Bio-economic and operational feasibility of introducing oestrus synchronization and artificial insemination in simulated smallholder sheep breeding programmes
Bibliographic record
Abstract
Low selection intensity due to few selection candidates available at any one time due to thinly spread year-round lambings in villages and prohibitively large nucleus requirements to provide sufficient improved rams to the production tier are the major challenges for designing effective village-based and central nucleus-based breeding programmes, respectively, for smallholder sheep farmers. To tackle these challenges, we used deterministic simulation to design three schemes in village-based programmes introducing hormonal oestrus synchronization (natural oestrus (VNE), single oestrus synchronization (VSE1) and double oestrus synchronization (VSE2)) and three schemes in central nucleus programme introducing artificial insemination (AI) (natural mating with nucleus sizes of 5% (CNM1) and 1% (CNM2) of the total ewe population and natural mating in breeding tier and AI in production tier (CAI)). The schemes were evaluated for their bio-economic and operational feasibility, taking Bonga sheep of Ethiopia as a case study. The selection intensities achieved in VNE, VSE1 and VSE2 were 2.0, 2.3 and 2.4, respectively, for selecting rams for the breeding tier and 0.0, 0.8 and 1.0, respectively, for the production tier. The profits per ewe per year from VNE, VSE1 and VSE2 were Birr 12.2, 21.7 and 24.5, but the profit from VNE for the production tier was zero. CAI generated more genetic gains in the breeding objective (Birr 4.8) than CNM1 (Birr 2.5) and CNM2 (Birr 0.0) in the production tier. However, CAI was less profitable than CNM1 and CNM2. In conclusion, hormonal oestrus synchronization was found to be a feasible technological aide to accelerate genetic progress in village-based programmes. CNM1 and CNM2 could not be recommended as CNM1 requires large nucleus of 10 325 ewes and CNM2 results in zero genetic gain in the production tier. CAI could overcome the challenge in central nucleus programmes, namely unaffordable large nucleus, but the scheme needs to be subsidized by the public sector to be economically feasible for farmers.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".