Yield decline in Chinese-fir plantations: a simulation investigation with implications for model complexity
Bibliographic record
Abstract
A variety of competing hypotheses have been described to explain yield decline in Chinese-fir ( Cunninghamia lanceolata (Lamb.) Hook.) plantations. The difficulty in implementing field experiments suggests ecosystem modeling as a viable option for examining alternative hypotheses. We present a conceptual model of Chinese-fir yield decline and explore its merits using the ecosystem-based FORECAST model. Model results suggest that yield decline is caused primarily by a decline in soil fertility, largely as a consequence of slash burning in conjunction with short rotations. However, as tree leaf area declines, there is a transition (over subsequent rotations) from seed rain based competition to bud bank based competition, increasing the competitive impact of minor vegetation on tree growth. Short rotations increase understory survival between rotations and may cause a gradual shift from tree dominance to shrub/herb dominance over subsequent rotations. These effects are most evident on nutrient-poor sites, but understory competition poses a significant yield decline risk on good sites as well. We conclude that sustainable production in Chinese-fir plantations requires the avoidance of activities that compromise soil fertility and increase understory competition. The risk and severity of yield decline would be reduced by increasing rotation lengths and avoiding plantations on infertile sites.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".