Targeting resilience and profitability in African smallholder agriculture: Insights from ICRISAT-led research programs
Bibliographic record
Abstract
We reviewed the strategy for Agricultural Research for Development (AR4D) adopted by the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT). The objective was to examine ICRISAT’s research strategy related to the twin challenges of resilience and profitability in developing technologies aimed at improving the livelihoods of smallholder farmers in the drylands of Africa. To do this, we examined the expected impact on resilience and profitability of its present program and the realized impact of ICRISAT’s previous research. We argue that the current CGIAR Research Programs led by ICRISAT envisage separate product lines for resilience and profitability, targeted at two groups, i.e., subsistence- and market-oriented smallholders. This approach, expected to make technology more appropriate for farmers’ needs, risks overlooking the interconnectedness of the two targets if they are too rigorously separated. Although our review of ICRISAT’s previous research program suggests that success stories have taken numerous forms—some increasing resilience, others profitability—our review also suggests that it is possible to develop win–win technologies that improve both targets. Finding ways to replicate win–win technologies will require that ICRISAT tests the resulting technologies and their implementation in specific contexts to improve and replace them as the research programs evolve.
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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 | 0.021 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".