Impacts of pine species, stump removal, cultivation, and fertilization on soil properties half a century after planting
Bibliographic record
Abstract
To better understand the long-term effects of species selection and forest management practices on soil quality and soil C retention, we analyzed soil samples from an experimental planting of loblolly ( Pinus taeda L.), longleaf ( Pinus palustris Mill.), and slash ( Pinus elliottii Engelm.) pines under different management intensities in Mississippi. The treatments included stump removal and cultivation (CULT), a one-time application of fertilizer combined with stump removal and cultivation (CULT+F), and a control (CON). After 49 years, pine species had no significant effect on any soil physical or chemical parameter examined, despite species differences in basal area. CULT exhibited significantly higher soil bulk density and lower soil C and soil N than CON and CULT+F in the upper 10 cm of soil. Stump removal is not a common practice in southern pine silviculture today; however, as demand for bioenergy fuels or feedstocks increases, more complete biomass utilization will be considered. Residual stumps play an important role in soil nutrient and C retention in pine plantations. Our results show that stump removal can lead to reduced soil C (–21%) and soil N (–35%) compared with controls, although it is possible to mitigate nutrient losses on poor sites with fertilization.
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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) | 0.001 | 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".