Irrigation practices, nutrient applications, and mulches affect soil nutrient dynamics in a young Merlot (<i>Vitis vinifera</i> L.) vineyard
Bibliographic record
Abstract
There is growing interest among commercial wine grape (Vitis vinifera L.) growers in reducing water and fertilizer consumption, but little information exists on how best to combine conservative irrigation and soil management practices in the vineyard. In a 3-year-old Merlot vineyard in the semi-arid Okanagan Valley, British Columbia, the interactive effects of resource-conserving micro-irrigation (drippers or microsprinkers), nutrient applications (fertigation or compost), and surface mulching (wood and bark chips) on nitrogen (N) and phosphorus (P) dynamics in the wetted zone of surface soils were examined throughout the growing season using ion-exchange resins. Treatment differences in soil carbon and major nutrient pools, temperature, and moisture were also measured. Higher NO3-N was adsorbed by resins buried under drippers than under microsprinklers except in mulched plots, where NO3-N was uniformly low. By enhancing soil carbon availability and moderating soil microclimate, surface mulches may have promoted microbial immobilisation of N. Compost applications increased soil ortho-P levels, especially on mulched plots, suggesting that both P inputs (from compost) and enhanced microbial biomass (from mulch) promoted soil P cycling. Future work will examine the interactive effects of these resource-efficient practices on leaching losses, greenhouse gas emissions, crop productivity, and fruit quality.
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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 | 0.000 | 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 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".