Land Dynamics of Cocoa Plantations Towards Healthy Landscape
Bibliographic record
Abstract
North Luwu Regency is one of the largest cocoa producers in South Sulawesi where production has tended to decline in the last 5 years.This research aims to look at the dynamics of cocoa land use towards a healthy landscape through implementing good plant spacing, designing agroforestry concepts and implementing good farming systems in 3 landscape clusters.This research uses descriptive correlation analysis to answer objectives 1 and 2 and Analytical Hierarchy Process (AHP) to examine objective 3. The research was conducted in North Luwu Regency, South Sulawesi Province from June to August 2022.From the results of data analysis carried out on 30 farmers, 3 extension workers and 3 MSME actors obtained the results: 1) Focus on landscape studies for cocoa cultivation in 3 clusters, namely clusters 2, 5 and 7, obtained data in cluster 2 with low cocoa production levels in the range of 400-500 kg/year with service contributions high environmental level, 2) Activities that have been carried out by SFITAL in providing assistance to cocoa farmers in terms of implementing plant spacing, agroforestry design and good farming systems through socialization activities, field schools, farming courses, mentoring as well as monitoring and evaluating activities and 3) The results of the AHP analysis show that there is a good application of the GAP concept, where this point is 5 times more important than the planting distance criteria and agroforestry design is 3 times more important than the planting distance criteria.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".