Canadian organic wheat breeding with on-farm selection: A case study using landrace and modern parents
Bibliographic record
Abstract
Participatory plant breeding (PPB) has the potential to be an alternative to a centralized breeding model for niche markets that are historically underserved. Our objectives were to evaluate the performance of two parental cultivars from a Canadian PPB program: a modern spring wheat ( Triticum aestivum L.) cultivar (5602HR) and a landrace (Red Fife)—with the progeny of the cross when selected by two farmers located 1800 km apart (denoted Farm1 and Farm2) and evaluate progeny differences from each other under organic conditions. Red Fife was 18 cm taller, matured 5 days later, and had greater lodging susceptibility, greater seed mass, and 3.5% lower protein concentration than 5602HR. Farmer genotypes were similar to 5602HR in protein concentration and lodging severity, and similar to Red Fife in plant height (14 cm taller than 5602HR) and seed mass. Farmer genotypes did not yield differently from either parent. Farmer genotypes did not differ from each other in most parameters measured; however, Farm1 had greater lodging resistance under high fertility conditions than Farm2 despite similar plant height. This research provides a proof of concept for the role that farmer selectors can play in selecting for positive traits for organic production and provide insight into organic farmers’ preferences. The research also demonstrated that using an older landrace (i.e., Red Fife) allowed positive features such as tall stature to be incorporated into the resulting progeny.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".