Training on Heat Detection to Increase the Success Rate of Cattle Artificial Insemination for Girinka Beneficiaries in Huye District, Rwanda
Bibliographic record
Abstract
Cattle farming is important for the socio-economic development of Rwanda, representing 12% of the national gross domestic product (GDP). In general, livestock production is a primary source of income. Recognizing the importance of cattle, the Government of Rwanda introduced the Girinka program in 2006 to reduce poverty and childhood malnutrition by providing poor people with dairy cows. The sustainability of the Girinka program relies on the successful breeding of cattle. Artificial Insemination (AI) is a method that can enhance cattle reproduction, but many Girinka beneficiaries did not have the requisite knowledge to maximize the success rate of AI by tracking the estrus cycle (heat detection) of their cattle.This project aimed to study the effect of training Girinka farmers on heat detection using the International Livestock Research Institute (ILRI) materials on cattle AI. A two-day training was provided to 74 Girinka cattle farmers. The cattle AI success rate and the farmers’ knowledge of heat detection were measured.The overall knowledge of farmers on cattle estrus cycle significantly increased from 37.16% pre-intervention to 92.34% post-intervention (P=0.008). The AI success rate significantly increased from 44% pre-intervention to 58.7% post-intervention (P<0.001).The study showed that by providing an evidence-based training to farmers on heat detection and estrus cycle in cattle could increase the success rate of AI. The same training is recommended for all Girinka beneficiaries in Rwanda. Longer term follow-up and scaling-up of the project should be considered to maximize the benefits.
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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 | 0.000 | 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) | 0.000 | 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".