Development Pathways of Upland Farmers in Selected Sites of Conservation Farming Villages (CFV) Program in the Philippines
Bibliographic record
Abstract
This study analyzed the livelihoods of upland farmers in the pilot sites of Conservation Farming Villages in Ligao City, Albay and La Libertad, Negros Oriental, Philippines from 2000-2015. It also identified the development pathways based on the livelihood change in the 15-year period, and analyzed the determinants of farmers' choice of development pathways. Development pathway is a pattern of change in the livelihood strategies in response to stimuli. The focus group discussions and farm household survey involving 200 farmer-respondents revealed that from intensified food crops production in 2000-2005, the upland farmers have shifted to crop diversification and conservation farming practices combined with non-farm employment in 2006-2015. Thus, five development pathways were identified, namely: reduction of monocropping; expansion of conservation in monocropping; expansion of conservation in multiple cropping; intensification of agroforestry; and intensification of agroforestry with non-farm employment. Multinomial logistics regression revealed that age, income, and policies determine the farmers’ choice of development pathways. The pathway ‘intensification of agroforestry and non-farm employment’ has the highest likelihood of being chosen with a mean predicted probability of 0.40. There is a need to sustain the promotion of agroforestry and conservation farming practices in the upland communities, highlighting the economic and ecological services of agroforestry systems and conservation farming practices, and with active engagement of local governments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".