Rock Phosphate and Biochar Effects on Greenhouse Gas Emissions and Soil Fertility in Southern Alberta Potato Field
Bibliographic record
Abstract
The application of biochar soil amendment and rock phosphate fertilizer to the soil could lead to achieving net zero emissions and food security; however, the effectiveness of using biochar and rock phosphate in southern Alberta brown chernozemic soil is not yet assessed. In the first trial of this study, varying levels of urea (N), with varying levels of biochar (B) were applied. The same level of rock phosphate (RP) and triple super phosphate (TSP) were applied to compare efficiency of the two fertilizers. The second trial involved applying the same nutrients, but with higher levels than the first trial. The results indicated that the fertilizer application rates did not affect the growth and yield of potatoes because of the application rates of the fertilizer and fertility status of the soil, whereas in the second trial, TSP and RP treatments had the same growth and yield. Furthermore, the high application rate of nitrogen fertilizer and RP with or without biochar in the potato plot emitted high nitrous oxide gas emissions while the low application rate of nitrogen fertilizer and RP kept the carbon dioxide in the soil. Nevertheless, in the second experiment, the application of biochar and rock phosphate reduced nitrous oxide and carbon dioxide emissions. We observed high residual nitrogen and manganese nutrients after harvest in the plot treated to rock phosphate and biochar. Therefore, rock phosphate and biochar have the potential to increase food production and mitigate climate change.
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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 | 0.001 | 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".