Nitrogen mineralization in beef- and pig-manure-amended soils measured using anion resin method
Bibliographic record
Abstract
Ige, D. V., Sayem, S. M. and Akinremi, O. O. 2015. Nitrogen mineralization in beef- and pig-manure-amended soils measured using anion resin method. Can. J. Soil Sci. 95: 305–319. A major challenge facing the widespread use of manure is the uncertainty about its fertilizer equivalence. This study was carried out to determine the fertilizer equivalence of locally available manures in two soils in Manitoba. A randomized complete block design was adopted with six treatments [nitrogen fertilizer, a liquid swine manure (LSM), three solid beef manures (SBM) and a control] and four replicates. Each treatment was applied to a cylindrical soil column installed at the site at the rate of 100 kg ha−1 of “available N”, and leached NO3-N was captured by resin bags at the bottom of the cylinder. The soils and resin bags removed from the cylinders were sampled at 0, 1, 2, 4, 6, 8, 10, 14, and 18 wk following treatment application. Ammonium nitrogen in the amendments was nitrified within the first 2 to 3 wk with significant build-up of NO3-N in the soil above the control (P<0.05). The greatest available N was in the fertilizer treatment, followed by the LSM and the smallest was in the SBM. The available N in the three SBM was statistically similar (P>0.05). Approximately 50% of the inorganic N in LSM was available during the growing season, while 68 to 100% of SBM inorganic N was available. Between 4 and 25% of the organic N in the three SBM was mineralized during the growing season. High soil moisture hindered N mineralization and enhanced N loss in the clay soil. LSM has the greatest fertilizer equivalence, with a mean of 65 to 68%, of the four manure types used. The fertilizer equivalence of the three SBM ranged between 42 and 59% and was influenced by the manure C:N ratio and the soil environmental conditions. Our study suggests the need to revise the assumptions regarding manure N availability by considering soil environmental factors in the estimation of available N.
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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) | 0.001 | 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) | 0.001 | 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".