Fertilisation with P, N and S requires additional Zn for healthy plantation tree growth on low fertility savanna soils
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.002 | 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 it