Biochar–compost mixture and cover crop effects on soil carbon and nitrogen dynamics, yield, and fruit quality in an irrigated vineyard
Bibliographic record
Abstract
Effects of biochar–compost (B+Com) mixture and cover crop were assessed on soil and grapevine productivity in an irrigated Merlot ( Vitis vinifera L.) vineyard in Okanagan Valley, British Columbia (BC), Canada, from 2017 to 2020. The experimental design was a factorial arrangement of control, B+Com, cover crop, and combination of cover crop and B+Com (cover crop/B+Com) treatments in alleys with four replications. The B+Com comprised a 1:1 ratio of biochar and compost and was applied at a rate of 22 Mg ha−1 dry weight basis in May 2017 and 2019. The cover crop consisted of a dryland forage mixture and bird’s-foot trefoil ( Lotus corniculatus L.). B+Com treatment did not affect cover crop biomass or tissue C and N concentrations except for a 12% reduction in 2019 biomass. B+Com and cover crop/B+Com increased soil C content averaged across sampling dates by 11% and 17% ( P < 0.05), respectively, only at the 0–15 cm soil depth compared with the control. Cover crop treatment did not affect ( P < 0.05) soil C content at two soil depths in all sampling dates. Soil N content was not affected by B+Com, decreased by an average of 12.5% at both soil depths with cover crop, and increased with cover crop/B+Com by 4% only at the 0–15 cm soil depth averaged across sampling dates ( P < 0.05). Grape yield was increased by 32% by cover crop/B+Com relative to control only in 2020. The cover crop reduced petiole N and pruning weights in one or two years out of three.
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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".