Impact of incorporated hay mulch on selected physical, chemical and microbiological properties of a loam soil in Atlantic Canada after five years of continuous potato production
Bibliographic record
Abstract
Maintenance of soil quality and crop productivity is a major concern under intensive potato (Solanum tuberosum L.) production. The effects of four consecutive annual applications of 0.00, 2.25, 4.50 and 9.00 t ha-1 wet hay on growing season soil moisture and thermal regimes, soil quality and yield were evaluated on a loamy Orthic Humo-Ferric Podzol between 1995 and 1999. Hay mulching increased soil moisture at the beginning of the growing season by 6.5 to 12.7%, with increases significant until June 24, September 07 and September 20 for the 2.25, 4.50 and 9.00 t ha-1 treatments, respectively. Growing season soil temperature of the 4.50 and 9.00 t ha-1 treatments were lower than control, but only by −0.2 and −0.8°C, respectively. Hay mulching increased soil organic carbon (SOC) of the plow layer (0–25 cm), which increased biological activity resulting in better soil aggregation with more macropores, faster saturated hydraulic conductivity and reduced bulk density. Soil air CO2 concentration was significantly correlated to SOC content, aggregation, porosity and saturated hydraulic conductivity. Hay mulching at 2.25 and 4.50 t ha-1 increased total potato yield over that of the unmulched control by 11–14%, but was insufficient to maintain soil productivity. Hay mulching at 9.00 t ha-1 may have been excessive in terms of crop yield as it showed no total yield benefits. Key words: Organic carbon, CO2 concentration, aggregates, porosity
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".