The Effect of Institutional Factors in Marketing of Agricultural Products by Cooperative Farmers
Bibliographic record
Abstract
This study was carried out to examine the effect of institutional factors in marketing of agricultural products by cooperative farmers in Anambra State of Nigeria. Four specific objective The study focused on four specific objectives which were to; examine the socio-economic characteristicsof the cooperative farmers and its effect on market participation; determine the quantum and value of agricultural produce that had been marketed; identify the extent to which agricultural market participation of the framer is influenced by institutional factors such as market information, organizational support, use of grades and standards, and legal environment; and make recommendations based on the findings. Three hypotheses were also tested. Descriptive survey design was used for the study where seven hundred and ten (710) was used as sample size. Findings revealed that market disposition of the member was not related to duration of membership which implied that cooperative experience do not have substantial influence on marketing decisions. Farmers affirmed institutional factors such as influence of tradition and cultural practices; legal environment relating to laws governing sale of agricultural products, land tenure system, organizational supports from the government, availability of market information; and use of grades and standards in agricultural marketing significantly influenced their marketing decisions. It was further revealed that institutional factors have no influence on market participation of the cooperative farmers. In conclusion institutional factors have significant influence on marketing decisions while socio-economic characteristics of members have no significant influence on market participation by the cooperative farmers. Based on the findings, it was further recommended that government should always create an enabling environment to encourage farmers to continue to participate in agricultural markets. They can do this by re-examining laws and regulation that appear to impact negatively on farm production and agricultural marketing. This may include abrogation of the land tenure Act that has over the years, hindered access to agricultural farmlands by individual farmers among others.
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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 | 0.002 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".