Integrating green manure and grazing systems: A review
Bibliographic record
Abstract
Thiessen Martens, J. R. and Entz, M. H. 2011. Integrating green manure and grazing systems: A review. Can. J. Plant Sci. 91: 811–824. Green manuring, also referred to as cover cropping, is an ancient practice that is gaining popularity, especially in ecologically integrated farming systems. Much green manure research in Canada has focused on legumes, where green manure plant material is incorporated into soil. This review focuses on the role of livestock in utilizing traditional and novel green manure crops adapted to the Canadian prairies. Legume and non-legume green manure plant species are discussed in terms of suitability to grazing management by different livestock species. Integrating grazing livestock into green manure systems affects nutrient cycling and potential nitrogen (N) loss pathways. However, losses may not be substantially different from other production systems, especially when loss mitigation practices are employed. Grazing green manures may also affect soil biological and physical properties. We conclude that grazing green manures may provide economic as well as biological advantages over the traditional approach of soil incorporation. For example, a green manure biomass yield of 5000 kg ha −1 is sufficient to produce 175 kg ha −1 of animal live weight gain, providing a gross revenue of $385 to $770 ha −1 at April 2011 prices, while returning at least 75% of N and other nutrients to the field. Barriers to farmer adoption of grazed green manure systems include a lack of livestock management knowledge and infrastructure.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".