Pest control with drip‐applied dimethyl disulfide and chloropicrin in plastic‐mulched tomato ( <scp> <i>Solanum lycopersicum</i> </scp> L.)
Bibliographic record
Abstract
Abstract BACKGROUND Dimethyl disulfide (DMDS) is used as a preplant soil fumigant for weed and soilborne pathogen control in plasticulture vegetable crops. The objective of this research was to determine the control efficacy of emulsifiable concentrate (EC) formulation of DMDS or DMDS + chloropicrin (Pic) on weed and Fusarium wilt in tomato ( Solanum lycopersicum L.) plasticulture. RESULTS The effective DMDS rates required to provide 50% (ER 50 ) control of purple nutsedge ( Cyperus rotundus L.) were 210 and 340 kg ha −1 at 4 weeks after fumigation (WAF) in fall 2017 and fall 2018, respectively, while these values increased to 348 and >467 kg ha −1 , respectively, at 12 WAF. The ER 50 values of DMDS + Pic were 150 and 240 kg ha −1 at 4 WAF in fall 2017 and fall 2018, respectively, while these values increased to 255 and 450 kg ha −1 , respectively, at 12 WAF. DMDS + Pic was generally more effective than DMDS for C. rotundus control. The high rates of DMDS or DMDS + Pic provided adequate C. rotundus control in early season but failed to provide effective control by season end. In addition, DMDS + Pic injections through drip tape effectively reduced Fusarium oxysporum f. sp. lycopersici (FOL) inoculum while DMDS alone was generally ineffective. CONCLUSION Injection of the EC formulation of DMDS or DMDS + Pic through drip tape should have been provided a viable option for C. rotundus and Fusarium wilt control in plastic‐mulched tomato. However, supplemental weed management actions, such as herbicide applications, may be required to achieve season‐long control. © 2019 Society of Chemical Industry
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".