Spatial Impact of Extension Workers’ Performance on Sustainable Agricultural Development in Kaduna State of Nigeria
Bibliographic record
Abstract
The study examined the performance of extension workers with respect to agricultural development in Kaduna State among the sampled agricultural extension workers. The study aimed at assessing the attitudes of the workers to job performance and identifying factors that enhance performance among workers in the study area. Primary data were collected from a sample of 60 agricultural extension workers. The study area has six extension stations at different locations. In each station, ten samples were taken adopting purposive sampling technique. The sample is believed to be adequate to reflect the opinion of entire extension workers. The non-parametric Chi Square technique and descriptive statistics were adopted to analyse the data. The study showed that majority of the extension workers 66.7% were married and males fell within the age-group of 21 and 40 years. The educational level was relatively average as 36.7% had either Ordinary National Diploma or Nigeria Certificate of Education (NCE). Indeed, only 20% claimed that the income was adequate. However, the job performance score was high as 75%. There were divergent opinions about some impediments that affected the job performance such as poor condition of service, irregular wages and allowances, and inadequacy of important materials and equipment to execute the work. The attitude of majority of the workers towards their job was found to be encouraging. The statistical test showed that there was a significant difference between level of education, attitude and the job performance of the extension workers at 0.05 alpha level. On the basis of the findings, one recommends that ministries and agencies concerned should address the constraints that affect agricultural extension workers’ performance once and for all and it will go a long way to boost job performance. Moreover, improvement in attitude of workers can as well result in a remarkable increment in agricultural productivity.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".