Breeding for Competitive and High‐Yielding Crop Cultivars
Bibliographic record
Abstract
ABSTRACT Weed control with herbicides is not possible in several systems including control of wild oat ( Avena fatua L.) in tame oat ( Avena sativa L.), red rice ( Oryza sativa L. var. sylvatica ) in rice ( Oryza sativa L.), and in organic systems. Competitive crop cultivars can be used to manage weed competition if selective weed control by herbicides is not possible. However, most existing competitive crop cultivars often have low weed‐free yield and poor grain quality. We hypothesize that the progeny of a cross between a competitive forage‐type oat cultivar and a high‐yielding and high grain quality grain oat cultivar will have high yield and good grain quality as well as high competitive ability (CA). The objective of this study was to evaluate progeny lines from a cross between a tall, competitive, forage‐type oat cultivar with a semidwarf, high‐yielding milling oat for their CA against wild oat. A field study was performed in two locations in Saskatoon, SK, Canada, in 2008 and 2009 using seven progeny lines and the two parents. Oat was seeded with and without wild oat at a target crop and weed density of 250 plant m ‐2 in a randomized block design with four replicates. All the genotypes were high yielding and did not differ in grain yield or quality either in weed‐free or weedy conditions. Some of the tall (CDC Baler, SA050498, and SA050479) and short (SA050040 and Ronald) genotypes had high average grain yield under both weed‐free and weedy conditions compared with the other genotypes. The tall oat line SA050479 was among the most weed suppressive, which resulted in less wild oat biomass. The seedling total leaf area, crop height, and the seedling third leaf area were negatively correlated with wild oat biomass. This study demonstrates that a cross between a high‐yielding and a highly competitive genotype can result in progeny with high CA, yield, and crop quality.
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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 | 0.000 |
| 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) | 0.000 | 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".