Journal of Forestry, Southern Journal of Applied Forestry, & Forest Science.
Elevated Carbon Dioxide in the Atmosphere:
What Might It Mean for Loblolly Pine Plantation Forestry?
By John W. Groninger (e-mail: firstname.lastname@example.org), Kurt H. Johnsen, John R. Seiler, Rodney E. Will, David S. Ellsworth, and Chris A. Maier
Research with loblolly pine suggests that projected increases in atmospheric CO2 concentration will accelerate early growth and could result in shorter rotation length, reduced time until first commercial thinning, higher optimal planting density, and possibly higher maximum stocking level in managed stands. We discuss some of the physiological processes and stand dynamics that underlie these changes, as well as silvicultural strategies that may serve to ensure sustainability of intensively managed forest systems in the face of increasing CO2 and possible climate change. JOF 97(7):4-10
Projecting the Growth of Loblolly Pine in a Changing Atmosphere
H.T. Valentine, R.L. Amateis, H.E. Burkhart, T.G. Gregoire, D.Y. Hollinger, and D.W. MacFarlane
Abstract: Recent findings regarding the magnitude of the influence of carbon dioxide concentration on the rate of photosynthesis in loblolly pine have been incorporated into Pipestem, a model of carbon allocation and growth. Pipestem translates photosynthetic rates into rates of change in stand basal area, quadratic mean diameter, tree density, average tree height, average crown length, dominant tree height, and woody dry matter. Projections of loblolly pine growth were run under the assumption that the atmospheric concentration of CO2 will continue to increase by 1.6 ppm/yr, the average rate of increase in the last 10 yr. Standing crops of woody dry matter in 20-yr-old loblolly pine stands in Buckingham County, Virginia, are projected to increase, on the average, by 9.8% in 20 yr. It is concluded that the CO2 effect should be accounted for in long-term projections of loblolly pine growth. South. J. Appl. For. 23(4):212–216.
Linking Growth and Yield and Process Models to Estimate Impact of Environmental Changes on Growth of Loblolly Pine
Forest Science, February 2001, vol. 47, no. 1, pp. 77-82(6)
Baldwin V.C.; Burkhart H.E.; Westfall J.A.; Peterson K.D.
Supervisory Research Forester USDA Forest Service,
Southern Research Station, 200 Weaver Blvd., Asheville, NC, 28802, Phone: (828)
Fax: (828) 257-4894 email@example.com
University Distinguished Professor and Head Department of Forestry, Virginia Polytechnic Institute and State University, Blacksburg, VA, 24061, Phone: (540) 231-5483
Fax: (540) 213-3698 firstname.lastname@example.org
Graduate Research Assistant Department of Forestry, Virginia Polytechnic Institute and State University, Blacksburg, VA, 24061, Phone: (540) 231-6958 email@example.com
Computer Specialist USDA Forest Service, Southern Research Station, 2500 Shreveport Highway, Pineville, LA, 71360, Phone: (318) 473-7233
Fax: (318) 473-7273 firstname.lastname@example.org
PTAEDA2 is a distance-dependent, individual tree model that simulates the growth and yield of a plantation of loblolly pine (Pinus taeda L.) on an annual basis. The MAESTRO model utilizes an array of trees in a stand to calculate and integrate the effects of biological and physical variables on the photosynthesis and respiration processes of a target tree on an hourly basis. PTAEDA2 sums the quantities for individual trees to obtain stand results; MAESTRO computes values for one tree at a time. These models were linked to provide a tool for further understanding stand, climatic, and edaphic effects on tree and forest productivity. PTAEDA2 predicts the characteristics of trees grown at a given stand density, on a given site, for a given length of time. These characteristics (outputs) are then used as direct inputs into MAESTRO which assesses the expected impact of environmental changes on tree function. The results from MAESTRO are fed back into PTAEDA2 to update future predictions by modifying the site index driver variable of the growth and yield model. An equation that predicts changes in site index as a function of net photosynthesis, age, and trees per unit area is the backbone of the dynamic linkage. The model changes required to link PTAEDA2 to MAESTRO were developed and reported earlier. This article reviews the earlier work and reports research results quantifying the relationships between net photosynthesis and the PTAEDA2 growth predictors, thus providing the basis for the MAESTRO to PTAEDA2 feedback process and integration of these two models. FOR. Sci. 47(1)77–82.