Crop rotation diversity and tillage effects on soil and wheat grain nutrient concentration in an organically-managed system
Bibliographic record
Abstract
Consumer demand for high quality organic produce in Canada has in recent years contributed to the development of a more sustainable organic agriculture system that increased productivity without decreasing soil fertility. The objective of this 4-yr field study was to determine if an organic system with diversified crop rotations under reduced tillage would produce high quality spring wheat ( Triticum aestivum L.) grain and improve soil fertility. A simplified rotation (SR) consisting of a forage pea ( Pisum sativum L.) green manure (GM)-spring wheat sequence and a diversified rotation (DR, forage pea GM-oilseed-pulse crop-spring wheat sequence) were compared under two tillage intensities (high tillage [HT] and low tillage [LT]). The oilseed alternated between mustard ( Sinapis alba L.) and flax ( Linum usitatissimum L.), and the pulse alternated between field pea and lentil ( Lens culinaris L.). Generally, nutrient concentrations in wheat grain were mostly significantly higher under LT than HT and in the DR compared with the SR. In addition, tillage-rotation system influenced soil zinc, calcium, magnesium, and sodium concentrations. However, grain Cd concentration was higher in the LT treatment under the DR sequence relative to other treatments. Our results indicate that organically managed diversified crop rotations under reduced tillage can produce wheat grains with high nutritional value for humans and livestock but may not increase soil micronutrient concentrations.
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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 | 0.000 | 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".