Developing fruit cultivars for organic production systems: A review with examples from apple and strawberry
Bibliographic record
Abstract
The amount of fruit and vegetables produced under organic production systems, irrespective of how “organic” is delimited, has been steadily increasing. Organic production is largely based on cultivars that were originally developed for conventional production systems. The prospects of breeding specifically for organic systems are intriguing. One approach is identifying the major constraints with which organic growers must contend (often diseases or insect pests) and then incorporating stable genetic resistance. A more complex approach is to define an organic crop ideotype and derive an index of traits as a selection criterion. How effective are these methods expected to be for improving clonally-propagated fruit crops? In addition to the importance of the breeding goals, the technologies employed are also of concern. The topic will be examined with special reference to breeding strawberries and apples. Key words: Fruit breeding, strawberry (Fragaria × ananassa Duch.), apple (Malus × domestica Borkh.), disease resistance, selection environment
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".