Nitrate Leaching in a Loamy Sand Soil Receiving Two Rates of Liquid Hog Manure and Fertilizer
Bibliographic record
Abstract
The effect of liquid hog manure (LHM) and commercial fertilizer on NO3−–N leaching was measured for 2 yr in a long‐term manure experiment on a loamy sand soil at Carberry, MB. The field experiment, sown to barley (Hordeum vulgare L.) and wheat (Triticum aestivum L.), comprised six treatments including two rates of LHM (28, 084 and 56,168 L ha−1 [2500 and 5000 gal acre−1, abbreviated LHM‐2500 and LHM‐5000, respectively]), two rates of fertilizer (abbreviated F‐2500 and F‐5000) corresponding approximately to available N in LHM‐2500 and LHM‐5000, compost (abbreviated Com‐2500) supplemented with urea to approximate available N in LHM‐2500, and an unamended control. In 2010, apparent losses amounted to 79 (112 kg ha−1), 55 (40 kg ha−1), 27 (19 kg ha−1), 24 (16 kg ha−1), and 6% (8 kg ha−1) of applied available N in F‐5000, Com‐2500, F‐2500, LHM‐2500, and LHM‐5000, respectively. In 2011, losses were higher in the F‐5000 (80%, 63.6 kg ha−1) and F‐2500 (79%, 31.5 kg ha−1) treatments than in LHM‐5000 (40%, 32 kg ha−1) and LHM‐2500 (9%, 3.5 kg ha−1). Treatments that received fertilizer lost more than half of the added N by leaching. The lack of yield difference between LHM‐2500 and LHM‐5000 suggested that application of LHM‐2500 was environmentally sound for the coarse sandy soil of the Carberry site. These findings demonstrate the potential for minimizing N leaching through judicious rates of LHM and fertilizer application. Core Ideas Fertilizer treatments had greater N loss than liquid hog manure (32 vs. 3.5 kg ha−1). Treatments that received fertilizer lost more than half of the added N by leaching. The lower rate of LHM led to the same crop yield with lower environmental impact.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".