Economic Evaluation of Weed Control and Herbicide Residues on Cassava (Manihot esculenta Crantz) in Ghana
Bibliographic record
Abstract
A study on economic evaluation of some weed management strategies and herbicide residues analysis on roots of cassava (Manihot esculenta Crantz) was conducted during 2014 and 2015 cropping season in Kumasi, Ghana. Cost and benefits were computed from the use of two manual weedings (hoeing and cutlassing), two pre-emergence herbicides (Butachlor 60% EC and Terbulor 500 EC) with two-supplemenatary hoe weeding, weed-free and weedy check. These were evaluated using two varieties of cassava, Ampong (Early branching) and Dokuduade (Late branching). The treatment was a factorial laid out in a randomized complete block design (RCBD) with four replicates. Partial farm budgeting were used for economic analysis of data and herbicide residues analysis in roots of cassava were determined using Gas Chromatography-Electron Capture Detector (GC-ECD). Results showed that Terbulor 500 EC with two supplementary hoe weeding was more economical, profitable and beneficial than those other treatments applied in the production of cassava. In addition, the average concentration of Terbulor 500 EC (0.003 mg/kg) and Butachlor 60% EC (0.001 mg/kg) residues in roots of cassava varieties were below the maximum residue limit (MRL) of 0.01 mg/kg set by Ghana Standards Authority for cassava. In conclusion, Terbulor 500 EC with two supplementary hoe weeding was more effective and financially rewarding and both herbicides had lower residual effects on cassava.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".