Effects of climate change on forest plantation productivity in Chile
Bibliographic record
Abstract
Abstract Forest plantations in Chile occupy more than 2.2 million ha and are responsible for 2.1% of the GDP of the country's economy. The ability to accurately predictions of plantations productivity under current and future climate has an impact can enhance on forest management and industrial wood production. The use of process‐based models to predict forest growth has been instrumental in improving the understanding and quantifying the effects of climate variability, climate change, and the impact of atmospheric CO 2 concentration and management practices on forest growth. This study uses the 3‐PG model to predict future forest productivity Eucalyptus globulus and Pinus radiata . The study integrates climate data from global circulation models used in CMIP5 for scenarios RCP26 and RCP85, digital soil maps for physical and chemical variables. Temporal and spatial tree growth inventories were used to compare with the 3‐PG predictions. The results indicated that forest productivity is predicted to potentially increase stand volume (SV) over the next 50 years by 26% and 24% for the RCP26 scenario and between 73% and 62% for the RCP85 scenario for E. globulus and P. radiata , respectively. The predicted increases can be explained by a combination of higher level of atmospheric CO 2 , air temperatures closer to optimum than current, and increases in tree water use efficiency. If the effect of CO 2 is not considered, the predicted differences of SV for 2070 are 16% and 14% for the RCP26 scenario and 22% and 14% for RCP85 for the two species. While shifts in climate and increasing CO 2 are likely to benefit promote higher productivity, other factors such as lack insufficient availability of soil nutrients, events such as increasing frequency and duration of droughts, longer periods of extreme temperatures, competing vegetation, and occurrence of new pests and diseases may compromise these potential gains.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".