Organic waste composts improve soil fertility and alter development of pathogenic dagger and pin nematode populations in a crown gall-diseased vineyard
Bibliographic record
Abstract
Compost application has many benefits for soil fertility and grapevine performance. Composts introduce new nutrient sources and exogenous microorganisms that enhance soil nutritional status and diversify microbial populations. This can lead to pathogen suppression by beneficial soil microbes through a variety of different mechanisms. Grapevine crown gall (GCG), induced by Allorhizobium vitis, has detrimental impacts on grapevine performance and crop quality. Plant-parasitic nematodes may increase the frequency and/or severity of soil-borne A. vitis infections because they induce wounds on roots through which the bacterium can easily enter the vine and establish systemic infection. Compost prepared from organic materials has reduced pathogenic nematode populations in soil of other perennial crop plants. Therefore, we hypothesized that organic compost application would improve soil fertility parameters, plant performance, crop quality, yield, and reduce the severity of A. vitis infection via reduction in pathogenic nematode soil populations. Three organic waste composts were applied in-row in a Chardonnay (Vitis vinifera) vineyard infected with A. vitis and plant-parasitic nematodes. After three years, all composts increased total carbon and nitrogen (%), organic matter (%), and phosphorus (mg.kg–1 soil) content in soil. Compost containing peat moss decreased Paratylenchus population densities in soil, while vineyard waste compost only reduced Ximphinema population densities in one year. However, no meaningful changes in plant performance, crop quality, yield, or GCG severity were detected. Extreme winter and summer temperatures experienced during this study likely reduced grapevine performance overall and potentially decreased the impact of improved soil fertility and lower parasitic nematode populations on GCG disease.
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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 | 0.001 | 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".