Application of PESTFADE to Simulate Nitrate Leaching in a Fertigated Corn Field in Thailand
Bibliographic record
Abstract
In this paper, we describe the modification and application of a pesticide transport model to simulate nitrate leaching in irrigated soils. The model PESTFADE consists of Runoff/Erosion, Water Flow, Heat Flow and Convection–Adsorption–Diffusion–Degradation submodels. The Soil Conservation Service (SCS) equation for estimating runoff and the Universal Soil Loss Equation (USLE) for calculating soil erosion were modified so the model can evaluate chemical partitioning in runoff and sediments, as well as to take into account multiple chemical applications. The model was validated using measured data from a fertigated corn field at the AIT Experimental Farm, Thailand. Specifically, in 1999 and 2000 observed values of moisture content and nitrate concentrations in the unsaturated zone were used as a basis for verifying the validity of the simulated values. Also some error statistics were used to assess the accuracy and performance of the model in comparison with measured data. Validation results indicate that the model simulated moisture content quite well as reflected by the close fit between measured and predicted values in the upper 60 cm of the soil profile. Specifically, the measured moisture content at this depth ranged from 28–36% during the growing period in 1999 while model results gave values ranging from 29–40%. In 2000, measured values ranged from 30–34.5% and the corresponding simulated values ranged from 33–37%. Despite the slight overprediction by the model, the average relative error (RE) of 0.05 indicates that model results are within acceptable range. Simulated nitrate concentrations also provided good correspondence, with observed data for the 100 kg N ha−1 treatments in 1999 ranging from 0.7–1.1 mg L−1 and model results ranging from 0.5–0.98 mg L−1. The model generally underpredicted nitrate concentrations for both years and for other application rates, which perhaps attributed to the higher simulated values of moisture content allowing more leaching, and thus less nitrate remaining in the crop root zone. Nevertheless, model results are within acceptable range as reflected in the small values of RE and other statistical variables (RE ranged from 0.07–0.4 and SE from 0.12–0.7). Thus it can be inferred that the model can be adopted for simulating soil moisture distribution and nitrate leaching in a fertigated corn field under Thailand soil and climatic conditions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".