Evaluation of LEACHMN for simulating seasonal changes in plant available nitrogen across a variable landscape
Bibliographic record
Abstract
A model that accurately simulates the seasonal variation in nitrogen (N) dynamics may represent an important additional tool in N management. The objectives of this study were to evaluate whether seasonal changes in the amount of N that becomes available to a maize crop in a variable landscape can be simulated with LEACHMN and, in particular, to assess the potential value of LEACHMN in estimating N available at the time of the presidedress soil nitrate test (PSNT). Soil mineral N (SMN) and shoot N were measured biweekly over seven growing seasons in corn (Zea Mays L.) grown under conventional tillage in a variable landscape in Southern Ontario, Canada. The model was calibrated using data from 2002 and 2003 from each of five positions in the landscape and then evaluated using data from the 1997–2001 growing seasons. Although the model under-estimated SMN and over-estimated shoot N, the model was more successful in simulating the sum, defined as plant available N (PAN). Simulations of PAN were best at the summit and shoulder positions. The agreement between measured and simulated PAN were poorest early in the season. Although the accuracy of PAN simulations late in the growing season indicates this model has potential value in N management decisions, the errors in simulating SMN early in the season suggest adjustments are required before it can be used, along with other tools, as a substitute for the PSNT in cool humid environments. Key words: Soil mineral N, Plant available N, variable landscapes, LEACHMN
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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 | 0.000 | 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".