Tomato response to legume cover crop and nitrogen: differing enhancement patterns of fruit yield, photosynthesis and gene expression
Bibliographic record
Abstract
Excessive use of nitrogen (N) in crop production has impacted ecosystems by contaminating soil and water. Management of N in agriculture is therefore of global concern. Sustainable agriculture systems that use leguminous cover crops such as hairy vetch (Vicia villosa Roth) to fix N and enrich soil organic matter by fixing carbon provide an alternative strategy. N signalling pathways were found associated with delayed leaf senescence and disease tolerance of hairy vetch-grown tomatoes. To test whether N in hairy vetch is the only contributing factor leading to these phenotypes, we designed a pot experiment in the field to analyse growth and gene expression in tomatoes, one set with soil overwintered without a cover crop (bare soil) and the other with soil overwintered with a hairy vetch cover crop including the vetch residue on the soil surface. Additionally, supplementary N fertiliser was also provided to aid distinguishing tomato responses to vetch from those to inorganic N. Tomato fruit yield, plant biomass and photosynthesis were higher in plants grown in vetch than bare soil. Tomato growth and photosynthesis metrics exhibited a parabolic response to inorganic N in bare soil, suggesting the potential for N toxicity in pots with the highest N rate. Vetch-grown tomato plants mitigated these effects and maintained elevated photosynthetic rates at high inorganic N levels. Vetch-grown plants also mitigated a decline in expression of several genes regulating nitrogen and carbon metabolism and upregulated the defence-related gene, osmotin, relative to plants grown in bare soil. Thus, some of the positive responses of tomatoes to a hairy vetch cover crop observed in the field seem mediated by physiological cues other than the additional N provided by the vetch cover crop.
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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".