Improved Exchange Rate Farmers through Rice Falied Crop Intensification in Tolitoli, Indonesia
Bibliographic record
Abstract
Farmer Trade Rate (NTP) is a price comparison received by farmers at the price paid by farmers, which is one indicator measure the welfare of farmers. The analysis of NTP research, has been conducted (Budi, 2015; BPS, 2013; Simatupang, 2007; Mokuwa, 2013; Jhung Ahn, 2016) note that low NTP is affected by production, household consumption, Selling of rice and the use of superior seeds. Despite efforts to improve NTP has not been done, so too In Tolitoli. The result is difficult to know the level of farmers' welfare in terms of the size of NTP obtained by farmers. So to increase the NTP used agricultural intensification by using organic fertilizer, which can increase NTP. The purpose to know the factors that affect (NTP), the magnitude of the increase in NTP improve the welfare of farmers, and comparisons of NTP users of organic and inorganic fertilizers. This study uses primary data obtained from farmers through direct interviews using a prepared list of questions. Farmer of respondents was taken by using slovin method so that determined big sample of rice farmer farmer as many as 117 people apply intensification by using organic fertilizer. The data were analyzed using multiple linear regression analysis. The result of the research showed that the influence of NTP, the Food Consumption Exchange Rate (NTKP) and the Production Factor Exchange Rate (NTFP) contributed 86.7% and significantly to the increase of NTP. Increased NTP of Organic Fertilizer has implication to farmer's prosperity. Organic fertilizer users obtain higher NTP than inorganic fertilizer users. Can be concluded intensification system by using organic fertilizer can increase NTP.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".