Influence of long-term application of N and S fertilizers (1980-2002) and liming in 1992 in dry matter yield of grass and soil properties in a dark gray Chernozem in north-central Saskatchewan
Bibliographic record
Abstract
Most soils are deficient in plant-available N in the Prairie Provinces of Canada, and in the Parkland region, many soils are also insufficient in available S for high crop yields. Long-term field experiments, initiated in 1980 and 1996 on a Dark Gray Chernozem loam soil at Canwood in north-central Saskatchewan, were conducted to determine the effects of N, S and lime application and forage removal on forage dry matter yield (DMY) and soil properties. The results indicated that application of N or S alone had only a little effect on DMY, while application of N together with S substantially increased DMY. Decline of soil pH by annual applications of N and S fertilizers mainly happened in the 0-5 cm layer. In layers below 10 cm, soil pH tended to increase with N or NS fertilization. Surface application of granular lime increased soil pH mainly in the 0-5 cm layer, and the high pH was maintained for at least 9 years. The TOC and TN mass in the 0-7.5 cm soil layer increased with annual applications of N and S fertilizers, and the increase was more pronounced with application of N and S together. In the subsoil layers, the N treatment tended to decrease, but the NS treatment tended to increase the TOC and TN. This suggests that application of N and S together was more effective in increasing C and N sequestration in a soil deficient in both N and S.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".