Improved ALMANAC simulations of upland switchgrass ecotypes in the northern United States
Bibliographic record
Abstract
Abstract This investigation used a modified parameterization of the Agricultural Land Management Alternative with Numerical Assessment Criteria (ALMANAC) model to improve simulated growth and biomass yield of upland switchgrass (Panicum virgatum L.) ecotypes in northern U.S. locations. Leaf area development, biomass accumulation, and N utilization of upland ecotypes were parameterized by field evaluations from Montreal, QC, Canada, and sites throughout the northern U.S. Great Plains. Resulting ALMANAC simulations were validated against measured yields from 66 location–years of switchgrass production across 13 sites in Minnesota, North Dakota, and South Dakota. As contrasted to the model defaults, the modified parameterization reduced RMSE of annual simulated yields from 3.77 to 2.62 Mg ha−1 and improved percentage bias from −16 to 13%. Model performance was most improved in environments with no N fertilization, where ALMANAC simulated annual yields with an RMSE of 1.73 Mg ha−1 and percentage bias of −0.5%. Relative to the default ALMANAC parameterization, the modified parameterization also simulated a longer growing season and extended the median simulated maturity date from 1 to 28 August, greatly improving the estimation of switchgrass phenology within the study region. Sensitivity analyses revealed that simulated switchgrass yield was unaffected by modifications of runoff curve number and was most affected by modifications of radiation use efficiency. The other seven parameter modifications each had a median yield impact of 0.57–1.4 Mg ha−1. This work provides an improved characterization of upland switchgrass ecotypes in northern U.S. locations for future ALMANAC users.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".