Crop age is the main driver affecting alfalfa mosaic virus: The predominant virus in the alfalfa virome
Bibliographic record
Abstract
Abstract The alfalfa virome has been understudied. Existing research dealing with viral incidences within the crop primarily concentrate on local factors, rather than considering a broader perspective. In this comprehensive 2‐year study, we define the alfalfa virome and the main local and landscape factors affecting the incidence and annual increase of the Alfalfa mosaic virus (AMV), the most prevalent virus in alfalfa. The study was conducted in commercial alfalfa fields located along the highly productive northeast region of the Iberian Peninsula. For the first time in Europe, next‐generation sequencing revealed the presence of 14 different viruses representing the genera Cytorhabdovirus, Alphapartitivirus, Amalgavirus, Alfamovirus, Luteovirus, Enamovirus and Flavivirus. AMV was the most prevalent species, accounting for 89% of the identified viral contigs. Enzyme‐linked immunosorbent assays showed that the incidence of AMV varied between fields, with the average incidence doubling from 34% in 2019 to 65% in 2020. To assess the effect of local and landscape characteristics on the incidence of AMV and the observed annual increase, we selected different local variables and recorded landscape structure at three different buffer radii (250, 500 and 1000 m) from the centre of each field. Both the incidence and annual increase in AMV were driven mainly by local characteristics. The incidence of AMV showed a significant relationship with crop age and field area, whereas the annual increase was mainly influenced by crop age and the cumulative number of alate morphs of the aphid Acyrthosiphon pisum. Only one landscape composition variable, the percentage of alfalfa, showed a significant relationship with AMV incidence at the 250‐m scale. These results confirm the effect of local variables on the population structure of generalist viruses.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".