Grain Yield and Quality of Organic Crops Grown under Reduced Tillage and Diversified Sequences
Bibliographic record
Abstract
Core Ideas An organic field trial in the Brown soil zone examined tillage intensity in a simplified (wheat‐green manure) and a diversified (wheat‐oilseed‐pulse‐green manure) cropping system. Yield variation was explained more by precipitation and soil nitrate levels than by weed infestations. Wheat yields were higher under high than low tillage, and in the simplified than the diversified rotation. Protein concentration in wheat grain varied among years, and there was no negative association with yield. Based on observations in the wet years this trial was conducted, the low tillage treatment did not appear to be viable for more than a few years. Because of the increased consumer demand for organic products and expansion of organic production in the Canadian Prairies, development of organic cropping systems that help lower production costs and risks, while improving productivity and environmental sustainability is needed. A trial was conducted in the Brown soil zone (2010–2015) to examine tillage intensity (low vs. high) in a simplified (wheat [Triticum aestivum L.]‐green manure), and a diversified (wheat‐oilseed‐pulse‐green manure) cropping system. Above‐average precipitation in the years this trial was run resulted in high weed infestations and persistent increases in perennial weeds over time, especially under low tillage. Grain yield was highest at the beginning of the trial (2010 and 2011), and lowest in its final year (2015), which was drier than the rest. Yield variation was explained more by precipitation and soil nitrate levels than by weed infestations. Overall, wheat yields were higher under high than low tillage, and in the simplified than the diversified rotation. Over the duration of this trial, yields under low tillage averaged about three quarters of the yield in a nearby conventional zero‐till trial. Protein concentration in wheat grain varied among years, and there was no negative association with yield, which might be explained by the release of mineralized N throughout the growing season. Protein was similar or higher than the average for commercial conventional wheat in this area. Based on observations in the wet years this trial was conducted, the low tillage treatment did not appear to be viable for more than a few years.
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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) | 0.001 | 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".