A Pipeline Strategy for Grain Crop Domestication
Bibliographic record
Abstract
In the interest of diversifying the global food system, improving human nutrition, and making agriculture more sustainable, there have been many proposals to domesticate wild plants or complete the domestication of semidomesticated orphan crops. However, very few new crops have recently been fully domesticated. Many wild plants have traits limiting their production or consumption that could be costly and slow to change. Others may have fortuitous preadaptations that make them easier to develop or feasible as high‐value, albeit low‐yielding, crops. To increase success in contemporary domestication of new crops, we propose a pipeline approach, with attrition expected as species advance through the pipeline. We list criteria for ranking domestication candidates to help enrich the starting pool with more preadapted, promising species. We also discuss strategies for prioritizing initial research efforts once the candidates have been selected: developing higher value products and services from the crop, increasing yield potential, and focusing on overcoming undesirable traits. Finally, we present new‐crop case studies that demonstrate that wild species’ limitations and potential (in agronomic culture, shattering, seed size, harvest, cleaning, hybridization, etc.) are often only revealed during the early phases of domestication. When nearly insurmountable barriers were reached in some species, they have been (at least temporarily) eliminated from the pipeline. Conversely, a few species have moved quickly through the pipeline as hurdles, such as low seed weight or low seed number per head, were rapidly overcome, leading to increased confidence, farmer collaboration, and program expansion.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".