Evaluating phosphorus loading from repeated manure applications to two Saskatchewansoils
Bibliographic record
Abstract
An understanding of the impact of repeated manure additions on soil phosphorus (P) is needed to determine appropriate manure application rates for prairie soils and manure sources. The objective of this study was to assess the loading of manure-derived phosphorus using P budgets and soil P measurement techniques applied to two Saskatchewan soils with known histories of manure application. Liquid hog manure (LHM) and solid cattle manure (SCM) treatments were applied annually over 8 yr to Black Chernozemic soils (Dixon site), and LHM was applied to Dark Brown Chernozemic soils (Plenty site) over 6 yr. Soil samples were collected in the spring of 2003 and 2004 and analyzed for labile P (Modified Kelowna extractable, Olsen extractable, water extractable, and Plant Root Simulator exchange resin methods) and total P. The P budgets were in good agreement with the observed patterns in labile soil P at the two sites. The annual application of LHM (37000 L ha-1 yr-1) at agronomic nitrogen (N) rates with no additional P fertilizer (Dixon only) did not significantly elevate soil labile P compared with the unfertilized controls at both Dixon and Plenty. The annual application of SCM (7.6 Mg ha-1 yr-1) at agronomic N rates did not significantly elevate soil labile P compared with the unfertilized control at Dixon; whereas of SCM at higher rates (15.2 Mg ha-1 yr-1 and 30.4 Mg ha-1 yr-1) showed some elevation in labile soil P. Measures of labile P were sensitive to P surpluses or deficits predicted by P balances, and all methods of measuring labile soil P were strongly correlated with one another at both sites (r ≥ 0.803 at P ≤ 0.01). Key words: Manure, cattle manure, nitrogen, phosphorus, soil test phosphorus
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".