Constraints to agroforestry diffusion under the Billion Trees Afforestation Project (BTAP), Pakistan: policy recommendations for 10-BTAP
Bibliographic record
Abstract
Effective agroforestry diffusion under the newly started 10-Billion Trees Afforestation Project (10-BTAP) needs a thorough understanding of the policy and implementation shortfalls of the already completed BTAP. This study examines the factors that affected the diffusion of the agroforestry in the Hindu-Kush Himalayan (HKH) region of Pakistan under BTAP. The data were gathered through in-depth interviews with Village Development Committee (VDC) members, Forest Department (FD) officials, and local farmers. Important factors positively affecting agroforestry diffusion included locations of crop fields on the river sides, community dependency on firewood, and market value of agroforestry timber. The logistic regression model shows that household head's age, access to information, and area under cropland positively affected household level adoption of agroforestry under BTAP; forest cover was negatively related. In-depth interviews show that key barriers to FD in diffusion of agroforestry included provision of false information by farmers to monitoring teams, non-availability of extension staff, lack of communication among project staff and community, as well as a lack of sufficient budget for activities. Primary barriers to community adoption of agroforestry included no participation of VDCs in planning and monitoring of agroforestry programs, lack of plant need assessments on the part of the project staff, poor quality of plants distributed by FD, farmers' poor know-how of plantations, lack of trust among community and project staff, as well as the waste of plants by farming community. Thus, this study recommends that policy-makers and project designers should consider these factors when planning agroforestry diffusion under 10-BTAP to improve its success.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| 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 teacher head, 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".