Postharvest advisory competency and training needs of N-Power Agro Advisors in Benin metropolis, Edo State, Nigeria
Bibliographic record
Abstract
This study assessed the postharvest advisory competency and training needs of N-Power Agro (one of the Federal Government of Nigeria’s Social Investment Programmes for youth in Agriculture, launched in 2016, to alleviate its citizens from poverty through capacity building) advisors in Benin Metropolis, Edo State, Nigeria. Multi-stage sampling procedure was adopted to select 120 N-Power Agro advisors. A structured questionnaire was used to collect data for the study. Collected data were analyzed using descriptive statistics such as frequency, percentages and mean while the Spearman rank correlation analysis was applied for relevant inferences. Results showed that the most performed postharvest advisory activities were those relating to the processing of crop products (x̄= 2.79), current market prices (x̄ =3.27) and formation of cooperatives for loans (x̄=3.17). The respondents were not significantly competent in providing advice on any of the storage activities but were most competent in providing advice on the processing of crop products (x̄= 2.31) and writing business plans (x̄ =2.90). The major areas of training needed by the advisors were on processing livestock products (x̄ = 3.39), pricing (x̄ =2.65) and formation of cooperatives for loan access (x̄ =2.60). The most significant constraints encountered by them were; poor linkage to research institution (x̄ =3.36), low institutional support (x̄ =3.31) and inadequate training (x̄ =3.31). There was a significant but inverse relationship between advisors’ competency level and their training needs (r = -0.428*; p = 0.000). The study therefore concluded that post postharvest losses may continue to rise if greater effort is placed on increased food production without relevant training of produce handlers to ensure that harvested produce is properly preserved. It was therefore recommended that there is a need for the provision of continuous training to N-Power Agro advisors on postharvest handling of produce in the areas of low competence to ameliorate wastage and improve food security.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".