During the project we reviewed many literature positions containing yield tables and models for various species.

[loblolly pine] [slash pine] [longleaf pine] [shortleaf pine] [pine-hardwood] [hardwoods] [other/multiple species]

For simulations we made certain simplifications and defined the following species groups.

More references on yield tables can be found, among others, in the following publications:

Baldwin, V. C., Jr., and Q. V. Cao. 1999. Modeling forest timber productivity in the South: where are we today? P. 487-496 in Proc. of the Silvic. Res. Conf. USDA For. Serv. Gen. Tech. Rep. SRS-30.

Daniels, R.F., Burkhart, H.E. 1988. An integrated system of forest stand models. For. Ecol. Manage. 23: 159-177

In the simulations we decided to limit a number of species groups defined in the program. The meaning of "species group" can be different in different scenarios. This is not the same as e.g., "loblolly pine" or "softwood". Species groups are to be defined according to current forest management practices, simulation objectives (e.g., hardwoods-softwoods vs. detailed species list), and significant differences in growth and yield (causing by different regions or management practices).

To define any species group we have to answer several questions and decide if certain factors are important or not. Even for given species, as loblolly pine, we have to know at least, how it is regenerated (naturally or artificially), how it's managed (as traditional plantation, improved plantation, intensively managed plantation or CRP area), and in which region it grows. All above factors have significant impact on species growth and yield.

OPTIONS doesn't have built-in yield tables or growth models. Its construction allows to use of any data from existing yield tables or tables generated by any growth models. Provided yield tables should be built for stands without any treatments. Responses of stands on silvicultural treatments are realized by using special factors (treatment response factors). There is also necessary to provide separated sets of yield tables for natural and planted stands.

Using of growth models or yield tables for large-scale simulations is relatively more difficult as compared to small objects, as single forest stands. Difficult issue is lacking of yield tables for many species, especially hardwoods. Existing yield tables have different formats and assumptions (e.g., different site index base age) and very often are also only "partial" models, including e.g., only height growth, basal area or volume. Searching for yield tables and growth models gave us a lot of them, but most of them are relatively old. The oldest were published more than 40 years old. Only a few new research results are available, but mainly for pine plantations. Very often there are no yield tables for stands of different origin and no sufficient information about growth and yield of some stands, as e.g., CRP stands or intensively managed plantations. There is also insufficient information on influence of treatments, as weed control, pre-commercial thinning, fertilization, thinning, or genetic improvements on growth rate.

Yield tables in Options work for species groups managed according to particular management regime. Assuming management practices includes such a topics as silvicultural treatments, management regimes and regime allocation table. Belonging to specific management regime causes in modification of growth and yield of the stands.

Analyzing the FIA data we concluded, that just a few species such as shortleaf and loblolly pine, longleaf and slash pine, red oak, sweetgum, yellow poplar, tupelo-blackgum and white oaks, account for about 80% of volume/biomass of forests. We also noticed signifficant differences in management and growth properties of natural / planted / intensivelly managed pines and hardwoods growing in the stream management zones. We also had to consider the ability of recognizing certain species using satellite imagery as we used it to create spatially explicit forest polygons, and FIA definitions of species froups as we used FIA data to fill Landsat-based polygons.

Finally for the large-scale simulations we considered the following species groups:

• natural pine - consisting of natural pine forests
• planted pine - consisting of planted pine stands under extensive (traditional) management
• intensivelly managed pine plantations - planted pine under intensive management;
• oak-pine forest type - forests in which hardwoods (usually upland oaks) constitute a plurality of the stocking but in which pines account for 25 to 50% of stocking
• upland hardwoods - consisting of hardwood forests classified as an oak-hickory or maple-beech-birch forest type
• bottomland hardwoods - consisting of deciduous forests with a forest type of oak-gum-cypress, elm-ash-cottonwood, palm, or other tropical species

For each of these species groups we tested a number of yield tables and finally chose the most appropriate:

• natural pine stands: Schumacher and Coile (1960)
• planted pine stands without intensive management: Harrison and Borders (1996)
• intensivelly managed pine plantations: since IMPs grow 2-4 times faster than PSOF (Borders and Bailey 2001, Siry 1998, Siry et al. 2001), we applied the planted pine yield tables with conservatively doubled growth rates to estimate growth of IMPs.
• oak-pine forest type: simplified yield tables developed using the FIA database
• upland hardwoods: Gingrich (1971); because of lower volumes for this species group as compared to bottomland hardwood, table values were adjusted by a 0.9 factor.
• bottomland hardwoods: Gingrich (1971)

Since there are differences between yield tables estimates and real inventory, we had to adjust the volume predicted by the yield tables using appropriate adjustment factors. They were calibrated in such a way that distribution of total volume by broad species groups and site index classes from the predicted volume for the time of inventory matched the volume published in the relevant FIA report (Thompson 1998). Scaling modeled estimates of the total inventory to the actual value of the estimator from the ground measurements, which is an unbiased valid statistical sample representing the resource availability, removes any inconsistencies potentially introduced by the tables and provides an unbiased estimation.

References:

Borders, B.E., Bailey, R.L., 2001. Loblolly pine - pushing the limits of growth. South. J. Appl. For. 25(2):1-6.

Gingrich, S.F. 1971. Management of young and intermediate stands of upland hardwoods. U.S. Department of Agriculture, Forest Service, Northeastern Forest Experiment Station, Res. Pa. NE-195. 26p.

Schumacher, F.X., Coile, T.S. 1960. Growth and Yields of Natural Stands of the Southern Pines. T.S. Coule Inc., Durham, N.C. 115p.

Siry, J.P., 1998. Southern plantation pine yield tables. SOFAC Rep. Research Triangle Park, NC: Southern Forest Resource Assessment Consortium. 5p.

Siry, J.P., Cubbage, F.W., Malmquist, A.J., 2001. Potential impacts of increased management intensities on planted pine growth and yield and timber supply modeling in the South. For. Prod. J. 51 (3): 42-48.

Thompson, M.T., 1998. Forest Statistics for Georgia, 1997. Resource Bulletin SRS-36. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 92p.

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