Annual fertilization and interspecific competition control: effects on in situ forest floor nitrogen fluxes of different-aged <i>Pinus taeda</i> stands in southeast Georgia, USA
Bibliographic record
Abstract
Forest floor organic matter in managed pine forests can accumulate large quantities of N during early stand development. The conversion of the forest floor from a net accumulator to a net source of N as stands age, however, is not well quantified, nor is the effect of management activities on this conversion process. Nitrogen flux from the forest floor of different-aged Pinus taeda L. stands (8, 12, and 14 years old) was measured to understand the impact of annual fertilization (~70 kg N·ha1·year1) and complete interspecific competition control on forest floor N cycling. Throughfall and forest floor leachate solutions were collected for 1 year and N mineralization assays conducted. Volume-weighted mean concentrations were used in conjunction with hydrologic fluxes to estimate the net nitrate (NO3-N), ammonium (NH4+-N), and dissolved organic N flux from the forest floor. Complete competition control had no effect on the inorganic N flux. Changes in inorganic or organic N flux also were not discernable with stand age. Fertilization treatments, excluding fertilizer pulses, significantly increased the forest floor release of N in the fertilized stands compared with the nonfertilized stands (p < 0.05). Overall fluxes of NO3-N and NH4+-N from the forest floor were, respectively, 2.8 and 6.2 kg N·ha1·year1 for fertilized stands and 1.1 and 0.9 kg N·ha1·year1 for nonfertilized stands. Unlike inorganic N, organic N was retained in fertilized stands as a result of large organic throughfall inputs. Nitrogen cycling through the forest floor was ~4 kg·ha1·year1 greater in fertilized stands. This increased N release may account for as much as 6% of average annual pine demand and thus may well enhance productivity in future years.
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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 | 0.000 |
| 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) | 0.000 | 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 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".