Effects of nurse-tree crop species and density on nutrient and water availability to underplanted <i>Toona ciliata</i> in northeastern Argentina
Bibliographic record
Abstract
Cultivation of high-value hardwoods is often more difficult than cultivation of many pioneer species commonly used in fast-growing plantations. On some sites, the facilitative effects of nurse trees can be necessary for initial crop species establishment, but their competitive effects can also reduce juvenile growth rates of the crop species. To improve establishment success in mixed-species plantations, we tested the effects of the nurse-tree species Grevillea robusta A.Cunn. ex R.Br., Pinus elliottii Engelm. × Pinus caribaea Morelet, and Pinus taeda L. and four densities (0%, 25%, 50%, and 75% of the initial density) on Toona ciliata M.Roem. light, soil water, and soil nutrient availability. Growth of T. ciliata tended to increase with decreasing nurse-tree density and increasing light availability. However, growth was greater under G. robusta than under the pines, even where light conditions were similar, corresponding to mostly higher nutrient availability and higher soil water contents underneath G. robusta. Wood δ13C of T. ciliata was positively correlated with growth, foliar nutrient contents (N, P, K, Mg, Ca), and soil water content at a depth of 20–40 cm. Our results suggest that G. robusta is less competitive for soil nutrients and water than the pine nurse-tree species.
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".