Camel Herd Structure and Performance in Isiolo and Marsabit Counties, Kenya
Bibliographic record
Abstract
Urbanization and improved middle-level class income have caused an increase in demand for animal products and allowed economic growth in developing countries. Arid and semi-arid lands (ASALs), which support high livestock populations have an opportunity to contribute to the economy. However, their potential may not be realized fully due to the effects of climate change. This study aimed to establish camel herd structure and performance in Isiolo and Marsabit Counties, Kenya. Focus Group Discussions (FGDs) and a cross-sectional study were conducted from July to August 2022 in each County. Data on the herd size and composition (the number of age and sex categories) was collected from 388 households through structured questionnaires. The pregnancy test was done on seventeen camels four months after the mating season by tail cocking and progesterone assay method. Analysis was done using descriptive, correlations, and linear regression statistics at a 0.05 significance level. There were more female camels over 4 years in Isiolo (above 12%) than in Marsabit (below 10%). In Isiolo, milk yield was above 20 and 10 liters per day in wet and dry seasons respectively, while Marsabit production was below 10 liters in both seasons. Milk production coefficients were significant for the females above four years (p < 0.05). There were more households in Marsabit (63%) having a calving interval of 24 months and below than in Isiolo (50%). The tail cocking method did not correlate with the progesterone assay method in pregnancy diagnosis. The study concludes that, in Isiolo County, pastoralists are doing camel rearing with the objective of milk production unlike in Marsabit County. The recommendation is that pastoralists should be encouraged to adjust age and sex categories to increase milk production. Further, they need to apply an accurate cost-effective pregnancy testing method.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".