Soil moisture and temperature simulation using the versatile soil moisture budget approach
Bibliographic record
Abstract
Soil moisture and temperature are two important soil parameters that influence many vital agronomic, environmental, engineering processes within the soil. Due to the difficulties arising when measuring these parameters in the field as well as the cost of instrumentation, many models that yield accurate and timely estimation of these parameters on a large scale have been developed as reliable and efficient alternatives. The Versatile Soil Moisture Budget model can be used to stimulate the vertical, one dimensional, water balance in a soil profile. Originally the model was designed to use air temperature and precipitation data to simulate soil water content within the root zone of a cereal crop. It has since undergone modifications and the model can now output, potential evapo-transpiration, actual evapo-transpiration, and surface temperature. The temperature algorithm simulates temperature at the soil surface and has not been rigorously tested for cropping systems. In this study, a simple empirical equation that simulates soil temperature at depth of up to 90 cm was introduced into the model. The model was evaluated and the accuracy of predicted soil moisture and temperature under both perennial and annual cropping systems were tested using two years of data collected at the University of Manitoba Research Station at Carman using soil water and temperature probes. The model’s accuracy in simulating soil moisture was also tested. Observed R2 comparing modelled temperature with observed was greater than 0.90 at the soil surface but decreased to about 0.40 at soil depth greater than 30-45 cm layer. The model was shown to be better at estimating soil temperature than soil moisture. The accuracy of the model was also shown to decrease with depth. These results can be used to improve soil temperature modeling at depth as well as improve farm management planning, irrigation schedules, nutrient management, fertilizer application and drought monitoring.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".