The effects of legume green manures, perennial forages, and cover crops on non-renewable energy use in western Canadian cropping systems
Bibliographic record
Abstract
Inputs such as machinery, fuel, pesticides and fertilizers contribute to energy expended in cropping systems. Reducing non-renewable energy use (EU) and increasing energy use efficiency (EUE) can make cropping systems more sustainable. Nitrogen benefits of legumes to succeeding non-leguminous crops are well documented. This study examined the effect of green manure and perennial forage legumes on energy efficiency of crop production for four western Canadian crop rotation studies: Lethbridge, AB; Swift Current, SK; Indian Head, SK; Glenlea, MB. Relative to continuous grain rotations, rotations containing 50% perennial forage legumes decreased EU by up to 85% and increased EUE by up to 438%. Relative to cereal, pulse and oilseed rotations, they reduced EU by up to 28% and increased EUE by up to 294%. Rotations containing green manure legumes decreased EU by up to 65%, and increased EUE by up to 196%. The primary contribution of legumes to lower energy use was nitrogen addition to the soil. Depending on site and rotation, economic performance of legume rotations varied compared to annual grain rotations. The rotational benefits of relay intercropped and double cropped legumes in cont nuous grain systems in Manitoba were also investigated. When examining relay intercropped alfalfa and red clover and double cropped chickling fetch and lentil, it was found that considerable nitrogen benefits were provided to a succeeding oat crop by all legumes at Winnipeg, and by some legumes at Carmen. Reduced legume growth at Carmen, due to drought conditions, resulted in few yield benefits from the relay intercropped and double cropped legumes. Including relay intercropped and double cropped legumes in continuous grain rotations reduced energy use by up to 39%, and increased energy use efficiency by up to 28%. Increasing the frequency of legumes in cropping systems shows promise to enhance agricultural sustainability.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".