Fertilisation with P, N and S requires additional Zn for healthy plantation tree growth on low fertility savanna soils
Bibliographic record
Abstract
Context Soil nutrient limitations characterise savanna soils globally and are one of several constraints to establishing productive tree plantations and enhancing economic opportunities in tropical regions. Fertilisation offers an approach to overcome soil nutrient limitations to maximise tree growth and health, but requires research on nutrient contents, composition, rates and methods of delivery in the context of soil characteristics. Aims To determine the optimal contents, rates and methods of application of fertiliser to maximise the growth and health of the plantation timber species Pinus caribaea on low fertility savanna soils. Methods Factorial field experiments tested growth responses to applications of phosphorus (P), nitrogen (N) and sulfur (S) on three soils near Darwin, Australia. Further experiments tested effects of zinc (Zn), copper (Cu) and potassium (K) application and small-scale variation in soil characteristics on tree performance. Key results Positive growth responses to P, N and S were recorded, yet unhealthy trees developed, particularly in better-performing treatments. Second phase experiments addressing potential causes of ill health confirmed Zn limitations. Intense spatial soil sampling demonstrated substantial variation in cation exchange capacity and composition over short distances. Conclusions Nutrient additions to enhance plantation tree growth will need to encompass minor and trace elements in addition to N, P and S, specifically Zn, and consider the mechanism of application. Implications Small-scale variability in cation exchange capacity and composition indicates that optimal fertilisation rates will vary spatially, and that soil sampling for site characterisation would be more accurate with replicated dispersed samples.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".