Long-term response of spruce–fir stands to herbicide and precommercial thinning: observed and projected growth, yield, and financial returns in central Maine, USA
Bibliographic record
Abstract
Herbicide application and precommercial thinning (PCT) are common silvicultural treatments used across North America and Europe. Despite this widespread use, long-term growth and yield responses from controlled experiments that include both of these treatments are relatively rare. We used 40-year growth and yield responses of spruce–fir stands to various combinations of early herbicide and PCT in a long-term silvicultural experiment in central Maine, USA, to calibrate the Forest Vegetation Simulator (Northeast Variant). Using the calibrated model, we projected rotation-length outcomes for stand development, merchantable wood volumes, and stumpage-based financial returns. Projections indicated gains in total yield (17%–31%) from herbicide treatments at the end of the rotation (∼60 years postharvest) relative to untreated stands. Substantial increases in merchantable wood volume also were achieved with PCT. Twenty-four years after PCT, stand stumpage value averaged $907 USD·ha −1 higher than that for unthinned stands. Total yield and stumpage gains from PCT were projected to continue through the end of rotation. Highest stumpage values resulted from combined herbicide and PCT treatments, followed by PCT-only and then herbicide-only. At end of rotation, highest net present value (NPV) resulted from PCT, whether alone or in combination with herbicides. PCT and herbicide investments substantially improved the NPV relative to untreated stands when using discount rates of 2% and 4%, but not when using a 6% rate. Our results documented that good financial returns are possible over the long-term from early investments in herbicide and PCT treatments in Maine spruce–fir stands.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".