Validating the Classification of Smallholder Dairy Farming Systems Based on Herd Genetic Structure and Access to Breeding Services
Bibliographic record
Abstract
Smallholder dairy farming in Africa is classified into rural, peri-urban and urban systems. The major classification criterion is demographic. Dairy systems are extensively characterized, but not based on rigorous statistical analyses. We validated this classification based on herd genetic structure and identify determinants of within-system variations, taking Ethiopia as a case study. Discriminant function analysis correctly classified 38% - 50.6% of the 360 sampled farms into the three systems. Multinomial logistic regression analysis showed that rural and peri-urban farmers were 1.26 (P < 0.1) to 1.45 (P < 0.001) times more likely to keep local and low grade crossbreds and fewer high grade crosses (P < 0.05; odds ratio = 2.35) than the urban farmers. In the rural system, proportion of high grade crosses declined and low grades increased over generations, whereas in urban system the reverse was observed. Access to breeding services and land resources significantly determined the adoption of crossbred dairy herd within systems. In conclusion, considering farms within systems as a uniform unit to target development interventions may not be appropriate and thus farm topologies and system specific determinants of farmers’ breeding strategies need to be considered to design and introduce appropriate breeding interventions.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".