Evaluation of Erosion Rates as Indicators of Ecosystem Services in Bali's Subak Rice Fields: Insights from Tabanan Regency, Indonesia
Bibliographic record
Abstract
Soil erosion causes an irreversible loss of soil fertility.The land use which is most affected by erosion is rice fields.This research aims to determine the rate of erosion in Subak in Tabanan Regency and analyse the environmental services provided by Subak.Environmental services are defined as services provided by ecosystem functions whose value and benefits are felt by humans.Subak is a traditional farming organization which is owned by the farming community in Bali Province, Indonesia, and specifically regulates the management and irrigation systems for every rice field in Bali.The research case studies were Subak Kedampal, Subak Sigaran and Subak Bongan.The method used in this research was to take soil samples from each Subak rice field, then calculate the erosion hazard using the Universal Soil Loss Equation (USLE).The Geographic Information System (GIS) was used to intersect the erosion parameters; namely, the rain erosivity factor, soil erodibility factor, slope factor, ground cover vegetation, plant management and conservation action factors, in order to create a soil erosion map.The results show that these three Subak rice fields had very low erosion rates.Subak Bongan has an erosion rate of 0.967 tonnes/ha/yr, Subak Sigaran one of 3,415 tonnes/ha/yr and Subak Kedampal one of 7,714 tonnes/ha/yr.These conditions were benefitted the Subak rice fields as a food supplier, a groundwater recharge area, a provider of environmental education, a recreation and agrotourism area, and as a way to control air pollution and preserve the ecosystem.Compared to rice fields outside Bali which are not managed by Subak, the erosion rate that occurs is higher because irrigation management and specific sloping land management carried out by Subak are able to reduce the level of erosion.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".