Management of Phytonematodes Infecting Vegetables in Ekiti and Ondo States Using Hydromorphic Fields
Bibliographic record
Abstract
Background and Objective: Phytonematodes are a serious problem in vegetable fields, through their feeding habits they impair vegetable roots making it difficult for nutrient absorption and consequently lower yields.Field experiments were conducted in 2016 and 2017 to investigate the effects of hydromorphic fields and conventional fields planted to Amaranthus cruentus, Solanum macrocarpon and Telfairia occidentalis on nematode populations in Ekiti and Ondo States Nigeria.Materials and Methods: A total of 60 soil samples were collected from five Local Government Areas (LGAs) visited in the states.Soil samples were collected from two hydromorphic fields and two conventional fields in all the LGAs visited.Nematodes were extracted, counted and analyzed using Analysis of Variance.The nematodes were identified under a compound microscope based on their morphological features.Results: The hydromorphic fields consistently and significantly reduced the populations of Meloidogyne, Rotylenchulus and Hoplolaimus, their effects on Pratylenchus and Helicotylenchus was in consistent, while the populations of these nematodes were significantly high in conventional fields planted to the vegetables.Conclusion: The study suggested that hydromorphic fields may be an effective strategy in nematode management.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".