Annual forages for grass-fed livestock production and the management practices used on Canadian grass-fed farms
Bibliographic record
Abstract
Grass-fed production relies on perennial pastures to ensure high-quality forages are available for livestock. With strategic management, these pastures can in turn provide ecosystem benefits such as erosion protection, water management and wildlife habitat in addition to yielding meat and dairy products for consumers. Grass-fed production in Canada, however, often faces pasture shortages during the mid-summer when warm temperatures result in reduced pasture yields. The objective of this thesis was to better understand grass-fed production within Canada and to study the use of annual forages as a mid-summer feed option in grass-fed production. This was achieved through a research trial and the completion of farm case studies. The trial explored the use of annual forages as a mid-summer feed option in grass-fed production. Below average rainfall resulted in moisture deficit conditions in all site-years. The species tested under organic, grass-fed management were annual ryegrass, winter triticale, oat, millet, corn, and sorghum-sudangrass. Averaged across 3 site-years, sorghum-sudangrass and corn had the greatest total dry matter yield at 7138 and 5466 kg ha-1, respectively. Relative to the other crops in the study, winter triticale had simultaneously among the highest total digestible nutrients and crude protein concentrations and lowest acid detergent fibre concentrations. When the forage quality of the weeds was considered, by examining the total biomass rather than only the crop biomass, millet had simultaneously among the highest total digestible nutrients and crude protein concentrations and lowest acid detergent fibre concentrations. The farm case studies documented the management systems used on two Canadian grass-fed beef farms and two Canadian grass-fed dairy farms with a specific focus on the land base and how the land base was used to feed the cattle over the course of a year. Pasture management was a primary focus at all farms and innovative solutions to common challenges found at the farms included farm partnerships, crop-livestock integration, and grazing dairy cows with calves. The case studies confirmed that Canadian farms are producing both beef and dairy using a completely forage-based diet for livestock.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.007 | 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".