Reducing Risks Of Income Of The Small Dry Land Maize Farmers In East Nusa Tenggara Province, Indonesia
Bibliographic record
Abstract
Maize is an important staple food for small dry land farmers in the East Nusa Tenggara (NTT) Province, Indonesia. This research was conducted to investigate factors influencing maize farmer’s income, level and source of income risk; and recommended strategies to reduce the risk involving 170 small farmers. Data were analyzed quantitatively using income and risk analysis, variation covariance, and multiple regressions, applying the revenue function and error component model. The results showed that the income of maize farmers was low, and all income risk factors are categorized as low-risk. Furthermore, all the income factors significantly affected the farmers’ income, but production and selling prices were the most important income factors. Other income factors, such as the costs of seeds, fertilizers, pesticides, labor and land area caused no effect on the farmers’ income. The main source of income risk in dry land maize farming was high labor costs, which caused low productivity and profitability. Therefore, improving land productivity through the use of appropriate and intensive modern technology and improving the skills of workers are the main strategies to reduce the income risk and to increase the income of small farmers in dry land areas.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".