Supporting the capacities and knowledge of small-holder farmers in Kenya for sustainable agricultural futures: A Citizen Science pilot project
Bibliographic record
Abstract
Sub-Saharan Africa is often presented as the continent most vulnerable to climatic change with major repercussions for food systems. Coupled with high rates of population growth and existing nutritional deficiencies, the need to enhance food production across the continent is thus seen as a major global imperative. We argue here, however, that current models of agricultural development in Eastern Africa often marginalise critical small-holder knowledge from the process of future agricultural design due to a lack of a methodological tools for engagement. This paper addresses this by outlining a potential means to capture and share locally produced agronomic information on a large scale. We report on a ‘Citizen Science’ pilot study that worked with smallholder farmers in Elgeyo-Marakwet County, western Kenya, to co-design a mobile application using the well-developed Sapelli platform that easily allows farmers to identify, record and geolocate cropping patterns and challenges at multiple stages in the agricultural calendar using their own understandings. The pilot project demonstrated the technical and epistemological benefits of co-design, the abilities of smallholder farmers to co-design and use smartphone applications, and the potential for such technology to produce and share valuable agricultural and ecological knowledge in real time. Proof-of-concept data illustrates opportunities to spatially and temporally track and respond to challenges related to climate, crop disease and pests. Such work expounds how smallholder farmers are a source of largely untapped ecological and agronomic expert knowledge that can, and should, be harnessed to address issues of future agricultural resilience and food system sustainability.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".