Tree and shrub species integration in the crop-livestock farming system
Bibliographic record
Abstract
Tree and shrub integration has been promoted as a means of enhancing rural livelihoods through sustaining watershed provision of services and products, especially in Ethiopia.However, research to support this effort has been limited.This study was conducted in Borodo watershed in central Ethiopia, to identify constraints to the process of tree and shrub integration in the watersheds.A household survey was conducted, supplemented with focus group discussions (FGDs), key informant interview and field observations.A total of 31tree and 11 shrub species were identified in different niches in the watershed.The key constraints to tree and shrub species integration included shortage of arable land, soil cracking, free grazing, lack of seedlings of desired species and water-logging.The main catalysts to the integration were availability of information on improved integration and cash for investment in the required activities, easy land certification and market opportunity for tree and shrub products.The tree and shrub growing niches preferred by farmers were homesteads (95.5%), gully sides (67.4%), stream sides (61.8%) road sides (60.7%), and crop land (12.4%).It is essential to address the factors that hinder tree and shrub species integration at various growing niche so as to improve the availability of tree products and services.Moreover, the capacity of farmers should be upgraded through training and demonstration of best tree planting, management and utilisation practices.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".