Renewal of Collaborative Research: Economically Viable Forest Harvesting Practices That Increase Carbon Sequestration
Bibliographic record
Abstract
Forests provide wildlife habitat, water and air purification, climate moderation, and timber and nontimber products. Concern about climate change has put forests in the limelight as sinks of atmospheric carbon. The C stored in the global vegetation, mostly in forests, is nearly equivalent to the amount present in atmospheric CO{sub 2}. Both voluntary and government-mandated carbon trading markets are being developed and debated, some of which include C sequestration resulting from forest management as a possible tradeable commodity. However, uncertainties regarding sources of variation in sequestration rates, validation, and leakage remain significant challenges for devising strategies to include forest management in C markets. Hence, the need for scientifically-based information on C sequestration by forest management has never been greater. The consequences of forest management on the US carbon budget are large, because about two-thirds of the {approx}300 million hectare US forest resource is classified as 'commercial forest.' In most C accounting budgets, forest harvesting is usually considered to cause a net release of C from the terrestrial biosphere to the atmosphere. However, forest management practices could be designed to meet the multiple goals of providing wood and paper products, creating economic returns from natural resources, while sequestering C from the atmosphere. The shelterwood harvest strategy, which removes about 30% of the basal area of the overstory trees in each of three successive harvests spread out over thirty years as part of a stand rotation of 60-100 years, may improve net C sequestration compared to clear-cutting because: (1) the average C stored on the land surface over a rotation increases, (2) harvesting only overstory trees means that a larger fraction of the harvested logs can be used for long-lived sawtimber products, compared to more pulp resulting from clearcutting, (3) the shelterwood cut encourages growth of subcanopy trees by opening up the forest canopy to increasing light penetration. Decomposition of onsite harvest slash and of wastes created during timber processing releases CO{sub 2} to the atmosphere, thus offsetting some of the C sequestered in vegetation. Decomposition of soil C and dead roots may also be temporarily stimulated by increased light penetration and warming of the forest floor. Quantification of these processes and their net effect is needed. We began studying C sequestration in a planned shelterwood harvest at the Howland Forest in central Maine in 2000. The harvest took place in 2002 by the International Paper Corporation, who assisted us to track the fates of harvest products (Scott et al., 2004, Environmental Management 33: S9-S22). Here we present the results of intensive on-site studies of the decay of harvest slash, soil respiration, growth of the remaining trees, and net ecosystem exchange (NEE) of CO{sub 2} during the first six years following the harvest. These results are combined with calculations of C in persisting off-site harvest products to estimate the net C consequences to date of this commercial shelterwood harvest operation. Tower-based eddy covariance is an ideal method for this study, as it integrates all C fluxes in and out of the forest over a large 'footprint' area and can reveal how the net C flux, as well as gross primary productivity and respiration, change following harvest. Because the size of this experiment precludes large-scale replication, we are use a paired-airshed approach, similar to classic large-scale paired watershed experiments. Measurements of biomass and C fluxes in control and treatment stands were compared during a pre-treatment calibration period, and then divergence from pre-treatment relationships between the two sites measured after the harvest treatment. Forests store carbon (C) as they accumulate biomass. Many forests are also commercial sources of timber and wood fiber. In most C accounting budgets, forest harvesting is usually considered to cause a net release of C from the terrestrial biosphere to the atmosphere. However, it might also be possible for commercial use of forests to contribute to terrestrial sequestration of C. The objective of the our research project is to determine whether shelterwood cutting regimes now being adopted in the commercial forests of Maine and other areas of the country can achieve these multiple goals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.035 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".