Exploring the relationships between biomass production, nutrient acquisition, and phenotypic traits: testing oat genotypes as a cover crop
Bibliographic record
Abstract
High biomass and nutrient acquisition are desirable for oat (Avena sativa L.) as a cover crop. However, our understanding of oat genotypes suitable for cover crops and associated traits is limited. The objectives of this experiment on growing oat as a cover crop, after winter wheat (Triticum aestivum L.) harvest, were to determine biomass production, nutrient uptake of a set of oat genotypes, and to identify phenotypic traits that can be used as indicators to select cultivars suitable for cover crops. The results showed that the top biomass-producing genotypes took up larger amounts of soil nutrients, up to 142 kg N ha−1 and 17 kg P ha−1 in 2016, and 43.5 kg N ha−1 and 8.3 kg P ha−1 in 2017. The biomass production was significantly related to plant height and leaf area index (LAI) in both years, and to the normalized difference vegetation index (NDVI) in 2017. Both NDVI and LAI were closely related to the total amounts of N and P uptake. The poor association between biomass and NDVI in 2016 was due to vigorous growth of volunteer wheat and weeds as well as severe rust (Puccinia coronata f. sp. avenae Eriks.) infestation. Our results suggest that it is important to choose oat varieties as cover crops. Leaf area index can be used as a nondestructive indicator for final biomass and nutrient acquisition, while both NDVI and LAI are important traits for choosing oats as soil conservation cover crops.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".