Using Tannery Sludge to Manage Soybean Cyst Nematodes in Soybean Crops
Bibliographic record
Abstract
Soybean crop (Glicyne max) is host to a range of pathogens, among them phytonematoids, with emphasis on the soybean cyst nematode (Heterodera glycines), which has recently caused great damages to the crop and compromised its productivity. Alternatives to the management of diseases, caused by phytonematoids, that are effective and less harmful to the environment, are increasingly being sought. Thus, the incorporation of organic compounds into the soil, such as tannery sludge (bovine chain by-product), presents great potential in the management of soil-borne pathogens, as well as reducing environmental impacts caused by the release into the environment. Thus, the aim of present study had as objective to evaluate the effect of different tannery sludge doses on the management of H. glycines in greenhouse. The study used a nematode-susceptible cultivar (BRS Valiosa RR) and followed a completely randomized experimental design, with 5 repetitions. The nematode penetration in the roots was assessed 10 days after inoculation (DAI), whereas the fresh root mass, the number of females per root gram, and the number of eggs per female were assessed 30 DAI. The tannery sludge is efficient in the management of H. glycines in nematode-susceptible soybean cultivars, reducing the number of females per gram of root, the number of juveniles of the second stage (J2) of the nematoid per root system and promoting increases in fresh mass of the roots. However, more studies are needed to understand the dynamics of the nematode reduction in the presence of the organic compound, since nematode reproduction was not affected by the presence of tannery sludge.
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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 | 0.001 |
| 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".