Harnessing soil feedback from resistant bananas to suppress pathogens in susceptible varieties
Bibliographic record
Abstract
Abstract Recent evidence shows that plants can recruit beneficial microbiomes that contribute substantially to disease resistance. However, it remains unclear whether resistant varieties can generate soil microbial legacies that persist beyond a single growing cycle and influence subsequent plant health. We tested this hypothesis using two banana varieties differing in resistance to Fusarium wilt to condition soils. These conditioned soils were then used to grow the two banana varieties to assess whether prior soil conditioning affected disease outcomes. We further analysed the microbial communities in the conditioned soils to identify key taxa associated with disease suppression. Cultivation‐based approaches were also used to explore the pathogen inhibition potential of microbial populations that were enriched by the resistant banana variety. The soil legacy from the highly resistant variety positively influenced plant health, whereas the susceptible variety's soil led to pathogen enrichment. Microbial community analyses identified bacterial communities as being more responsive to feedback effects, while varietal differences more strongly influenced fungal communities. Beneficial microbes such as Dyella , Rhizobium and Sphingobium were enriched in the highly resistant variety's soil legacy, contributing to enhanced plant health. Synthesis and applications . Our findings highlight a nature‐based strategy for improving plant health through the intentional use of resistant crop varieties to engineer beneficial soil microbiomes. This approach could enhance the sustainability and disease resilience of modified agricultural systems. By informing the design of crop rotation and soil management practices, our results demonstrate how ecological processes can be actively managed to promote long‐term agroecosystem health.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".