PARTICIPATORY PLANT BREEDING IN WATER-LIMITED ENVIRONMENTS
Bibliographic record
Abstract
Drought is one of the major factors limiting crop production worldwide. Dry areas are a much less homogeneous population of target environments than areas with high and reliable rainfall. In this paper we argue that a decentralized participatory plant breeding programme can address the complexity of dry areas, characterized by high and repeatable genotype × locations and genotype × years within locations interactions, more efficiently and effectively than a centralized non-participatory plant breeding programme. This is because varieties can be tailored not only to the multitude of target environments typical of dry areas, but also to diverse clients needs. In addition, varieties can be delivered in a shorter time and with a higher probability of adoption. Decentralized participatory plant breeding also has beneficial effects on biodiversity because selection is for specific adaptation rather than for broad spatial adaptation. The paper gives examples of methodological aspects including the modes of farmer selection, the precision of the trials, the efficiency of selection, the response to selection, the role of the type of germplasm and the role of molecular breeding in a participatory breeding programme. The paper gives the example of drought-resistant barley lines identified through extensive field testing and selection in a decentralized participatory breeding programme, and concludes that this type of plant breeding may be better targeted, more relevant and more appropriate for poor farmers in marginal areas.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".