Cyclic Yield Stability of Ley Farming System in Northern Quebec
Bibliographic record
Abstract
Predicted climate change is a threat to dairy farming operations in northern Quebec. Soil conservation measures and the timeframe of crop management may contribute to reducing yield variability and also support dairy herd populations. Our objective was to compare annual and cyclic barley (Hordeum vulgare L.) and forage yield stability for 3‐yr ley farming system (barley‐mixed, forage‐mixed, forage) over 24 yr of experimentation under contrasting soil conservation practices. Treatments were chisel or moldboard plowing, and mineral fertilization (MIN) or liquid dairy manure (LDM) applied cyclically (across eight 3‐yr cycles) at 85 to 209 kg LDM‐N ha−1 and 90 to 146 kg MIN‐N ha−1 to forage and 87 to 127 kg LDM‐N ha−1 and 70 kg MIN‐N ha−1 to barley. Yield stability assessment was initialized in 1993, 1994, and 1995 and ended in 2014, 2015, and 2016, respectively. While the cyclic assessment showed no significant effect of tillage practice, LDM outperformed MIN, confirming the benefits of manure‐based ley farming systems for dairy farming. Coefficients of variation ranged from 7.8% to 14.4% for barley grain yield, 14.4% to 18.7% for barley straw yield, and 11.8% to 15.3% for forage yield. Coefficients of variation were reduced by factors of 1.86 ± 0.57, 1.87 ± 0.24, and 2.00 ± 0.25 for grain, straw, and forage, respectively, using cyclic assessments. To face the more frequent extreme meteorological events predicted by regional climatic models, feed supply could be stabilized locally and regionally at cyclic rather than yearly timescales to support dairy farming. Core Ideas Long term 24‐year experiment comparing annual and cyclic grain and forage yield stability. Cyclic assessment of yield stability provide low coefficient of variation for grain, straw, and forages. Crop supply and storage could be managed on a cyclic, rather than yearly basis.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".