Simulating switchgrass aboveground biomass and production costs in eastern Canada with the Integrated Farm System Model
Bibliographic record
Abstract
Switchgrass (Panicum virgatum L.) is a herbaceous perennial grass that can be used as bedding for livestock, planted in buffer strips, and used as biofuel, but it is still not widely grown in eastern Canada. The objectives of this study were to verify the performance of the Integrated Farm System Model (IFSM) in simulating switchgrass growth and to estimate its yield potential and production costs in eastern Canada. The performance of IFSM was assessed with dry matter (DM) yield of switchgrass (‘Cave-in-Rock’) measured over three growing seasons (2015–2017) in southern Quebec, Canada. The model performed reasonably well, with normalized root mean square errors of 19.5% for calibration and 27.9% for validation. Simulation results of potential yield and economic management over the long term (1986–2015) for five switchgrass production sites in eastern Canada indicated that average DM yields in Quebec City and Fredericton (9.6 and 9.7 t ha−1, respectively) were significantly lower than average DM yields in Saint-Hubert, Ottawa, and London (10.8, 10.4, and 11.0 t ha−1, respectively). Average annual production costs per tonne of DM for the spring harvest were higher at low-yield sites (CAD$66.67 and $64.50 for Fredericton and Quebec City, respectively) than at high-yield sites ($60.10, $62.82, and $60.08 for Saint-Hubert, Ottawa, and London, respectively). The IFSM-estimated production costs were within the range of the calculated values reported in other agro-economic analyses conducted in Ontario and Quebec.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".