Surface Covers Affect Liquid Manure Temperature, Albedo, and Evaporation
Bibliographic record
Abstract
Highlights Evaporation from clear water, manure, and separated liquid manure was 4.6 mm d-1 on average. Straw, foam, geotextile, and roof covers decreased evaporation by 54%, 53%, 31%, and 21%, respectively. Albedo was highest for floating foam covers and lowest for metal roof covers. Straw, foam, and geotextile increased manure temperature compared to uncovered manure. ABSTRACT. Evaporation is a key component of the surface energy budget of liquid manure. Models rely on accurate energy budgets to predict manure temperature, which in turn is used to model temperature-dependent greenhouse gas emissions from liquid manure storages. Due to lack of data, it has been assumed that liquid manure has similar evaporative properties to water; however, this assumption may be inaccurate. Many factors, including manure crusting, covers, and turbidity, are all likely to affect the surface energy budget and the evaporation rate. This experiment investigated the differences in evaporation between eight treatments, including water, dyed water, raw and separated liquid manure, and four commonly used covers (straw, geotextile, foam, and roof), by measuring weekly evaporation. Albedo, surface temperatures, and internal temperatures were also measured to determine treatment effects. Over the 10-week study, no significant difference was found between the evaporation rates of water, raw manure, and separated liquid manure, with an average rate of 4.6 mm d-1. Notably, the raw manure did not form a consistent surface crust, which may explain the similarities in evaporation rates in this study and is unlikely to represent manure with a crust. Overall, covers significantly decreased evaporative losses by between 21% and 54% compared to uncovered raw manure. Average evaporation rates of the covered treatments were 1.9 mm d-1 for straw cover, 2.0 mm d-1 for foam cover, 2.9 mm d-1 for geotextile cover, and 3.4 mm d-1 under a roof cover. Similarities between each treatment and water as well as between the four covered treatments and the uncovered raw manure were found using linear regression on weekly evaporation. Generally, the uncovered treatments were more similar and could be predicted (high R2) by multiple linear regression with environmental variables, while the covered treatments differed more and were not as well predicted (lower R2). Results from this study can help adjust evaporation rates in biophysical models to improve estimates of manure temperature, tank holding capacity, and emission predictions. Keywords: Evaporation, Dairy manure, Liquid manure, Manure covers, Manure management.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".