Polyphosphate application influences morpho-physiological root traits involved in P acquisition and durum wheat growth performance
Bibliographic record
Abstract
BACKGROUND: Among phosphate (P) fertilizers, polyphosphates (PolyPs) have shown promising results in terms of crop yield and plant P nutrition. However, compared to conventional P inputs, very little is known on the impact of PolyPs fertilizers on below- and above-ground plant functional traits involved in P acquisition. This study aims to evaluate agro-physiological responses of durum wheat variety ´Karim´ under different PolyPs applications. Three PolyPs fertilizers (PolyA, PolyB, and PolyC) versus one orthophosphate (OrthoP) were applied at three doses; 30 (D30), 60 (D60), and 90 (D90) kg P/ha under controlled conditions. The PolyPs (especially PolyB and PolyC) application at D60 significantly increased morphophysiological root traits (e.g., RL: 42 and 130%; RSA:40 and 60%), shoot inorganic P (Pi) content (159 and 88%), and root P acquisition efficiency (471 and 296%) under PolyB and PolyC, respectively compared to unfertilized plants. Above-ground physiological parameters, mainly nutrient acquisition, chlorophyll content and chlorophyll fluorescence parameters were also improved under PolyB and PolyA application at D60. A significant and positive correlation between shoot Pi content and rhizosphere soil acid phosphatase activity was observed, which reveal the key role of these enzymes in PolyPs (A and B) use efficiency. Furthermore, increased P uptake/RL ratio along with shoot Pi indicates more efficient P allocation to shoots with less investment in root biomass production under PolyPs (especially A and B). CONCLUSIONS: Under our experimental conditions, these findings report positive impacts of PolyPs on wheat growth performance, particularly on photosynthesis and nutrient acquisition at D60, along with modulation of root morpho-physiological traits likely responsible of P acquisition efficiency.
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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 | 0.000 | 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".