584 Cover Crop Evaluations for Eastern Washington Vineyards
Bibliographic record
Abstract
The location of the Columbia and Yakima valleys present vineyard managers in eastern Washington with significant concerns, particularly low rainfall and wind erosion. Cover crops, as part of a complete management system, can reduce the effects of wind erosion in vineyards by stabilizing soil particles and reducing runoff. Cover crops also reduce weed biomass. During research conducted at Prosser, Wash., 175 foreign and domestic species were assessed for performance as cover crops. Using a screening process, nine species were chosen for evaluation in large commercial plots. Grass species included cereal rye, crested wheatgrass, Sherman Big Blue wheatgrass, perennial rye, pubescent wheatgrass, and three fescues. Legume species included two annual clovers ( Trifolium spp.) and two reseeding annual medics ( Medicago spp.). Unseeded, resident vegetation served as a control. Vine and soil water statuses were monitored regularly. Initial establishment of all species was delayed because of low rainfall throughout the growing season; thus performance varied for each species. Drought-tolerant grass species had better germination and establishment than legumes, due to planting method. In-row water status and vine water potentials remained constant throughout the main portion of the growing season. A mix of crested wheatgrass, perennial rye, and pubescent wheatgrass (Canada mix) gave especially good cover without affecting vine or soil water status. Weed biomass was reduced in most cases, with legumes having least effect; cereal rye, crested wheatgrass and the Canada mix had the greatest effect. Season-long suppression was best achieved with the Canada mix because of the nature of establishment. In this study, most drought-tolerant grasses performed better than legumes; however, with proper establishment, legumes can be a beneficial part of a sustainable agriculture system.
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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 | 0.000 |
| 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) | 0.004 | 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".