Coupling the STICS model and CATIMO equations to simulate the growth and nutritive value of alfalfa
Bibliographic record
Abstract
Abstract Although alfalfa ( Medicago sativa L.) is one of the most cultivated perennial forage crops in the world, little information exists on the possibility of simulating both its growth and nutritive value. Our objectives were to evaluate and improve the performance of the STICS 10.0.0 model in simulating the biomass and leaf area index (LAI) of alfalfa grown in eastern Canada, and the performance of the equations derived from the Canadian timothy model (CATIMO) to predict three attributes of alfalfa nutritive value: neutral detergent fiber (NDF), neutral detergent fiber digestibility (NDFd), and in vitro true digestibility (IVTD) of the dry matter with STICS outputs. STICS was first calibrated, and its performance to simulate the growth of two alfalfa cultivars in spring and summer was evaluated. Leaf and stem biomass outputs from STICS were then used to calibrate and evaluate the CATIMO nutritive value equations. Twenty‐four datasets were used from two cultivars (Oneida VR and Calypso) and five sites in eastern Canada. STICS succeeded in simulating the aboveground biomass (normalized root mean square error [NRMSE] ≤ 27%) and the LAI (NRMSE ≤ 21%). Taproot biomass and aboveground biomass N concentration were also sufficiently well simulated. The new parameterization and modifications of the CATIMO equations allowed the model to accurately simulate the alfalfa nutritive value with NRMSE under or equal to 15%, 11%, and 5% for NDF, NDFd, and IVTD, respectively. STICS, combined with the improved nutritive value equations, is therefore suitable to simulate alfalfa growth and nutritive value in future studies.
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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) | 0.000 | 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) | 0.000 | 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".