Neighbouring plants modify maize root foraging for phosphorus: coupling nutrients and neighbours for improved nutrient‐use efficiency
Bibliographic record
Abstract
Nutrient distribution and neighbours can impact plant growth, but how neighbours shape root-foraging strategy for nutrients is unclear. Here, we explore new patterns of plant foraging for nutrients as affected by neighbours to improve nutrient acquisition. Maize (Zea mays) was grown alone (maize), or with maize (maize/maize) or faba bean (Vicia faba) (maize/faba bean) as a neighbour on one side and with or without a phosphorus (P)-rich zone on the other in a rhizo-box experiment. Maize demonstrated root avoidance in maize/maize, with reduced root growth in 'shared' soil, and increased growth away from its neighbours. Conversely, maize proliferated roots in the proximity of neighbouring faba bean roots that had greater P availability in the rhizosphere (as a result of citrate and acid phosphatase exudation) compared with maize roots. Maize proliferated more roots, but spent less time to reach, and grow out of, the P patches away from neighbours in the maize/maize than in the maize/faba bean experiment. Maize shoot biomass and P uptake were greater in the heterogeneous P treatment with maize/faba bean than with maize/maize system. The foraging strategy of maize roots is an integrated function of heterogeneous distribution of nutrients and neighbouring plants, thus improving nutrient acquisition and maize growth. Understanding the foraging patterns is critical for optimizing nutrient management in crops.
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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".