Improving Soil Quality and Potato Productivity with Manure and High-Residue Cover Crops in Eastern Canada
Bibliographic record
Abstract
Under intensive low residue agricultural systems, such as those involving potato (Solanum tuberosum L.)-based systems, stagnant crop yields and declining soil health and environmental quality are common issues. This study evaluated the effects of pen-pack cow (Bos Taurus) manure application (20 Mg·ha−1) and cover crops on nitrate dynamics and soil N supply capacity, subsequent potato yield, selected soil properties, and soil-borne disease. Eight cover crops were tested and included grasses, legumes, or a mixture of legumes and grasses, with red clover (Trifolium pratense L.) used as a control. Forage pearl millet (Pennisetum glaucum L.) was associated with highest dry matter. On average, red clover had 88% higher total N accumulation than the treatments mixing grasses and legumes, and the former was associated with higher soil nitrate in fall before residue incorporation and overwinter, but this was not translated into increased potato yields. Pearl millet and sorghum sudangrass (Sorghum bicolor × sorghum bicolor var. Sudanese) were associated with lower soil nitrate in comparison to red clover while being associated with higher total potato yield and lower numerical value of root-lesion nematodes (Pratylenchus penetrans), although this was not statistically significant at 5% probability level. Manure incorporation increased total and marketable yield by 28% and 26%, respectively, and increased soil N supply capacity by an average of 44%. Carbon dioxide released after a short incubation as a proxy of soil microbial respiration increased by an average of 27% with manure application. Our study quantified the positive effect of manure application and high-residue cover crops on soil quality and potato yield for the province of Prince Edward Island.
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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 | 0.001 |
| 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) | 0.001 | 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".