Assessing the Environmental Impacts of Banana Farming in Yogyakarta Special Region Using Life Cycle Assessment (LCA)
Bibliographic record
Abstract
Indonesia is one of the largest banana producers in Asia, with an annual production of 9.34 million tons, including 809,976 quintals from Yogyakarta in 2022, making banana cultivation an important sector.This study evaluates the environmental impact of banana cultivation in the Special Region of Yogyakarta using the Life Cycle Assessment (LCA) approach with the IMPACT 2002+ method.The research sample involved 80 farmers and 4 distributors from two main regions.The analysis focused on four main categories: human health, ecosystem quality, climate change, and resource consumption, which are the core elements of the IMPACT 2002+ method.Farmers' environmental awareness was assessed based on seven indicators, including land and water management, as well as the use of fertilizers and pesticides.The results showed that NPK fertilizer is the main contributor to climate change, accounting for 92.3% of greenhouse gas emissions (4.48E4 kg CO2 eq).A shift to organic fertilizers is estimated to reduce emissions by up to 30%.Meanwhile, distribution activities accounted for 87.3% of resource consumption (6.07E5 MJ primary) due to the use of fossil fuels.These findings highlight the importance of transitioning to sustainable practices, such as the use of organic fertilizers and optimizing local distribution networks.This study provides a basis for agricultural policies that support ecosystem balance and climate change mitigation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".