Nodulation and root growth increase in lower soil layers of water‐limited faba bean intercropped with wheat
Bibliographic record
Abstract
Abstract Below‐ground niche complementarity in legume–cereal intercrops may improve resource use efficiency and root adaptability to environmental constraints. However, the effect of water limitation on legume rooting and nodulation patterns in intercropping is poorly understood. To advance our knowledge of mechanisms involved in water‐limitation response, faba bean (Vicia faba L.) and wheat (Triticum aestivum L.) were grown as mono‐ and intercrops in soil‐filled plexiglass rhizoboxes under water sufficiency (80% of water‐holding capacity) and water limitation (30% of water‐holding capacity). We examined whether intercropping facilitates below‐ground niche complementarity under water limitation via interspecific root stratification coupled with modified nodulation patterns. While no significant treatment effects were measured in intercropped wheat growth parameters, water limitation induced a decrease in shoot and root biomass of monocropped wheat. Likewise, shoot biomass and height, and root length of monocropped faba bean significantly decreased under water limitation. Conversely, water limitation stimulated root biomass of intercropped faba bean in the lower soil layer (15–30 cm soil depth). Similarly, total nodule number of faba bean roots as well as nodule number in the lower soil layer increased under intercropping regardless of water availability. Under water limitation, intercropping also led to a significant increased nodule biomass (48%) in the lower soil layer as compared to monocropping. The enhanced nodulation in the lower soil layer and the associated increase in root and shoot growth provides evidence for a shift in niche occupancy when intercropped with wheat, which improves water‐limited faba bean performance.
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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 | 0.001 |
| 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".