Biowaste usage for soil erosion control and soil physical improvement under potatoes (<i>Solanum tuberosum</i>) in Atlantic Canada
Bibliographic record
Abstract
Using potatoes (Solanum tuberosum L) as a test crop and standard erosion plots, a long-term study was done to assess the overall effects of straw mulch, compost (potato culls + manure + sawdust) and liquid pig manure (LPM) on soil physical properties, soil organic matter (SOM), erosion amounts and crop yield on a fine sandy loam in Prince Edward Island. The study occurred in two experimental phases separated by fallow periods. Phase 1 assessed treatment effects of straw mulch and compost during 1996-1999, and Phase 2 assessed treatment effects of LPM and compost during 2001-2002 and 2005-2006. Soil physical properties, mostly compaction-related, were penetration resistance, shear strength (TO), bulk density (BD), saturated hydraulic conductivity (HC), water content (SWC) and aggregate stability (AgSt), which, overall, were improved up to 27% with compost, the outstanding amendment. In Phase 1, compost significantly increased potato yield 9%. Runoff and sediment were, respectively, reduced with compost by 15 and 33%, and with mulching by 42 and 73%. Potato yield showed significant negative relations to soil compaction, whereby BD and TO, respectively, accounted for up to 89 and 70% of variation attributable to regression. In Phase 2, compost and LPM significantly increased yield by 23 to 38%. Compost alleviated soil compaction significantly, reducing BD by 14% and TO by 15 to 22%. It increased SOM almost 30%, AgSt almost 10%, SWC about 6% and HC more than twofold. Pig manure did not affect soil compaction for the most part or SOM, but increased AgSt and HC by 5 and 67%, respectively.Key words: Compost usage, straw mulching, liquid pig manure, soil organic matter, land degradation, soil erosion, soil conservation, soil improvement
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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 | 0.001 | 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".