Adaptability analysis in a participatory variety trial of organic vegetable crops
Bibliographic record
Abstract
Abstract Successful organic farming requires crop varieties that are resilient to environmental variability. Assessing variety performance across the range of conditions represented on working farms is vital to developing such varieties; however, data collected from on-farm, participatory trials can be difficult to both collect and interpret. To assess the utility of data arising from participatory trialing efforts, we examined the performance of butternut squash ( Cucurbita moschata L.), broccoli ( Brassica oleracea L.) and carrot ( Daucus carota L.) varieties grown in diverse organic production environments in participatory trials in Oregon, Washington, Wisconsin and New York using adaptability analysis (regression of variety means on environmental index). Patterns of adaptation varied across varieties, with some demonstrating broad adaptation and others showing specific adaptation to low- or high-yielding environments. Selection of varieties with broad vs specific adaptation should be guided by farmers’ risk tolerance and on-farm environmental variation. Adaptability analysis was appropriate for continuous variables (e.g., yield traits), but less so for ordinal variables and quality traits such as flavor and appearance, which can be vitally important in organic vegetable crop variety selection. The relative advantages of adaptability analysis and additive main effects and multiplicative interactions are also discussed in relation to on-farm trial networks. This work demonstrated the unique challenges presented by extensive participatory vegetable trialing efforts, which, as compared to grain crops, require novel approaches to facilitating farmer participation as well as data collection and analysis. Efficient, precise and reliable methods for evaluating quality related traits in these crops would allow researchers to assess stability and adaptation across a wider range of traits, providing advantages for effective plant breeding and trialing activities within the organic sector.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".