Income Risk Perception and Management by Rural Farm Households, Taking Part in Sugarcane Contract Farming in Lao PDR
Bibliographic record
Abstract
This research aimed to examine income risk perception and management of sugarcane contract farming at farm household level in Lao PDR. The study was conducted using a qualitative approach, employing purposive sampling with a total target group of thirty respondents that included older people from the village and heads of the sugarcane grower group as the key informants. The target group also included twenty five farm households who were planting and harvesting at least one hectare of contract farming sugarcane during a single season in 2014. In-depth interviews, group interviews and observation techniques were employed. Data collection was done during October-December, 2014. Content analysis was employed for the data analysis while descriptive analytic methods were used to present the results. The results indicated the sources of income risk to rural farm households engaging in sugarcane contract farming. These risks included the high cost of clearing land; in particular land containing large trees, the high cost of sugarcane stalk, which was often supplied contaminated with soil and tree branches, the high cost of fertilizer and also labor costs caused by annual inflation. Households were aware of the income risks from sugarcane contract farming and used their own strategies to manage them. These strategies would include reducing the amount of inputs, using their own labor, renting out their land to others and diversification in to other crops and livestock.
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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 | 0.000 | 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) | 0.001 | 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".