Negative effects of fertilization on plant nutrient resorption
Bibliographic record
Abstract
Plants in infertile habitats are thought to have a high rate of nutrient resorption to enable them reuse nutrients more efficiently than those in fertile habitats. However, there is still much debate on how plant nutrient resorption responds to nutrient availability. Here we used a meta-analysis from a global data set of 9703 observations at 306 sites from 508 published articles to examine the effects of nitrogen (N) and phosphorus (P) fertilization on plant foliar N and P concentrations and resorption efficiency. We found that N fertilization enhanced N concentration in green leaves by 27% and P fertilization enhanced green-leaf P by 73% on average. The N and P concentrations in senesced leaves also increased with respective nutrient fertilization. Resorption efficiencies (percentage of nutrient recovered from senescing leaves) of both N and P declined in response to respective nutrient fertilization. Combined N and P fertilization also had negative effects on both N and P resorption efficiencies. Whether nutrient resorption efficiency differs among plant growth types and among ecosystems, however, remains uncertain due to the limited sample sizes when analyzed by plant growth types or ecosystem types. Our analysis indicates that fertilization decreases plant nutrient resorption and the view that nutrient resorption is a critical nutrient conservation strategy for plants in nutrient-poor environments cannot be abandoned. The response values to fertilization presented in our analysis can help improve biogeochemical models.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".