Fertilization can compensate for decreased water availability by increasing the efficiency of stem volume production per unit of leaf area for loblolly pine (<i>Pinus taeda</i>) stands
Bibliographic record
Abstract
Over half of the standing pine timber volume in the southeastern USA is composed of loblolly pine (Pinus taeda L.), making it the most important tree species in the region. Future climate variability may impact productivity of these forests due to reduced water availability. To determine the effects of nutrient availability and decreased water availability on stand-level water use efficiency and growth efficiency, we examined the interactive effects of fertilization and reduced throughfall on whole-tree water use, stand-level canopy transpiration, leaf area index (LAI), and stand-level stem volume growth. This study was conducted over the 6th and 7th growing seasons (2013–2014) of a loblolly pine plantation in southeastern Oklahoma. Across all plots, throughfall reduction reduced volumetric soil water content (VWC) from 13.6% to 10.9% for soil depths of 0–12 cm and from 22.3% to 19.9% for soil depths of 12–45 cm and reduced stand volume growth from 20.9 to 17.9 m3·ha−1. Across all plots, fertilization increased LAI by 12%, increased stand volume growth from 18.3 to 20.5 m3·ha−1, and increased water use efficiency of stem volume production by 18%. These results indicate that fertilization can benefit stand growth of loblolly pine plantations even when soil moisture is limiting, in part, by increasing the efficiency of water use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".