Effect of repeated manure application on potassium, calcium and magnesium in soil and cereal crops in Saskatchewan
Bibliographic record
Abstract
Increasing use of animal manures in Saskatchewan requires information on the effect of manure addition on the availability of soil K, Ca and Mg and their concentrations in plant tissue. To address these issues, we examined the effects of repeated application of liquid swine and solid cattle manure at low and high rates on extractable K, Ca and Mg in soils from three different long-term field trials in Saskatchewan, and on plant K, Ca and Mg concentrations in cereal straw grown on the soils. After 5 to 7 yr of manure application, extractable potassium in the soils was significantly increased, while extractable Ca and Mg tended to remain similar, or was decreased with swine manure addition. In the cereal straw, concentrations of K, Ca, and Mg were all increased by repeated swine manure application, such that there was no significant increase in the K/(Ca + Mg) ratio. However, the K/(Ca + Mg) ratio in the cereal straw grown on soil amended with the high rate of cattle manure was increased. These findings suggest that increased risk of tetany potential from manure application would mainly be associated with excessive application rates of cattle manure in these soils, but should be monitored in feeds grown on all manured soils.Key words:Soil extractable K, Ca, Mg and Na; cereal K, Ca and Mg concentrations, K/(Ca + Mg) ratio, tetany potential, urea, swine manure, cattle manure
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".