A Simple Crop Phenology Algorithm in the Land Surface Model CN‐CLASS
Bibliographic record
Abstract
Land surface models are useful tools for estimating the contribution and response to climate change of C dynamics in various terrestrial ecosystems. In many land surface models, plant phenological algorithms are incorporated based on field studies in forests. However, to simulate adequately the C cycle over a large area, there is a need to include and validate algorithms for other ecosystems. The Carbon and Nitrogen‐coupled Canadian Land Surface Scheme (CN‐CLASS) is a land surface model that has been applied successfully to the study of C stocks in forest ecosystems. The objective of this study is to incorporate a simple crop phenology algorithm into CN‐CLASS and validate its ability to simulate C cycles at an agricultural site in southern Ontario, Canada. The model was validated on a corn crop (Zea mays L.) in 2005 and 2008 based on measurements of aboveground biomass and net ecosystem productivity (NEP), as well as a well‐tested agricultural model, DayCENT (the daily time‐step version of the CENTURY model). The modified CN‐CLASS showed similar dynamics of biomass allocation compared with field measurements and DayCENT simulations. Regression analysis indicated that the modifications improved the NEP simulation for a cornfield, with the coefficient of determination (R2) relating simulated and observed NEP increasing from 0.51 in the original CN‐CLASS to 0.78 in the modified model. Other crop species could be further validated to expand the model application to crop rotation studies and large areas covered by forests and crop fields in consideration of land management practices.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".