Bibliographic record
Abstract
Home gardens are regarded as a way to improve the livelihood and nutritional security of small scale farming households in developing countries. Viable home gardens can improve the ability of small-holders and their communities to meet interrelated concerns of food, nutrition, health and economic security. Home gardens can increase dietary diversity as well as the availability of food throughout the year. From an economic security perspective, home gardens could play two roles: marketing of the surplus home garden produce could reduce the income risks from other income generation activities, or agricultural production decisions and saving on food expenses through the consumption of home garden produce could help the households to use their earnings for other priorities such as education of children, health and paying off debts. From a social perspective, home gardens may allow women to exert greater control over the types and quality of food consumed in the household. By using a household production model with fixed consumption levels for a number of representative households in the Wayanad district of Kerala, India, the economic impact of home gardens on six different household categories: landless households with and without home gardens; landholding households where agricultural production is relatively large in terms of its share in the total household income (between 35 – 100%), with and without home gardens (mentioned as agricultural majority households); landholding households where agricultural production is relatively small in terms of its share in the total household income (below 35%), with and without home gardens (mentioned as agricultural minority households), are examined. The impact of home gardens on male and female headed households is also assessed in the study. Whether home gardens contribute to increasing income and reducing household income variability across time is tested using simulation. The study uses data collected under the project titled ‘Alleviating Poverty and Malnutrition in Agro biodiversity Hotspots in India’ led by the University of Alberta and the M.S. Swaminathan Research Foundation, India, to estimate production and supply relationships. In addition, time use data from both male and female heads of the six household categories mentioned above, and historical price, production and rainfall data were collected to examine the impact of home gardens under uncertainty across time. Optimization results indicated positive profits and consumption value from home gardens for the sample households, regardless of the category. The percentage contributions of home garden profits to the net income levels were found to be significantly higher for agricultural minority households (20% and 39%). This respective household category constituted 71% of the total sample population. The reasons for low contributions for other household categories can be attributed to the higher income levels of agricultural majority households and the landless households’ lower diversity in the production from home gardens. Under uncertain scenarios, home gardens were able to contribute to the households’ economic security by providing income and saving on food expenditure. Most of the home garden households, relative to households without home gardens, achieved more stable net incomes even during negative market shocks (clearly visible in the agricultural majority category). Simulations across time highlighted higher mean and lower coefficients of variation for net income for households with home gardens.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".