Selection for Dry Bean Yield On‐Station Versus On‐Farm Conventional and Organic Production Systems
Bibliographic record
Abstract
ABSTRACT High yielding cultivars adapted to conventional and organic production system would maximize efficiency of nutrient usage and reduce dependence on pesticides. The objective of this study was to determine if separate breeding efforts are needed to obtain high yielding dry bean breeding lines for on‐farm organic (FO) and on‐farm conventional (FC) production systems. The six highest yielding breeding lines selected from each of FO, FC, and on‐station conventional (SC) production systems in two populations (1WS, 2WS) and their parents were evaluated for days to maturity, 100‐seed weight, and seed yield in 2007 and 2008. The production system and genotype effects were significant (P ≤ 0.01) for all three traits. Seed yield gains were between 10.5% in FC and 15.0% in SC in 1WS and between 6.2% in FO and 21.0% in SC in 2WS. Seed yield of breeding lines selected in SC was higher than those selected in FC and FO when tested across the three production systems. The SC seed yield was positively associated with seed yield in FC and FO in 2WS and with seed yield in FC in 1WS. The FC and FO seed yields were positively associated only in 2WS. The highest yielding breeding lines within and across the three PS varied by the production system in which they were selected. Thus, breeding high yielding cultivars in SC or FC may serve FO in some populations, whereas a separate breeding for FO and FC may be justified in other populations if the accompanying seed yield gains would off‐set the added costs involved in the production system specific breeding.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".