An evaluation of a Browse-aid at the doorsteps of (agro)pastoralists to improve consumption of in situ browses and shrubs and prevent animal deaths during droughts
Bibliographic record
Abstract
Abstract Background Thousands of animals die in Ethiopia during droughts due to unavailability of feed. Large amounts of browses are available during droughts, which animals do not consume because of the presence of tannins. The hypothesis of the study was that the provision of small amounts of Browse-aid (polyethylene glycol-4000) to animals at farmers’ doorsteps would increase browse consumption and body weight gain, and prevent deaths during droughts. Methods A field study was conducted at two sites in Ethiopia to assess the dietary inclusion of the Browse-aid (daily 5 g and 10 g for shoats; and 15 g and 30 g for cattle) to farmers’ sheep, goats and cattle in areas devoid of grasses during a very dry spell. Results Inclusion of the Browse-aid to sheep and goats significantly increased the percent body weight gain, and improved the body condition score, skin coat appearance and shine in eyes when compared with those of animals not given the Browse-aid. Similar results were observed in cattle. The foraging frequency of the animals also increased. Nutrient availability to the animals from the increased consumption of browses also increased. Furthermore, the body condition scores of the animals given the Browse-aid were greater than those of the animals that did not participate in the trial but were grazing in the same area. Conclusion Browse-aid made a positive impact on the production of animals during periods of severe feed scarcity. This strategy of providing the Browse-aid (daily 5 g for sheep and goats, and 15 g for cattle) to animals during droughts is highly cost effective; additionally, compared with currently used feeding strategies, the cost in late 2023 was 60% and 90% lower for sheep and goats, and cattle respectively to prevent animals from dying during droughts. A large number of animals can be prevented from dying during droughts, at a much-reduced cost by the dietary inclusion of a small amount of the Browse-aid. The implications of the study are not restricted to Ethiopia but extend to countries in the Horn of Africa. This is the first study that has investigated the effect of the Browse-aid at the farmers’ doorsteps in the field.
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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 | 0.000 | 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) | 0.002 | 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".