Socioeconomic Analysis of Rice Farmers and Effects of Group Formation on Rice Production in Ekiti and Ogun States of South-West Nigeria
Bibliographic record
Abstract
The study was conducted to determine the impact of farmers’ membership of cooperative societies on rice production. Against the backdrop that the promotion of membership of cooperative society among farmers would give them better access to agricultural inputs and consequently improve their income. Multistage sampling technique was employed to select a total of 310 rice farmers. Data collected were analyzed using descriptive statistics, budgetary technique and inferential statistics. The results revealed the mean age of the rice farmers as 48 years. Majority (92%) of the farmers produced upland rice, with a single harvest per year using mainly owned resources. Family labour was the most important source of farm labour in rice cultivation and about 60% of the members of the farm families participated in the family rice farm. The results further showed that 38.9% of rice farmers had primary education, 27.4% had secondary education, while 25.1% had no education. A total of 71% of the rice farmers were members of rice farmers’ cooperative societies, while 29% were not. The average farm size cultivated was 1.72ha and 1.64ha for cooperative and non-cooperative members respectively. The result also showed that there is no significant difference in the gross margin per hectare realized by farmers that were cooperative members (N90, 222) and the non cooperative members (N92, 986). The input-use structure showed that cooperative members were more intensive users of purchased inputs like fertilizer and pesticides valued at N124,555 per ha (about 41% of variable cost) compared to the non cooperative members valued at N57,647 per ha (about 22% of the variable cost). Almost all the groups were established to serve as receptacles for subsidized agricultural services and inputs rather than real producer organizations that seek to attract commercial providers of services and ensure efficient marketing of their farm outputs. Further revelation from the study is the fact that membership of cooperative society was found to be influenced by household size, access to extension services, number of rice farms owned, access of rice farmers to herbicide and quantity of rice output. The non-significant difference in the gross margin of cooperative and non-cooperative members despite the greater intensity of use of purchased inputs (fertilizer and pesticide) by cooperative members suggests the need for monitoring of rice farmers who are cooperators in order to ensure that the substantial inputs are rightly channeled.
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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".