Patterns and simulation of soil water under different grazing management systems in central Alberta
Bibliographic record
Abstract
A study was conducted at the Lacombe Research Centre to quantify and simulate the impacts of forage and grazing systems on soil water content. Four forages used in the study were alfalfa (Medicago sativa L.), a mixture of meadow bromegrass (Bromus riparius L.) and alfalfa, an annual pasture and an old grass pasture that was composed of mainly quackgrass (Elytrigia repens L.), smooth bromegrass (Bromus inermis L.) and Kentucky bluegrass (Poa pratensis L .). Within each 1.2-ha paddock were two grazing treatments: rotational grazed and ungrazed. Soil water measurements to a 65-cm depth were conducted between May and October of 1999 and 2000 using a neutron moisture probe. Total soil water was affected by forage species more than grazing. Actual evapotranspiration rates were 3-4 mm d-1 in both years. Simulation of daily volumetric soil water content (%) for each year was conducted using the Versatile Soil Moisture Budget (VB2000) model on grazed alfalfa, ungrazed alfalfa, grazed annual and ungrazed annual treatments. During calibration year of 1999, the overall modeling efficiency (EF) was 0.58 while, during the evaluation year it was 0.43. Further, simulations for alfalfa were better than those for annual treatments. These EF values are relatively low indicating substantial discrepancies between observed and simulated results, which could have been attributed to a combination of input data errors, model errors and propagation errors in output. Key words: Evapotranspiration, forages, model calibration, model evaluation, versatile soil moisture budget model
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".