Aboveground and belowground interactions in mixed plantations of <i>Eucalyptus globulus</i> and <i>Acacia mearnsii</i>
Bibliographic record
Abstract
This study investigated whether increased productivity in mixed plantations of Eucalyptus globulus Labill. and Acacia mearnsii de Wild when compared with monocultures could be explained by niche separation of the fine-root systems. For this purpose fine-root architecture, nutrient concentration, and fine-root distribution were examined in two horizons (0-15 and 15-30 cm) of mixed and pure stands. Investigations were carried out in 6.5-year-old plantations consisting of 100% eucalypts, 75% eucalypts + 25% acacia, 50% eucalypts + 50% acacia, 25% eucalypts + 75% acacia, and 100% acacia. Aboveground the two species interacted synergistically. Stem volume and tree height was highest in the 50:50 mixtures. For acacias, intraspecific competition was stronger than interspecific competition with eucalypts. Fine-root biomass and length density were similar for all species combinations, and there was no synergistic effect. The vertical distribution of fine roots and fine-root architecture were similar for acacias and eucalypts. This indicated that soil exploitation strategies may be similar, which can result in strong competition for soil resources. Fine-root nitrogen concentrations of eucalypts were highest in the 50:50 mixture. Improved productivity of mixtures appears to be a result of both canopy stratification and improved N nutrition of eucalypts through N fixation by acacias.
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) | 0.000 | 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".