An economic analysis of the costs of alternative sugarcane fallow weed control programs
Bibliographic record
Abstract
Economic research was conducted to present estimates of costs per acre associated with fallow sugarcane weed control programs for Louisiana in 2010. The 2010 projected costs are associated with the various phases of sugarcane fallow using different machinery, implements, and weed control practices followed by most growers in the main sugarcane production area of Louisiana. For bermudagrass and johnsongrass weed control treatments, the herbicides applied were Roundup Original Max at 46 oz/A, generic glyphosate at 64 oz/A, DuPont K4 60DG, Trifluralin 4EC at 4 qt/A, and EPTC at 3.5 pt/A. Purple nutsedge weed control treatments included Roundup Original Max at 46 oz/A, generic glyphosate at 64 oz/A, Permit 75DF at 1 oz/A, and Yukon 67.5WG at 6 oz/A. Roundup Original Max at 46 oz/A applied for perennial weed control was more expensive by $30.40 and $15.20 per acre compared with generic glyphosate treatments applied at 64 oz/A. Treatments applied with Roundup Original Max had a higher sugarcane fallow cost compared with treatments using generic glyphosate at current fuel, labor and herbicide input prices. A spreadsheet decision aid was developed which summarizes sugarcane fallow field operations and weed control costs, including equipment used, performance rates, and herbicides applied. These data can be entered by the user for specific farm situations, calculating total variable tillage and weed control costs per acre. Binary and non-binary linear programming were utilized to determine optimal sugarcane fallow weed control programs for bermudagrass, johnsongrass, and purple nutsedge control. The non-binary LP model selected treatments to achieve desired control of bermudagrass, johnsongrass and purple nutsedge and minimum cost program. In comparison, the binary LP model selected only one treatment that had minimum fallow field operation and weed control cost while satisfying minimum weed control levels. Generic glyphosate cost was found to be sensitive to price increases to $0.27 oz/A or above for bermudagrass control, and $0.33 oz/A for johnsongrass and purple nutsedge control. Fuel prices, directly impacting tillage costs, were found to not be sensitive in determining optimal weed control choices.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".