Livestock Diversity and Food Security in Smallholder Farming Households in Haiti
Bibliographic record
Abstract
While animal‐sourced foods and income can play central roles in the livelihoods of smallholder farmers, there is very little knowledge on how the diversity of these farmers' livestock holdings may impact their food security (FS). Using survey data from 500 smallholder Haitian farmers, this research addresses three crucial questions: (1) determining the most appropriate index for measuring smallholder farm livestock diversity, (2) evaluating the impact of this diversity on household FS, and (3) assessing impacts of the presence and diversity of different animal functional groups on household FS. Of the five ecological diversity scores tested (along with several alternative weighting modifications), the unmodified Shannon Index was found to be the most appropriate and simplest animal diversity index for FS analyses. Our base model included household income, total livestock (tropical livestock units), and livestock diversity (Shannon Index). Interestingly, while all three variables positively impact FS, the impact of total livestock holdings is only observable at high livestock diversity levels. Further analysis comparing diversity within and between livestock functional groups indicated that both the fraction of and diversity within a specific group of animals (mostly female animals producing a constant food supply; e.g. cows, chickens) was most protective against food insecurity, while diversity between different functional groups had no significant impact. These results represent an important contribution to the work of policymakers and practitioners to better design interventions meant to effectively impact FS among smallholder 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".