Economic Analysis of Growth Effects of Thinning and Fertilization of Lodgepole Pine in Alberta, Canada
Bibliographic record
Abstract
Abstract This study examined the economic profitability of eight combinations of thinning and fertilization treatments applied to 40-year-old natural stands of lodgepole pine (Pinus contorta Dougl. Var. latifolia Engelm) in Alberta, Canada. The eight treatments, consisting of four levels of nitrogen fertilizer application (0, 180, 360, and 540 kg ha−1) and two levels of thinning (thinned and unthinned), were applied in 1984 in a randomized complete block design with factorial treatments and nine replications per treatment. The diameters and heights of all trees on the experimental plots were measured in 1984, 1989, 1994, and 1999. A simple factorial analysis of variance (ANOVA) with the 1984 volume as a covariate showed that both fertilization and thinning increased volume growth significantly. Economic analyses showed that thinning without fertilization was the most profitable treatment combination. The ranking of profitability was based on the soil expectation value and assumed that the thinnings had a commercial value. If thinnings had no market value, then the unthinned treatments were more profitable than their corresponding thinned ones. The profitability ranking was robust for real discount rates of 4 to 10%. To improve economic profitability, fertilization of lodgepole pine should be carried out when the stands are in an optimal stand density range for the site to ensure that the increased growth is concentrated on the most valuable trees with the best growth potential.North. J. Appl. For. 22(4):254–261.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".