Seasonal Nitrogen Partitioning and Nitrogen Uptake of Carrots as Affected by Nitrogen Application in a Mineral and an Organic Soil
Bibliographic record
Abstract
An understanding of nitrogen (N) uptake and the partitioning of N during the season by the carrot crop ( Daucus carota subsp. sativus [Hoffm.] Arkang.) is required to develop more efficient N fertilization practices. Experiments were conducted on both organic and mineral soils to track the accumulation of dry matter (DM) and N over the growing season and to develop an N budget of the crop. Treatments included two carrot cultivars (`Idaho' and `Fontana') and 5 N rates ranging from 0% to 200% of the provincial recommendations in Ontario. Foliage and root samples were collected biweekly from selected treatments during the growing season and assessed for total N concentration. Harvest samples were used to calculate N uptake, N in debris, and net N removal values. Accumulation of DM and N in the roots was low until 50 to 60 days after seeding (DAS) and then increased linearly until harvest for all 3 years regardless of the soil type, cultivar, and N rate. Foliage dry weight and N accumulation were more significant by 50 to 60 DAS, increased linearly between 50 and 100 DAS, and reached a maximum or declined slightly beyond 100 DAS in most cases. The N application rates required to maximize yield on mineral soil resulted in a net loss of N from the system, except when sufficient N was available from the soil to produce optimal yield. On organic soil, a net removal of N occurred at all N application rates in all years. Carrots could be used as an N catch crop to reduce N losses in a vegetable rotation in conditions of high soil residual N, thereby improving the N use efficiency (NUE) of the crop rotation.
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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 | 0.000 | 0.001 |
| 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".