Influence of Socio-Economic Conditions of Farmers on the Control of Insect Pests of Citrus in Benue State, Nigeria
Bibliographic record
Abstract
Despite the significant losses of citrus fruits due to insect pests damage, not all farmers control the menace of these pests. Control of these pests is inevitable for high quality, sustained and increased production of the product and income for the farmers. It is, therefore, imperative in the study to empirically establish the socio-economic variables of citrus farmers influencing the control of citrus insect pests. To achieve this, data collected from a random sample of 50 commercial citrus farmers from the major producing areas of Benue State in 2014, through the use of questionnaire, were analyzed by employing descriptive statistics and logistic regression model. With the exception of age with a coefficient of -.035, which influenced the control of insect pests negatively, the influence of other variables such as education (.362), experience (.159), gender (.992), income from citrus (.002) and income from other enterprises (.001) were positive, although only education and income earned from citrus were significant at 10% and 1% level of probability, respectively. Control of insect pests of citrus can be better achieved by potential and existing farmers if their education and earning from citrus production are continually and simultaneously increased.
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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 | 0.001 |
| 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) | 0.002 | 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".