Economics of Weed Management Methods as Influenced by Row-Spacing in Cowpea
Bibliographic record
Abstract
Weed management is an important factor that influences the economic importance of cowpea as a cheap source of food and income for many farmers. A study was conducted to evaluate economic benefits of weed management methods used singly or in an integrated approach, and to understand the influence of row-spacing on economic benefits of weed management methods. Total variable cost of cowpea cultivation was substantially influenced by cost of weed control. A single input of hand weeding resulted in higher cost of weed control than a single input of PRE-herbicide for weed control. Increase in weed control inputs or frequency did not guarantee an increase in economic benefits. For example, removing weeds three times with hand at 3, 6 and 9 weeks after planting (WAP) during the cowpea growing season did not necessarily result in the highest yield, but rather increased the variable cost of weed control. Integrating PRE-herbicide and hand weeding for weed management resulted in the highest yield and gross profit. The lowest cost of weed control provided by using PRE-herbicide gave the highest benefit-cost ratio. The differences in economic values of weed management methods were mostly not affected by cowpea row-spacing, but generally, economic benefits of management methods decreased with increase in row-spacing. Practically, this study suggests that minimizing the use of hand weeding by complementing with PRE-herbicide for weed management could help to optimize yield, and increase profitability, particularly under a narrow row-spacing in cowpea cultivation.
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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 | 0.002 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".