Modeling the Biomass and Harvest Index Dynamics of Timothy
Bibliographic record
Abstract
Residual stubble, following harvest or the grazing of perennial grasses, is an important element of the C cycle in agricultural systems that has not been considered in most growth models. The stubble fraction from aboveground biomass can be quantified by predicting the harvest index (HI) of forage grasses, i.e., the amount harvested or grazed as a proportion of the aboveground biomass. A HI simulation module, using new functions to describe shoot apex height and plant weight density, was developed and integrated into the timothy ( Phleum pratense L.) growth model CATIMO. The model was calibrated and validated using data from two timothy cultivars and different N fertilizer rates from five experiments conducted in eastern Canada. The root mean square errors (RMSEs) between simulated and measured values for HI (0.11), shoot apex height (7 cm), stubble biomass (25 g dry matter m −2 ), aboveground biomass (68 g dry matter m −2 ), and harvestable biomass (76 g dry matter m −2 ) were acceptable with normalized RMSEs (13–41%) compared with the coefficients of variation of measured data (13–30%). The HI also has implications for determining forage nutritive value. This improved CATIMO model provides a framework to explore options for optimizing yield and nutritive value and to quantify the stubble biomass of timothy within the context of C sequestration.
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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) | 0.000 | 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 | 0.000 | 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".