Nitrogen budget for fertilized carrot cropping systems in a Quebec organic soil
Bibliographic record
Abstract
Caron, J., Rancourt, G. T., Bélec, C., Tremblay, N. and Parent, L.-É. 2014. Nitrogen budget for fertilized carrot cropping systems in a Quebec organic soil. Can. J. Soil Sci. 94: 139–148. After reclamation, organic soils tend to shift from net N immobilization to net N mineralization. The N amounts mineralized annually can be sufficient to cover the N needs of the crop, reducing the need for supplemental N fertilization. The objective of this study was to assess N budgets and to infer the apparent N mineralization in fertilized and unfertilized carrot (Daucus carota L.) crops in an organic soil in southwestern Quebec. Five pre-plant N fertilization treatments (0, 25, 50, 75, and a split 25+25 kg N ha−1) were applied over a 3-yr period to non-irrigated carrots. Crop yield and plant and soil N contents were measured. Apparent net N mineralization was an important source of N, averaging 117 kg N ha−1yr−1. Crop yield was not affected by the N fertilization rate, by splitting of the fertilizer application or by the previous year’s crop. Increasing the N fertilizer rate increased the soil nitrate content below the root zone (∼40 cm below the surface), enhancing the risk of nitrate leaching. Thus, because soil N mineralization was sufficient for the carrots’ requirements in this organic soil, supplemental N fertilization was found to be unnecessary and could exacerbate the environmental risk of nitrate leaching.
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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".