Life Cycle Assessment of Sago Palm, Oil Palm, and Paddy Cultivated on Peat Land
Bibliographic record
Abstract
The continuously increasing population growth more than food agriculture growth on the existing land, has been encouraging to this research. The land use competition for agriculture and housing purposes have caused the land use change from forest to agriculture and housing. Within forested landscapes food production, commodity agriculture, biodiversity, resource extraction and other land uses are also competing for space. The forest land use change (deforestation) is one of the climate change causes. The impact of climate change among others is the uncertain climate, such as the long drought period, flood, and the extreme temperature that cause decreasing in agriculture production. Therefore, at present, many people use the marginal land, such as peat land for agriculture cultivation to increase the food agriculture production and to achieve the domestic and export demand. Indonesia has a huge peat land and the fourth biggest in the world after Rusia, Canada, and America. The focus of this study is comparing the life cycle assessment of three agriculture commodities: sago palm, oil palm, and paddy cultivated on peat land. The purpose of this research is to contribute a recommendation of the most sustainable commodity from the aspect carbon dioxide (CO2) emission among three food agriculture commodities include oil palm and paddy that currently as excellent commodities, and sago palm, the neglected indigenous plant, which are cultivated on peat land. The method applied for this research to analyze the environmental aspect using life cycle assessment (LCA) started from seedling, plantation, harvesting, transportation, and production process. The analysis result reveals that sago palm is the most environmental friendly. The lowest CO2 emission (ton/ha/year) is sago palm (214.75 ± 23.49 kg CO2 eq), then paddy (322.03 ± 7.57 kg CO2 eq) and the highest CO2 emission (ton/ha/year) is oil palm (406.88 ± 97.09 kg CO2 eq).
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".