Leaf Tissue Testing and Soil and Plant Tissue Relationships for Nitrogen Management in Carrots
Bibliographic record
Abstract
Nitrogen (N) management in carrot (Daucus carota L. var sativus) production systems is critical for increasing efficiency of crop production, decreasing costs, and decreasing nitrate leaching losses to groundwater. Leaf tissue testing may be an appropriate method to monitor and meet carrot N requirements. A field trial was conducted in three locations to 1) determine if “critical tissues” identified in previous research are appropriate for leaf tissue testing in N management of carrots, 2) determine the effects of various N regimes on soil and tissue N concentrations, 3) describe the relationships among soil N concentrations, tissue N concentrations, and yield for several N regimes, and 4) study the effects of N regimes on growth, yield, and recovery of marketable grades of carrots. Nitrogen critical tissues for leaf tissue testing were not useful in N management. Overall, results showed no significant differences in soil and tissue N levels due to increasing N regimes. Correlations among soil, tissue, and yield differed at each harvest but most were not significant. N concentration was higher in soils at a depth of 0–15 cm compared to 15–30 cm. Total N concentrations in tissues decreased over sequential harvests. No clear relationships emerged comparing tissue NO3‐N to soil N measurements over the entire growing season. There were no significant differences in growth and yield of carrots in response to N regimes. Interestingly, a N rate of 0 kg/ha had significantly more fancy‐grade carrots than a N rate of 200 kg/ha. There were no significant differences in culls due to increasing N application.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".