Combined Use of Green Manure and Biological Agents to Control Meloidogyne javanica (Treub) Chitwood in Soybean
Bibliographic record
Abstract
Root-knot nematode management requires the adoption of integrated practices. Biological agents and cultural control practices are the most widely used, but little is known about their combined effects. This study aimed to assess the interaction effects of the biological agents Trichoderma harzianum + Purpureocillium lilacinum and different green manures on the control of Meloidogyne javanica in soybean under greenhouse conditions. Green manures from white oat, Urochloa ruziziensis, Crotalaria spectabilis, millet, and buckwheat were grown separately and applied onto the soil surface. Subsequently, soybean seeds were treated with the biological agent and planted. The experiment was repeated twice to confirm the results. In Trial 1 and 2, application of green manure or biological treatment alone was efficient in reducing nematode populations. In Trial 1, there was an interaction between factors on total nematode number and number of nematodes per gram of root. Combined use of biological control with white oat and millet green manure produced great results, since when associated with the biological one, the reduction in the total number of nematodes was potentiated by 55 and 49%, respectively (Trial 1). There was no interaction between green manure and biological factors for Trial 2, and the best results were observed with green manures of C. spectabilis, U. ruziziensis and white oat, with a reduction in the population density of the nematode in 60, 59 and 44%, respectively. It is concluded that green manure application and T. harzianum + P. lilacinum were effective in reducing nematode populations when applied separately. White oat and millet green manures associated with T. harzianum + P. lilacinum increase thecontrol of M. javanica in soybean.
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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) | 0.001 | 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) | 0.000 | 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 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".