The Role of Soil Amendments on Population Dynamics of Insect Pests, Growth Parameters and Yield of Eggplant, Solanum melongena (L.) Moench
Bibliographic record
Abstract
Loss of soil fertility as a result of continuous cropping on the same piece of land has necessitated the need to improve soil fertility for better crop yields. Inorganic and organic fertilizers have been used to improve soil fertility, however, excessive use of soil amendments improve vegetative growth of plants thereby attracting large numbers of insect pests. Cow dung and poultry droppings were used as soil amendments in a field experiment using eggplant Solanum melongena. The effects of these organic manures were compared with inorganic fertilizer (NPK) and a control where there was no application of soil amendment in a randomized complete block design with 3 replicates. Parameters studied were pests’ and their numbers, plant height, number of leaves per plant, leaf area, stem girth and yield. The major insect pests identified on the plant were Bemisia tabaci, Aphis gossypii, Leucinodes orbonalis and Eublemma olivacea. Bemisia tabaci and Aphis gossypii scores were significantly larger on cow dung and poultry manure plots. Leucinodes orbonalis and Eublemma olivacea numbers were not significantly different on the treated and control plots. Mean plant height, number of leaves and yield differed significantly among the soil amended plots. Even though soil amendments improve the nutrient content of the soil and the yield of crops it could lead to increase in pests numbers and damage caused to plants.
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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 | 0.000 | 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".