Gender-Differentiated Contribution of Goat Farming to Household Income and Food Security in Semi-arid Areas of Msinga, South Africa
Bibliographic record
Abstract
Small-scale goat farming can significantly contribute to livelihoods, particularly in semi-arid areas where rainfall is erratic and crop farming is too risky. The study investigated the gendered-differentiated contribution of goat farming to household food income and food security in the semi-arid area of Msinga in South Africa using focus group discussions, key informant interviews and a questionnaire survey of 241 households. Using the Household Food Insecurity and Access Scale to measure the household food security of goat farming households, descriptive statistics and the Chi-square statistics, results showed a significant relationship between food security and the household socio-economic parameters such as the education level of the household head (p < 0.05), the gender of the household head (p < 0.05) and the total household income (p < 0.01). The Tobit regression model showed that the main factors determining food security at the household level were education levels, gender and the total household income. Female-headed households were less food secure than male-headed households because they did not have reliable employment to provide adequate and nutritious food for their households. Therefore, empowering women is crucial to ensuring food security because unstable employment opportunities lead to households’ failure to cope with food insecurity adequately. Goat farming did not contribute to household food security because it generated little income as goat sales were generally low, with a mean of 2.1 for male headed-households and 1.0 for female headed-households in 12 months (p < 0.05). Farmers obtained little income from goat farming because goat flock sizes for most households did not increase due to poor nutrition, diseases, predation, and theft. With the household food basket cost reported to be ZAR3 400/US$188, a household would need to sell up to four goats each month to survive solely on goat farming. However, where goat flock size was small, households limited goat sales to maintain the potential to increase their flock size. Empowering women by promoting rural education may increase their chances of being exposed to better management options, acquiring a better understanding of goat management practices, and making informed decisions, thereby contributing to the improvement of food security. Enhancing goat production is essential to increase flock sizes, as this enables farmers to make more sales, thereby improving food security. Therefore, extension workers need to help farmers better manage and utilize goat farming to their full potential. Finally, rural households need to reduce their autonomy and dependency on supermarket goods and become more agri-oriented.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".