Cover crops effects on grape yield and yield quality, and soil nitrate concentration in three vineyards in Ontario, Canada
Bibliographic record
Abstract
The biodiversity and resilience of wine grape production systems in humid temperate regions can be enhanced by cover crops. Individual and mixed species of cover crops were evaluated in 2014 and 2015 for their effect on vineyard productivity and soil properties in Prince Edward County (PEC), Niagara, and Lake Erie North Shore (LENS) in Ontario, Canada. Treatments included annual ryegrass (AR, control), annual ryegrass and red clover (AR+RC), annual ryegrass and forage radish (AR+FR), creeping fescue and micro clover (CF+MC), and a mixture of cover crops including oats, Italian ryegrass, red clover, alfalfa, alsike clover, and forage radish (Super Mix) and were replicated three times. Soil samples were collected once at bud break for soil properties (0-15 cm depth) and four times during the growing season for mineral N (0-30 cm depth; bud break, flowering, veraison and harvest). Results showed AR+RC and AR+FR treatments had the highest biomass in the PEC region, while AR+FR had the highest biomass in the Niagara and LENS regions averaged across years. The CF+MC treatment generally had poor establishment. Weed biomass was negatively correlated with cover crops biomass. Grape yield or yield quality (Brix, TA and YAN) and soil properties were not affected by treatments, except for AR that resulted in lower grape yield compared to other treatments in PEC in 2014. Soil nitrate concentrations, among treatments or sampling dates in each location, were not different in 2014. In 2015, soil nitrate concentrations were significantly higher in AR+RC and Super Mix treatments compared with the AR treatment only in Niagara and PEC. In conclusion, the AR+RC or AR+FR cover crops in the PEC vineyard and AR+FR in the Niagara and LENS vineyards showed high biomass and weed suppression compared to annual ryegrass with minimal cost differences.
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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) | 0.000 | 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".