Assessing Herbicide Use and Hand Weeding Efficacy in Groundnut Production Intensification
Bibliographic record
Abstract
Poor and costly weed management constrains Groundnut (Arachis hypogea L.) production in Uganda. A field study was therefore conducted at the National Semi-Arid Resources Research Institute (NaSARRI), Serere, Uganda during the long rains of 2020 and 2021 and short rains of 2020 to evaluate the efficacy of hand weeding and different herbicides on weed management, yield, and the economics of their use in groundnut. The experiment for this study comprised 7 treatments constituted by six herbicides; four pre-emergent (Glyphosate, Clethodim, S-Metolachlar, and 2,4-Dichlorophenoxyacetic acid), and two post-emergent (Bentazone and Quizalofop-p-ethyl) and hand weeding. Post-emergence herbicide application and hand weeding were done at 30, 45, and 60 DAS. The treatments were laid out in a randomized complete block design (RCBD) with three replications. Calculated weed indices show the effect of weed control measures on groundnut weeds. Pre-emergence application of glyphosate followed by post-emergence application of Quizalofop-p-ethyl produced superior pod yield (1724.3 kg/ha), the lowest weed density of grass (0.62), and Sedges (0.61), the lowest weed biomass at harvest (122.5g), the highest percentage of weed control efficiency (69.65%), and highest net returns (7,937,746UGX/ha). However, post-emergence sole application of quizalofop-p-ethyl produced the highest B: C ratio (36.49). Therefore, this study has indicated that the pre-emergence application of glyphosate followed by the post-emergence application of quizalofop-p-ethyl is the most profitable weed control measure in groundnut; while the post-emergence sole application of quizalofop-p-ethyl is the most economical. Hand weeding though may be used where labour is cheap and not scarce as opposed to the herbicides.
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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 | 0.001 | 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 | 0.003 |
| Open science | 0.000 | 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".