Overcoming Degradation and Increasing the Value of Peatland Benefits Through the Cultivation of Pineapple in Riau Province, Indonesia
Bibliographic record
Abstract
Peatland restoration can be done by re-greening, but it takes a long time. Therefore, planting more productive and short-lived crops on burnt peatlands could be a good alternative solution. Restoration of damaged peatlands can be done by cultivating pineapples by applying good agricultural practices. The results of the research we conducted in Riau Province using a gap analysis showed that most farmers in Riau Province had implemented good pineapple cultivation methods from the aspects of seed selection, land preparation, planting and harvesting. However, the application of good agricultural practices is still weak from the aspect of plant maintenance, including fertilizing, weeding, thinning and watering. The lack of knowledge of good pineapple cultivation techniques and limited capital on the one hand, the high price of fertilizer on the other hand means that the maintenance of pineapple plants cannot be carried out optimally. Based on the results of the income analysis, it can be said that the income derived from pineapple farming is greater than that of oil palm farming carried out by independent smallholders on peatlands. Through training, capital assistance, and continuous assistance, it is believed that it will provide optimal results so that it can overcome, at least reduce, the problem of degradation and increase the beneficial value of peatlands. Further research is planned to obtain a more comprehensive and accurate description of the potential commodities to be developed on peatlands. In 2023 the focus will be on coconut and sago commodities, and in 2024 the focus will be on palm oil and rubber commodities.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".