Impacts of Agricultural Cooperatives on Farmers’ Revenues in Cambodia: A Case Study of Tram Kak District, Takeo Province
Bibliographic record
Abstract
Agricultural cooperatives in Cambodia have been promoted with the aim of increasing agricultural production and farmers’ revenues. The objectives of this study are to identify factors influencing farmers’ decision on membership in agricultural cooperatives, and to assess the impact of being a member in those cooperatives on farmers’ revenues from paddy, livestock and farm. Cross-sectional data from interviews of 242 households in Tram Kak District, Takeo Province were used. The probit model and propensity score matching were employed to achieve the objectives. The results show that farmers who sold their paddy and had been contacted by extension workers from the government agency and non-governmental organizations (NGOs) are more likely to join the cooperatives while male-headed household farmers and farmers who have high off-farm income are less likely to become members of the cooperatives. Moreover, the results of propensity score matching reveal that agricultural cooperatives have no impact on paddy yields and paddy revenue due to the fact that agricultural cooperatives do not provide sufficient training to their members, and members did not actively attend those trainings provided. Also, the cooperatives have failed to provide members better prices for their paddy. There are positive impacts on their livestock and farm revenues through increasing livestock and other crop production when agricultural cooperatives provide livestock and other crop training to their members. However, there is no impact on non-members if they join the cooperatives as they have higher off-farm income, less paddy land size and fewer laborers that are not favorable to taking on other farming activities.
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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 | 0.001 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".