Determining adaptability of farmer bred spring wheat (<scp><i>Triticum aestivum</i></scp> L.) genotypes to Canadian organic production using stability analysis
Bibliographic record
Abstract
Abstract Participatory plant breeding (PPB) is a collaborative process between farmers, plant breeders and researchers to create germplasm specifically bred for target environments. We sought to examine the yield performance and adaptability of genotypes from an organic PPB wheat programme under organic management across the Canadian prairies. We evaluated 25 farmer genotypes and 6 commercial cultivars in locations in Alberta, Saskatchewan and Manitoba, totalling 12 organic environments. The top performers most responsive to higher yield environments were three farmer genotypes (BL34‐SW, BL43C‐TM and BJ13‐GW) and one check cultivar (Vesper). Genotype plus genotype by environment (GGE) biplot analysis indicated that one farmer genotype (BL23‐AS) and one check cultivar (Vesper) demonstrated high yield and greater organic adaptation than other genotypes tested. Two registered cultivars (AAC Brandon and Jake) had low yield and poor adaptation, as did one farmer genotype (PWA10B‐LD). Yield was positively and strongly correlated with height, anthesis and mature biomass and kernel number per unit area. The results provide evidence that early generation farmer selection is an effective breeding strategy for discovering wheat genotypes with high yield and excellent adaptability to organic production systems in Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".