Tree Growing Objectives of Smallholder Farmers in Claveria, Northern Mindanao, Philippines
Bibliographic record
Abstract
This study aimed to capture the underlying motives of smallholders for planting trees in their farms. Specifi cally, it aimed to identify the farm and household characteristics that might explain the underlying tree growing objectives of smallholder farmers, and draw policy implications for enhancing tree growing among farmers. The study was conducted in Claveria, northern Mindanao, Philippines. It made use of 192 farmer-respondents who practiced tree based farming systems with corn being the dominant crop. Empirical evidence from the principal component analysis indicated that smallholders’ farm forestry is an investment with multi-objectives, primarily focusing on monetary and other economic objectives. The fi rst component represents “employment, economic security and asset motive” of tree growing was related to labor income and selfemployment, increase income from timber sales, asset motives, security in old age, speculative motive, etc. The second component interpreted to represent “recreation and aesthetic objective” was characterized by non-timber use of tree growing such as outdoor recreation, solitude and meditation, aesthetic value, and improved healthy residential environment. The variables relating to the objectives of restoring farm fertility and productivity, erosion control measure, nature protection, and making the environment cooler represented the third component, as “environmental protection and restoration objective.” Tree planting for monetary and economic objectives was signifi cantly and positively infl uenced by farm distance from barangay center and inversely related to gender and education of household heads, household size, total annual cash income, and average farm distance from the nearest road. Similar variables were associated with recreation and aesthetic objectives of tree planting except on civil status. Environmental and restoration motive was negatively associated with farming experience of households. However, it was uniquely related to the age of household heads and knowledge level regarding tree-based systems. This fi nding has signifi cant implications on agricultural technology adoption in general, and tree farming in particular. Key words: principal component analysis, smallholder farmers, tree growing, grasslands, Philippines
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".