Utilization of Oilseed Crops for On-farm Energy Security
Bibliographic record
Abstract
There has been considerable interest focused on the investment for the production of oilseed crops for biodiesel. The advance research is needed to provide knowledge needed for the feasible way of adaptation of oilseed crops for biodiesel. Among the various oilseed crops produced, sunflower is a prominent summer crop that can be used to produce on-farm biodiesel for energy security. The objective of this paper is to estimate the overall economic benefits of biodiesel production from sunflower under small scale settings. The economic viability on production of biodiesel and its market opportunity to the farmers were investigated at its 3 different production stages namely; seed, oil and seed meal, and biodiesel production. The study was based on three different on-farm biodiesel extraction scenarios. For example, the seed press and biodiesel extractor can be operated at three different rates (low, medium and high). Feedstock production, capital investment for biodiesel units and operations costs were considered for the analysis. To calculate cost and revenue in annual equivalent terms, the present values of all costs and revenues over the useful life was transformed into an equivalent annuity. Analysis shows that biodiesel production from sunflower production could generate positive net returns under various scenarios. The analysis per volume basis showed that the production cost of biodiesel from sunflower oil to producer was between $2.00 to $3.21/gal when revenue from seed meal was accounted. The estimated break-even price of sunflower seed range from $0.18 to 0.26/kg depending upon sunflower yield. The findings of this research further improve the understanding of growing sunflower as an oilseed crops and on-farm processing of oil for local biodiesel production that could play vital role in improving farm income as well as sustainable and independent energy for small farmers.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 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".