Efficacy and cost of four plant‐derived, natural herbicides for certified organic agriculture
Bibliographic record
Abstract
Abstract BACKGROUND Weed management is the greatest production challenge for most certified organic farmers, with few herbicides allowed on organic farms. Here, we compared the efficacy and cost of handhoeing (MECH) with a control (no weed management) and four commercially available registered organic herbicides under United States Department of Agriculture organic standards in managing Canada thistle ( Cirsium arvense (L.) Scop.) and total vegetative cover in two apple ( Malus domestica Borkh.) orchards. The four herbicide treatments had the following active ingredients: capric and caprylic acid (CAP), d ‐limonene (LIM), acetic and citric acid (ACET), and clove and cinnamon oil (CIN). In separate greenhouse trials, Canada thistle response to CAP (at two concentrations), LIM, glyphosate (GLY), and MECH were also studied. RESULTS All materials reduced weed cover by 48% or more 72 h after treatment in the orchard trials; CAP performed best, reducing weed cover by 88% in 1 h and 98% in 72 h. CAP and LIM reduced early season, perennial weed cover after 3 years of repeated applications in an organic orchard; a single application of CAP and LIM would cost on average US$769.50 ha −1 less and US$203.50 ha −1 less than MECH at US$12.00 h −1 wage respectively, with similar efficacies. In greenhouse trials, CAP mixed to 7.11% v/v and 4.74% v/v active ingredient were equally effective at removing weed cover compared with MECH, further reducing the cost of a single application of CAP by US$122. CONCLUSION Adding effective, next‐generation, natural herbicides, such as CAP and LIM, to existing weed management strategies may allow organic producers to reduce weed pressure, till less, and improve profitability, while providing conventional producers options to combat herbicide‐resistant weeds. © 2025 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
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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 | 0.000 |
| 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".