Determination of Sustainable Food Land Directions in Bantul Regency, Indonesia Based on Food Security Level and Land Use Conversion
Bibliographic record
Abstract
Bantul Regency was originally a food-rich area with a rice surplus.However, in the last decade, land conversion has been converted to non-agricultural land, significantly reducing rice production and disrupting food security.Based on this background, this study was conducted to analyze processes, patterns, and spatial trends of land use conversion and their effects on food security conditions, both of which are the basis for recommending sustainable land use directions in Bantul Regency.The study was conducted using a mixed approach with the research population of all sub-districts in Bantul Regency, and the survey method was used to validate land conversion data.The variables in this study consist of (1) land conversion, including processes, patterns, and spatial trends, and (2) food security.Data collection techniques used are observation, interviews, and field checks.Land conversion analysis uses the Average Nearest Neighbor (ANN=z-score), with data on the distribution of conversions from agricultural to non-agricultural land use made from LANDSAT Satellite Imagery data.The research results on the nearest neighbor distribution index from the ANN analysis show that the pattern of land use conversion at the district level is clustered.In contrast, they are clustered, random, and dispersed at the sub-district level.While at the sub-district level, the food security level is a high surplus in six sub-districts, a low surplus in eight sub-districts, and minus in three sub-districts.Directions for sustainable land use are as follows: areas that can be converted for development are three sub-districts; as a granary area are two sub-districts; and as a buffer zone are six sub-districts.Land conversion not only affects the existence of productive land, but also affects various other factors such as economic, social, and environmental factors which in turn have an impact on decreasing food production and income per capita of farming families.Buffer areas should be converted with such strict provisions that sustainable food security can be realized.
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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".