Financial Feasibility Assessment of Sweet Potato Cultivation Technology Packages Application in Tidal Swamp Land
Bibliographic record
Abstract
Sweet potato is consumed as a source of carbohydrate as a substitute for essential food (rice). Due to limited area in Java island, Indonesia, the expansion of sweet potato could be cultivated in tidal swamp land. Therefore, this research was aimed to determine the financial feasibility of sweet potato technology packages in tidal swamp field. This research was carried out in tidal swamp fields: Roham Village, Wanaraya District, Barito Koala Regency, and South Kalimantan Province, Indonesia from March to July 2019. This study compared two innovative and existing technologies. The innovative technology introduced to the farmers emphasized on intensive processing in order to reduce the occurrence of the main pests of sweet potato in tidal fields. Innovative technologyy includes tillage done with plows and rakes. The results of this research showed that application of sweet potato cultivation technology packages with improved tillage, land cover with mulch, pest control using chemical fungicides and shallot extracts has proven to be financially feasible. Existing farmers (local variety) who switch to innovative technology using Sari variety, the profit earned increased by 232.47%. Technically, the application of the tuber yield innovative technology for Sari variety was higher, both controlled using chemical insecticides and innovative technology of 18.25 and 24.15 tons/ha, respectively. The implementation of the introduced cultivation technology package was able to increase local sweet potato production to the superior Sari variety by 96.82% compared to the farmer technology package at the same location. R/C and B/C ratio > 1 for innovative technology shows that innovation technology is feasible to be developed at the researched location.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".