Soil sampling protocol reliably estimates preplant NO<sub>3</sub><sup>−</sup>in SDI tomatoes
Bibliographic record
Abstract
Subsurface drip irrigation (SDI), because it can precisely deliver nutrients close to plant roots, could lead to carefully determined applications of fertilizer to meet crop needs and less risk of nitrate (NO3-) leaching to groundwater. Appropriate fertilizer applications, however, depend on an accurate assessment of the spatial distribution of the main plant macronutrients (N, P and K) in the soil profile before planting. To develop nutrient sampling guidelines, we determined the spatial distributions of preplant nitrate (NO3-), bicarbonate extractable phosphorus (Olsen-P) and exchangeable potassium (K) in the top 20 inches (50 centimeters) of subsurface drip irrigated processing tomato fields in three of the main growing regions in the Central Valley of California. Nutrient distribution varied with depth (P and K), distance from the center of the bed (NO3-) and growing region (NO3- and K). No depletion of NO3-, Olsen-P or K in the root feeding areas close to the drip tape was detected. Preplant NO3- ranged considerably, from 45 to 438 pounds N per acre (50 to 491 kilograms/hectare), the higher levels in fields with consecutive crops of tomatoes. A sampling protocol that growers could use, developed from analysis of the distribution results, provided reliable estimates of preplant NO3- as well as P and K in all surveyed fields.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 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".