Implementing Perennial Kitchen Garden Model to Improve Diet Diversity in Melghat, India
Bibliographic record
Abstract
Lack of diet diversity causing micronutrient deficiency is common in developing countries and is gaining attention due to the hidden consequences of impaired physical and cognitive development. This paper describes the propagation of a sustainable perennial kitchen garden (KG) model to address household (HH) diet diversity in Melghat. Nutrient dense plants, comprising of minimum one tree (perennial) and one green leafy vegetable (GLV) were given to participating HHs along with qualitative interventions. Baseline survey was conducted in winter 2011 followed by seasonal surveys over 2 years to record changes in KG practices, dietary intake and childcare practices. Marked increase from 4% at baseline to 95% at endline was seen in the KG maintainance. Increased diversity was seen in all food categories other than cereals and pulses. Variety of GLVs consumed increased over the two winters as well as the 2 summers. However, no change in the quantity of GLV consumed was noted which was attributed to the duration of the study period being insufficient for the trees to grow and provide adequate leaves for consumption. Notably, livelihood component was not promoted and HHs were encouraged to harvest and distribute excess seeds to relatives and neighbours. The study generated huge demand from HHs within the intervention and neighbouring villages. It concludes that a well designed perennial KG along with imparting adequate knowledge can be a sustainable practice to increase diet diversity and GLV intake which would help address micronutrient deficiencies in the community.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".