Effects of liquid hog manure on soil available nitrogen status, nitrogen leaching losses and wheat yield on a sandy loam soil of western Canada
Bibliographic record
Abstract
Nikièma, P., Buckley, K. E., Enns, J. M., Qiang, H. and Akinremi, O. O. 2013. Effects of liquid hog manure on soil available nitrogen status, nitrogen leaching losses and wheat yield on a sandy loam soil of western Canada. Can. J. Soil Sci. 93: 573-584. Manure can improve soil quality and enhance crop yields. However, excessive manure use may contribute to nitrate leaching, particularly on coarse-textured soils. The objective of this study was to evaluate the effects of liquid hog manure (LHM) on wheat yield, soil available N and leaching on a sandy soil in Manitoba. Manure treatments included three rates of LHM applied to supply 64 (low), 128 (medium) and 192 kg N ha-1 (high) in 3 consecutive years (2002-2004). Unamended plots were used as a control. Available nitrogen was measured three times during each growing season at soil depths of 0-15, 15-30, 30-60, 60-90, and 90-120 cm. Undisturbed soil core lysimeters were used to measure leaching. Crop yields (grain and straw), N-uptake and N use efficiency were assessed at the end of each growing season. In 2002 and 2003, LHM had little effect on wheat yield, N nutrition and leaching due to below-normal rainfall in both years. In contrast, in 2004 when precipitation was above normal, LHM amendment increased grain yield and plant N-uptake. Relative to the control, grain yield was 20, 30 and 50% greater in the low, medium and high manure-N plots, respectively. Manure increased soil available N concentration 1.2-, 1.3- and 1.7-fold and induced additional -N leaching of 4.7, 28.4 and 54.5 kg ha-1 in the low, medium and high manure-N plots, respectively. Results suggest that LHM should be used with caution on sandy soils due to leaching potential in years of high precipitation and low crop yields in years of low 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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".