Spring wheat yield response to variable seeding rates in organic farming systems at different fertility regimes
Bibliographic record
Abstract
Seeding rates in organic systems should balance a crop’s competitive ability while maintaining grain yield and quality. A 2-yr study assessed the response of spring wheat (Triticum aestivum L.) to variable seeding rates (1 × conventionally recommended rate, 1.25 ×, 1.5 × and 2 × ) in a plot experiment in Nova Scotia and on organic farms across Canada. The plot experiment was a two-factor factorial assessing seeding rate and fertility. For the Canada-wide experiment, the four seeding rates were completely randomized on each farm. Wheat yield was highest at the 2 × seeding rate in 2003, but average crop emergence across all treatment combinations was only 56%. In 2004, crop emergence was 76% and the 1.25 ×, 1.5 × and 2 × seeding rates had greater yield than the 1 × rate. Seeding rate affected plant density and heads per square metre, but no differences were observed among rates for kernels per head or thousand kernel weight. Grain protein content was similar among seeding rates and was increased by the high fertility treatment. On farms, a 1.25 × seeding rate was sufficient to maximize yield when averaged across all sites. Increasing seeding rate by at least 1.25 × the conventionally recommended rate appears to be an appropriate management practice for organic production. Key words: wheat (spring), seeding rate, crop density, organic agriculture
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 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".