Cover crop microbiomes affect legume cash crop growth but not consistently through enriching nitrogen-fixing rhizobia
Bibliographic record
Abstract
ABSTRACT Harnessing plant-microbe interactions offers a promising path to reduce chemical inputs and enhance crop resilience in agricultural systems. However, microbial inoculants often fail to persist or function consistently across soils, which limits their broad utility. Here, we explore whether legume cover crops can be used to create microbial legacies that improve nodulation and nitrogen fixation in downstream legume cash crops. In a greenhouse experiment, we inoculated four cash crops with rhizosphere and nodule microbiomes derived from different legume cover crops, then used 16S rRNA and nifH amplicon sequencing to profile bacterial and diazotroph communities, respectively. Host identity shaped cover crop rhizosphere and nodule microbial communities, and specific taxa within these communities predicted nodulation and growth in some cash crops. Host specificity varied widely across cash crops, with alfalfa narrowly dependent on a specific symbiont and common bean and fava bean forming more permissive, taxonomically diverse nodule communities. Increased nodulation did not consistently improve biomass, and outcomes in some cash crops depended on more than symbiont compatibility alone. In particular, common bean growth was predicted by both rhizobial and non-rhizobial taxa, while soybean nodulation was shaped by its compatible symbiont as well as a mismatched rhizobial taxon associated with other hosts. Together, these results suggest that cover crops can shape cash crop microbiomes and productivity in host-specific ways, requiring precise symbiont matching in selective hosts but offering more flexible, multi-taxon management opportunities in permissive hosts.
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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".