Development and Validation of a Crop and Nitrate Leaching Model for Potato Cropping Systems in a Temperate–Humid Region
Bibliographic record
Abstract
The Root Zone Water Quality Model (RZWQM) is a one-dimensional process-based model used for simulating major physical, chemical, and biological processes in agricultural systems. To date, the model has not been applied to potato production systems for simulating nitrate leaching. In this study, 35 datasets collected between 2009 and 2016 at a field under a three-year potato (potato–barley–red clover) rotation in Prince Edward Island (PEI), Canada, have been employed for calibrating and validating the water, nitrogen (N) cycling, and plant growth routines of RZWQM and for subsequently estimating nitrate leaching. The model fitness, evaluated using univariate and bivariate indicators, was rated as high for most of the parameters tested. As a result of the combined influence of higher infiltration and reduced plant uptake, the model showed that the highest leaching at the rotation level occurred between September and December. A secondary leaching period occurred in spring, when residual soil nitrate was mobilized by increased percolation due to snowmelt. Most of the nitrate leaching occurred during the potato year (89.9 kg NO3–N ha−1 y−1), while leaching for barley and red clover years had comparable values (28.6 and 29.7 kg NO3–N ha−1 y−1, respectively). The low N use efficiency of the entire rotation (i.e., 30.2%), combined with the high NO3–N concentration in leachate (i.e., 34.9 mg NO3–N L−1 for potato and 16.3 mg NO3–N L−1 for the complete rotation), suggest that significant efforts are required for adapting management practices to ensure sustainability of potato production systems.
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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 | 0.001 |
| 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".