Capturing the synergies among mitigation, adaptation and food security through smart agriculture practices in Muchinga Province of Zambia
Bibliographic record
Abstract
Between March 2016 and September 2020, the Canadian government supported a Southern African Nutritional Initiative (SANI) project in Malawi, Mozambique and Zambia to enable women of reproductive age and children under the age of 5 to produce, access, store, preserve and process high nutrient food. We report on Zambia’s agricultural component of the project summarizing the key food production techniques used to encourage sustainable agricultural production through the use of smart agricultural practices. These practices have the potential to allow small farm holders to adapt to climate change and offer opportunities to reduce and remove Green House Gases from these systems in order to contribute to the Nationally Determined GHG Contributions under the Paris Agreement and meet national food security and development goals. As part of the study, in person training sessions were conducted with participants on smart agricultural practices such as the promotion of local technologies around seed bed preparation of home gardens and orchards, manuring, and the use of local products to control insects, pests and diseases instead of chemicals. Apart from receiving training in sustainable practices, participants were also trained in food preservation and value addition to harvested produce and grains in order to increase the shelf life and usability of various food types as a way of promoting food security. Anecdotal evidence through follow up field evaluations and food preparation demonstration sessions showed that project participants were adapting and moving towards achieving a resilient status. These and scale up issues will be discussed in this contribution.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".