Spatial Variability of Pesticide Sorption: Measurements and Integration to Pesticide Fate Models
Bibliographic record
Abstract
Pesticide fate models are useful in testing the impact of agricultural management practices on the quality of water resources. The sorption parameter is among the most sensitive of the input parameters used by pesticide fate models. Numerous pesticide sorption experiments have been conducted in the past 75 years, but mainly focussed on surface soils. The batch equilibrium procedure is a conventional technique to quantify sorption parameters. In this chapter, we summarize selected batch equilibrium studies to demonstrate that sorption parameters vary widely among sampling points within soil-landscapes. We recognize that probability density functions (PDFs) have been incorporated into stochastic simulations of pesticide fate to help account for sorption spatial variability, but also show that PDFs can vary widely for different pesticides and/or soil depth. We propose that near-infrared spectroscopy (NIRS) can be used in combination with batch equilibrium techniques to more rapidly quantify the sorption variability of herbicides. We demonstrate that NIRS can be integrated into the Pesticide Root Zone Model version 3.12.2 to improve spatial resolutions for calculating the mass of herbicide leached to depth at the field scale. Better quantification of herbicide sorption variability and hence leaching potential will provide greater confidence in using pesticide fate models in regulatory practices, as well as in management programs that promote both sustainable agriculture and adequate environmental protection of water resources.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 teacher head, 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".