Availability of Fodder Trees and Shrubs Integrated Into Agricultural Systems in Eastern Hararghe, Ethiopia
Bibliographic record
Abstract
Tree/shrub based feed resources and feeding systems in Eastern Hararghe are not studied well. Therefore, a study aimed at assessing the availability of fodder trees and shrubs integrated into farming system, available feed resources, and feeding systems was made in two districts of eastern Hararghe zone, Ethiopia. A total of 268 respondents from the two districts, both from lowland and highland agro ecology, were interviewed. Sørensen’s Similarity Index was used to assess species composition in relation to agroecology. The result revealed that about 67.2% of the respondents have not integrated any fodder trees and shrubs into their farmlands whereas only 32.8% of the respondents integrated fodder trees and shrubs into their farmlands. A total of 20 fodder and non-fodder tree species were identified. Regarding tree species composition, only 46% of tree species were found in both districts, whereas about 54% of the tree species were dissimilar between the districts. The feeding system significantly varied with agroecology. Zero grazing system with stall-feeding technique is common in the highland agroecology. However, in the lowland agroecology free grazing on communal grazing area and feeding crop residue are common. Factors like inadequate extension service, lack of planting material and land scarcity has resulted in reduction of farmers’ interest to integrate fodder trees and shrubs into their farming system.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".