Effect of rate, frequency and method of liquid swine manure application on soil nitrogen availability, cropperformance and N use efficiency in east-central Saskatchewan
Bibliographic record
Abstract
A study was initiated in the fall of 1996 in the Black soil zone in east-central Saskatchewan (parkland region) to examine the soil and crop response to application of liquid swine manure at different rates, frequencies and methods of application. Low, medium and high rates of liquid swine manure (equivalent to approximately 100, 200 and 400 kg total N ha-1, respectively) were applied annually and in reduced frequency applications using injection and broadcast/incorporated placement over a 4-yr period. Crops grown during this period were Argentine canola (Brassica napus L.) in 1997, hard red spring wheat (Triticum aestivum L.) in 1998, hulless barley (Hordeum vulgare L.) in 1999, and Argentine canola in 2000. Under an annual application regime, a significant elevation of pre-seeding available N in the 0–60 cm soil depth and increased grain yield and protein content with increasing application rates of liquid swine manure were observed. Under a reduced frequency application regime, elevation of pre-seeding available N, grain yield and protein content observed in the year of application declined in the second year, and were significantly diminished by the third year. Cumulative N use efficiency (NUE) was highest (50–60%) for low annual application and lowest (10–30%) for high annual application rates that were injected. Generally, injection of liquid swine manure into the soil resulted in better enhancement of pre-seeding available N, higher grain yield and protein content, and better NUE than broadcasting and incorporation. Type of opener used to inject swine manure had no significant effect on either crop response or available N. This study showed that in the Black soil zone of the parkland region of Saskatchewan, annual application of low to medium rates (100 to 200 kg total N ha-1) of liquid swine manure are sufficient for high grain yield and grain protein, without leaving excess nitrates in the soil. In contrast, annual application of high rates (400 kg total N ha-1) of liquid swine manure has no agronomic advantage over the lower rates, but may result in higher residual nitrates in the soil, increasing potential for environmental pollution. Key words: Swine manure, N availability, manure management, N use efficiency
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".