Liquid Manure Hauling Capacity of Custom Applicators Using Tank Spreader Systems
Bibliographic record
Abstract
In the last several years livestock farms in the Great Lakes Region have expanded and increased in size. With more livestock comes more manure handling and a greater transport distance to reach the expanding land base needed for land application. Because hauling and spreading large volumes of manure can greatly impact peak labor demand and the timeliness of tillage and planting, many farm managers are using custom hire of manure hauling and land application. A time-and-motion study of 13 liquid manure hauling systems on 10 farms using custom hauling services in Michigan, Ohio, and Ontario, Canada was done in 2006-2008. Representative time and material flow rates were used to model the hauling capacity of tank spreader systems as a function of spreader tank volume and transport distance. Simulated hauling rates were fit to a general model to develop machinery system-specific coefficients to predict an effective hauling capacity of tractor-drawn and truck-drawn spreader tanks, and hauling systems using truck-drawn nurse tanks for over-the-road transport to tractor-drawn spreader tanks for field spreading. The machinery-system specific coefficients are presented in a convenient reference table and can be used to estimate liquid manure hauling capacity over a range of tank volume and travel distance. Compared to standard tractor-drawn tank spreaders, spreaders drawn with high-speed tractors and truck-drawn spreaders had an advantage with longer hauls. Compared to injection with a 6-shank injector a broadcast application with a 26495-L (7000-gal) high-speed tractor-drawn spreader increased the hauling rate 25% near storage and 17% with a 3.2-km (2-mile) haul. The hauling rate of tank spreader systems was most sensitive to an increase in tank volume and travel speed. A 20% increase in tank volume increased the effective hauling rate about 8% with a 0.16-km (0.1-mile) haul and more than 16% with a 16-km (10-mile) haul. A 20% increase in travel speed had little effect when hauling near storage, but increased the hauling rate more than 7% with a 3.2-km (2-mile) haul, and more than 14% with a 16-km (10-mile) haul.
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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 | 0.000 | 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".