, which can be aggregated for landscape-scale estimation.

Forest carbon estimates are necessary for regional accounting and analyses. Quantification of carbon stock on specified tracts will help forest managers to develop and implement plans to enhance carbon sequestration capacity.

Project Objectives
The primary objective of this project is to estimate current quantities of carbon sequestered and stored within the MacDonald-Dunn Forest. A secondary objective is to examine the effects of different silvicultural treatment options on long-term carbon sequestration and storage capabilities. This information will be important for land managers in balancing timber harvesting and other objectives with forest carbon goals.

We will be calculating carbon loads and projecting carbon trajectories on the McDonald-Dunn research forest, which is owned and managed by Oregon State University’s College of Forestry. The forest consists of approximately 11,170 acres of forestland on the western edge of the Willamette Valley, within the eastern foothills of the Oregon Coast Range.

We will use existing inventory data collected by Oregon State University on the McDonald-Dunn Research Forest. This data includes individual tree heights, diameters, as well as stand-level variables such as site index, stand age, and stocking density. The available inventory data was collected by contracted crews in 2019 and 2020.

To estimate current standing carbon, we will utilize the component ratio method (CRM). This method converts tree volume to biomass through allometric equations that distribute biomass quantities to individual aboveground tree components (stem wood, stem bark, and foliage) (Chojnacky, 2012). The component of interest in this study will be aboveground biomass (ABG). This study will focus on calculating the dry weight of aboveground live tree biomass as opposed to standing dead or belowground biomass, since this pool is significant in size and can be directly influenced by management decisions. After calculating above-ground-biomass, we will be able to convert to carbon mass by multiplying by 0.5 (IPCC, 2003). After estimating current carbon loads on inventoried stands within the MacDonald-Dunn Forest, we will model the effect of changing rotation length and thinning intensity on projected aboveground carbon loads using Forest Vegetation Simulator (FVS). FVS is a growth-and-yield model utilized extensively throughout the United States and is an approved quantification tool according to the American Carbon Registry (Galvez et al., 2014). FVS operates at the individual tree level and simulates growth, mortality, and regeneration based on empirical data and theoretical concepts. FVS uses these tree-level calculations to estimate biomass and carbon on the stand-level (Dixon, 2003).

We will be utilizing the Fire and Fuels Extension (FFE) to FVS, which includes carbon reporting functions corresponding to the US Carbon Accounting Rules and Guidelines for 1605 (b) Voluntary Greenhouse Gas Reporting Program (IPCC, 2003). The suite of models included in FFE-FVS software will allow us to quantify changes in carbon stocks and fluxes over a 150-year projection period while summarizing at five-year intervals (Hoover & Rebain, 2010).

Management scenarios
Our treatments modeled in FVS will be based on varying rotation age, thinning intensity, and frequency of thinning treatments for stands associated with site indices I-IV. Stands associated with site indices I & II will begin with 400 trees per acre (10.4’ x 10.4’ spacing) while stands with site indices of III & IV will begin with 360 trees per acre (11’ x 11’ spacing).

We will apply four different rotation ages to each stand. For each rotation age, we will simulate standing carbon over a 150-year projection period. Each treatment will be defined by frequency of thinning treatments and associated intensity of those treatments. The 40-year rotation age will have a maximum of one thinning treatment, the 60-year and 80-year will have a maximum of two thinning treatments, and the 120-year will have a maximum of three thinning treatments.

The three proposed thinning intensities will be constrained within density thresholds established by Drew and Flewelling (1979):

  • “Light thinning” treatment will be defined as meeting a residual density of 45% maximum SDI, which is above the “lower limit site occupancy” threshold and below the “self-thinning” threshold. At this value, trees occupy all available growing space and there is active competition between individual trees. Density-related mortality would not yet be a factor since relative stand density does not breach the “self-thinning” threshold (Drew & Flewelling, 1979).
  • “Medium thinning” will achieve a stand density equivalent to the “lower limit site occupancy threshold” at 35% maximum SDI. At the 30-35% level, trees would occupy all available growing space. As the relative density of the stand increases beyond this level, growth per unit area will increase while individual tree growth will be reduced (Drew & Flewelling, 1979).
  • “Heavy thinning” treatment will reach a percent maximum SDI of 25%, slightly above the “onset of competition” threshold which occurs at 15% maximum SDI. The “onset of competition” threshold is differentiated from lower relative stand densities by the onset of canopy closure. A percent maximum SDI of 20-25% was determined to be appropriate because it is the thinning intensity associated with variable aged management and drought mitigation. At 20-25% maximum SDI, individual trees have ample growing space and, therefore, the stand possesses adequate regeneration potential. Thinning treatments defined by a residual 25% maximum SDI upon completion have been shown to decrease mortality in stands during severe drought periods and facilitate individual tree drought resilience.

We will calculate gross and net carbon sequestered for each projection. This will be done by importing the FVS projection outputs into R studio and performing the appropriate calculations. Interpolation of carbon projection approach to un-inventoried stands will be accomplished using a spatial linear model which takes into account spatial proximity and mapped variables.

Anticipated Research Outcomes
The results of this project will help translate broad forest-based greenhouse mitigation concepts into concrete management strategies for enhancing forest carbon. Estimates of carbon sequestration under each management trajectory can be incorporated into future management plans, which focus on balancing harvest objectives with long-term carbon storage.

Research Results
This research project was initiated in 2022. Preliminary results will be reported here as they develop.

We anticipate a Master’s thesis and/or professional paper to result from this important work in 2023.

Literature Cited
Chojnacky, DC. 2012. FIA’s volume-to-biomass conversion method generally underestimates biomass in comparison to published equations. Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012. GTR-NRS-P-105.

Dixon, G.E. 2003 (Revised January 2022). Essential FVS: A User’s Guide to the Forest Vegetation Simulator. Internal Rep. Fort Collins, CO: USDA Forest Service, Forest Management Service Center. 226p.

Drew, T. J., & Flewelling, J. W. (1979). Stand density management: An alternative approach and its application to Douglas-fir plantations. Forest Science, 25, 518-532.

Galvez, FB, Hudak, AT, Byrne, JC, Crookston, NL, Keefe, RF. 2014. Using climate-FVS to project landscape-level carbon stores for 100 years from field and LiDAR measures of initial conditions. Carbon Balance and Management 2014, 9:1.

Hoover, C.M., Rebain, S.A. 2010. Forest Carbon Estimation using the FVS: Seven things you need to know. USDA, Forest Service, Northern Research Station. General technical report: NRS=77.

IPCC. 2003. Good practice guidance for land use, land use change and forestry. The Institute for Global Environmental Strategies for the IPCC, Japan.

Smith, J.E., Domke, G.M., Nichols, M.C., Walter, B.F. 2019. Carbon stocks and stock change on federal forest lands of the United States. Ecosphere 10(3):e02637. 10.1002/ecs2.2637

Xiao, C.W., Yuste, J.C., Janssens, I.A., Roskams, P., Nachtergale, L., Carrara, A., Sanchez, B.Y., Ceulemans, R. 2003. Above- and belowground biomass and net primary production in 73-year-old Scots pine forest. Tree Physiol. 23(8):505-516. doi:10.1093/treephys/23.8.505.