Processing Tomato Nitrogen Utilization and Soil Residual Nitrogen as Influenced by Nitrogen and Phosphorus Additions with Drip‐Fertigation
Bibliographic record
Abstract
Timely sufficient water supply through drip irrigation or fertigation may increase nutrient demand of processing tomato ( Lycopersicon esculentum Mill.) due to increases in yield production. However, excessive nutrient application could result in crop luxury uptake and enrichment in soil profile, especially mineral N, with the latter potentially causing environmental concerns. A study was conducted to determine the responses of crop N utilization and post‐harvest soil profile mineral N to fertilizer N and P additions under drip fertigated processing tomato in sandy loam soils from 2003 to 2005. Across the 3 yr, both fruit N removal and plant total N uptake were either linearly or quadratically related to fertilizer N rate, with 187 kg N ha −1 of fruit removal and 268 kg N ha −1 of plant total N uptake obtained at the maximum yield. Nitrogen uptake efficiency and apparent N recovery decreased linearly with increases in N rate. At the maximum fruit yield, N uptake efficiency was 0.71, and apparent N recovery was 51.7%. Post‐harvest soil profile (0–100 cm) mineral N increased with increases in fertilizer N rate, and at greater rates with fertilizer N applied at rates above those required for maximum fruit yield production. Of the soil residual N, 62% remained in the top 40‐cm soil layer. Addition of fertilizer P had no effects on plant N uptake, N uptake efficiency and post‐harvest mineral N in soil profile, presumably due to the high levels of soil test P. Beneficial management practices need to be developed to prevent soil N losses during the non‐growing season following production of processing tomato with drip fertigation.
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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 | 0.000 |
| 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".