Evaluation of Potential for Ethanol Production from Rice Straw Using Satellite Data
Bibliographic record
Abstract
Purpose of this study is to estimate rice yield on a plot basis with use of satellite data and field investigation data and to calculate potential quantity of rice straw to be utilized as a raw material for biofuel production in Bali, Indonesia. In addition, for continuous supply of rice straw by farmers to a biofuel producer, it is absolutely necessary to investigate farmer’s interest, behavior and potential issues to solve. For this investigation, an interview was made to the subak heads. The subak is a traditional social organization consisting of farmers for managing irrigation and agriculture in the farmers’ village.The created estimation equation of rice yield had NDVI from SPOT satellite data as a predictor and was significant at 1% level. Based on the rice yield estimated through the equation and the paddy area, quantity of rice straw to be available and quantity of bioethanol to be produced were estimated. In case of Kediri, the rice straw quantity was 42,274 t/year and the ethanol quantity was 12,682 kl.On the other hand, the handling of rice straw after harvesting was leaving/putting back to soil or burning, which accounted for 76% of the rice straw. Concerning a price of rice straw, about 60% of farmers expressed their willingness to sell at 100 rupiah (1 cent US$) per kg, and about 88% including people saying high probability seemed to think this price would be as an adequate level of sales price. Through the interview work, it becomes clear that, when some issues such as price of rice straw are settled, farmers are positive to selling of rice straw for production of biofuel.For realizing bioethanol production from rice straw, there still remain some items to be studied further such as production process, transportation and storage system and costs. The result of our study suggests that a proposal for improvement of stable production can be made through rice yield estimation and monitoring using satellite data and that rice straw can be supplied as a promising resource of raw material for bioethanol production. This is considered to contribute the promotion of activity to reach the national goal of bioethanol production in future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".