Feasibility of Artificial Insemination Network for Egyptian Buffalo Development
Bibliographic record
Abstract
Reviews of literature confirmed that Egypt has a comparative advantage in milk production rather than red meat, particularly from buffalo. In addition, there is an increasing scarcity in water resources, which constrains horizontal expansion in fodder acreage. In addition, there is a sharp competition between food demand and feed demand on available agricultural land resources. Thereof, a horizontal expansion in dairy buffalo stock would not be feasible. Thereof, the only option for buffalo development in Egypt is the vertical expansion via increasing milk yield to fulfil the current deficit in domestic milk production. The Egyptian consumer gives high preference to buffalo milk for color taste and high content of total solids, particularly fat. Buffalo milk has higher price than cow milk and its production grow faster than cow milk production. Artificial insemination (AI) network is the approach to accelerate the proposed genetic improvement of the buffalo milk yield. A recent study [1], provided evidence that the return of genetic investment in dairy buffalo would be feasible, (IRR = 19.71%) However, the Official statistics showed that there are only two AI-centers for buffalo selected buffalo sires, serving four AI-units in Egypt. Therefrom, the objective of this study was to assess the feasibility of establishment an AI-network in Egypt, by estimating (NPV, IRR, and payback period) and its sensitivity to unfavorite changes that may face the proposed program. The study used a field survey data collected from an AI-unit of the buffaloes’ semen and an AI-Center for raising buffalo sires in Nile Delta. Results showed that, while the average discount rate of the Egyptian economy was 17.5%, under the most probable condition the estimated IRR for one AI-unit was about 35%. A 10% Decrease in Semen Price and a 10% increase in Insemination Costs would result in IRR about 28% and 31%, respectively. The estimated IRR for the AI-center, under the most probable conditions was about 31%. 10% Decrease in Semen Price, and 10% increase in feed costs or in Sire’s price would result in 26%, 30% or 28% respectively. Thereof, the less sale price of semen dose is the most effective variable on the IRR. However, unfavorable changes would keep investments with high incentives in establishing a feasible AI-Network for increasingly rapidly the dairy buffalo milk yield.
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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 | 0.002 | 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 | 0.000 | 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) | 0.000 | 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".