Economic benefits of livestock management in Ghana
Bibliographic record
Abstract
Abstract The traditional system of producing small ruminants (sheep and goats) contributes significantly to the socioeconomic wellbeing of farm households in northern Ghana. Besides serving as an important source of income, sheep and goats also offer important non-pecuniary benefits such as the provision of manure, savings, insurance, farm portfolio diversification, and strong social relations. However, technical evaluation of traditional small ruminant systems places much emphasis on financial gains to farmers and abstracts on the non-conventional utilities derived from the livestock system. The main purpose of this study, therefore, is to estimate the overall economic benefits associated with traditional small ruminant systems by accounting for both market and non-market values in two agro-ecological zones (Guinea and Sudan savannah) of northern Ghana. Using a multistage sampling technique, we collected cross-sectional data from 249 small ruminant farmers for empirical analysis. The results show that at least 60% of the net benefits from sheep and goat production in northern Ghana are in non-marketable (non-cash) forms. The study demonstrates that the traditional free-range system of managing sheep and goats is economically viable when the non-market value derived from the system is incorporated in the economic analysis. The findings have practical implications in improving the traditional small ruminant system for higher meat productivity and income generation in northern Ghana and elsewhere in sub-Saharan Africa (SSA).
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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 | 0.000 | 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) | 0.002 | 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".