Herbage yield and crude protein concentration of rangeland and pasture following hog manure application in southeastern Alberta
Bibliographic record
Abstract
Intensive hog production is expanding into semi-arid regions of Alberta, where perennial forage lands are increasingly targeted for manure applicati on despite limited guidelines for its efficient use. Herbage yield and crude protein were assessed over two consecutive years within two native rangelands and two tame pastures, following different rates (10, 20, 40, 80 and 160 kg ha-1 NH4-N), methods (surface banding vs. subsurface injection) and seasons (fall vs. spring) of one-time liquid hog manure (LHM) application. Increasing manure rates improved grass yield across all sites the first growing season after treatment, from 1626 to 3576 kg ha-1. Although absolute increases in production were greatest on tame pasture, relative yield increases were similar among sites. Average crude protein (CP) concentration also increased from 69 to 91 g kg-1 in the first year. Despite low rainfall and the absence of a yield response in the second year, grass CP and crude protein yield (CPY) were maximized with increased manure application, highlighting the positive effects of manure on forage production, even with drought. Forb yields demonstrated variable effects among sites, with increasing manure decreasing alfalfa and increasing native forbs. Overall, both semi-arid tame pastures and native rangelands responded positively to LHM application, highlighting the complementary nature of hog and forage production under these conditions. Key words: Crude protein yield, forage, hog manure, injection, native rangeland, precipitation
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".