Effect of Biochar on Ammonification and Nitrification in a Coarse Sandy Soil
Bibliographic record
Abstract
The addition of biochar to soil is believed to have positive effects on soil nutrient retention. Enhanced cation exchange capacity, water holding capacity and soil aeration are thought to be some of the benefits provided by biochar. In Alberta, reclamation of disturbed sites may be hastened by the addition of soil amendments and biochar is being studied as one possible option. More conventional amendments such as chemical fertilizer, compost, peat and forest floor material have been previously studied and compared in a reclamation setting. The objectives of the work presented in this thesis are to determine the effects of biochar on: 1) the fate of nitrogen applied to a nutrient-deficient, coarse-textured forest soil in the form of both inorganic and organic fertilizers; 2) the biological processes of ammonification and nitrification 3) the physical attributes responsible for nitrogen retention such as sorption of organic nitrogen and ammonium by negatively charged sites. The results of the experiments summarized in this thesis found that biochar reduced nitrogen leaching at an application rate of 25 tonne ha-1 and that biochar increased soil retention of nitrogen fertilizer, however the biological effects of biochar on ammonification and nitrification of soil organic nitrogen, can lead to nitrogen losses from soil, offsetting the increased storage capacity. The alteration of soil biogeochemistry by biochar in this experiment resulted in increased nitrification.
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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 | 0.001 | 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".