Adoption of improved grains legumes and dryland cereals crop varieties: A synthesis of evidence
Bibliographic record
Abstract
Like other crop improvement programs, a key prerequisite for the CGIAR Research Program on Grain Legumes and Dryland Cereals (CRP GLDC) to generate large-scale impact is large-scale adoption. Hence, evidencing the breadth and depth of such adoption is both of intrinsic interest and important for estimating downstream impacts, such as improved food and nutritional security, income, resilience, and soil health. While various GLDC adoption studies have been undertaken, a recent effort to systematically review these studies and synthesize the results is lacking. We undertook such a review, identifying 69 studies and 35 independent country crop combinations (CCCs). To generate aggregated and updated estimates of GLDC improved varietal adoption, we devised and applied a procedure to estimate national cropping areas under such varieties and, in turn, the number of adopting households. Estimates derived from household surveys and expert opinion solicitation are treated with higher and lower levels of confidence, respectively. As of 2019, we estimate from higher confidence studies that improved GLDC crops were cultivated on 15.37 million hectares of land by 17.64 million households in CRP GLDC’s 13 priority countries. With the inclusion of lower confidence studies, these numbers increase to 32 and 44.64 million, respectively. We are further confident that the program exceeded its adoption target of 8.9 million newly adopting households from 2011, particularly when likely spillovers vis-à-vis non-surveyed areas, non-priority countries, and non-priority crops in priority countries are considered.
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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 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".