Predicting Nitrate Leaching under Potato Crops Using Transfer Functions
Bibliographic record
Abstract
Nitrate leaching is a major issue in many cultivated soils. Models that predict the major processes involved at the field scale could be used to test and improve management practices. This study aims to evaluate a simple transfer function approach to predict nitrate leaching in sandy soils. A convective lognormal transfer (CLT) function is convoluted with functional equations simulating N mineralization, plant N uptake, N fertilizer dissolution, and nitrification at the soil surface to predict solute concentrations under potato (Solanum tuberosum L.) and barley (Hordeum vulgare L.) fields as a function of drainage water. Using this approach, nitrate flux concentrations measured in drainable lysimeters (1-m soil depth) were reasonably predicted from 29 Apr. 1996 to 3 Dec. 1996. With average application rates of 16.9 g m(-2) of N fertilizer in potato crops, mean nitrate-leaching losses measured under potato were 8.5 g N m(-2). Tuber N uptake averaged 9.7 g N m(-2) and soil mineral N at start (spring) and end (fall) of N mass balance averaged 1.7 and 4.5 g N m(-2), respectively. Soil N mineralization was estimated by difference (4.3 g N m(-2) on average) and was small compared with N fertilization. Small nitrate flux concentrations at the beginning of the cropping season (May) resulted mainly from initial soil nitrate concentrations. Measured and predicted nitrate flux concentrations significantly increased at mid-season (July-August) following important drainage events coupled with complete dissolution and nitrification of N fertilizers, and declining N uptake by potato plants. Decreases in nitrate concentrations before the end of year (November-December) underlined the predominant effect of N fertilizers applied for the most part at planting acting as a pulse input of solute.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".