An investigation of the effects of PICSA on smallholder farmers’ decision-making and livelihoods when implemented at large scale – The case of Northern Ghana
Bibliographic record
Abstract
Participatory Integrated Climate Services for Agriculture (PICSA) is an approach that has been used to date in 20 countries and benefited tens of thousands of households including over 5000 in Northern Ghana and 75,000 in Rwanda. PICSA involves trained field staff or community volunteers working with groups of farmers and includes farmers: using both historical climate information and forecasts; exploring practical options to address challenges and; using participatory decision making tools to evaluate and plan options for individual farm contexts. A survey of randomly selected farmers and detailed case studies was used in Northern Ghana to investigate the influence of PICSA on farmer’s decision-making, livelihoods, and innovation behaviours. Ninety seven percent of farmers had made changes to their practices (mean of three per farmer), including starting new enterprises and a wide range of management practices. Farmers described positive effects including on income and food security and importantly on wellbeing, and confidence in their abilities to address climate change and variability. In case study interviews farmers clearly explained the rationale for their changes as well as reporting how they actively sought and obtained further technical information and resources. Innovation processes observed are in stark contrast to those associated with linear dissemination of technology models.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".