How did conservation agriculture go to scale?
Bibliographic record
Abstract
Background: The Foodgrains Bank has an established record working in agriculture and food security with resource constrained, marginalized farmers in sub-Saharan Africa. The three outcome areas of the Scaling-Up Conservation Agriculture in East Africa (SUCA) Program were: the adoption of conservation agriculture systems, an enabling institutional environment, and the promotion of enabling policies. These program areas were expected to yield intermediate outcomes that, together, would lead to the ultimate outcome of improved food security and sustainable livelihoods for smallholder farming households in East Africa. This case study reports on the end-line evaluation of the five-year program. Purpose: To illustrate the overlap between utilization-focused evaluation (UFE) and collaborative approaches to evaluation (CAE). The case study profiles an agricultural intervention, and explores how the evaluation design accommodated the systemic nature of the program. Setting: Scaling-Up Conservation Agriculture in East Africa (SUCA) was a five-year program of the Canadian Foodgrains Bank implemented from 2015-2020 to expand the size and scope of Foodgrains Bank’s work in conservation agriculture in East Africa. The program supported local partners with a target of 50,000 male and female farmers practicing a minimum of 2 of 3 conservation agriculture principles, and to improve food security and sustainable livelihoods for 18,000 of these farmers’ households across three countries. Research design: The Foodgrains Bank was directly involved in the evaluation design through the definition of evaluation uses and key evaluation questions. Eleven implementing partners in East Africa were involved in primary data collection and some initial analysis. Data collection and analysis: A mixed method approach was used combining quantitative, qualitative, and participatory / visual data collection tools. A robust, intersectional gender lens was applied to the data collection instruments in the form of gender disaggregated data collection and gender-focused questions across most data collection instruments. Findings: The collaborative process confirmed a sense of ownership by the primary evaluation users over the evaluation design. The evaluation design combined outcome and learning uses that took advantages of the implementing organizations’ commitment to learning. The findings demonstrated the value of the program and produced a framework illustrating the multi-disciplinary approach underlying its success.
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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 | 0.034 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".