A Promising Niche: Current State of Knowledge on the Agroecological Contribution of Alternative Livestock Farming Practices
Bibliographic record
Abstract
Agroecology is increasingly used to study the evolution of farms and food systems, in which livestock plays a significant part. While large-scale specialized livestock farms are sometimes criticized for their contribution to climate change and nutrient cycle disruption, interest in alternative practices such as raising multiple species, integrating crop and livestock, relying on pasture, and marketing through short supply chains is growing. Through a narrative review, we aimed to determine if the scientific literature allowed for an evaluation of the agroecological contribution of alternative livestock farming practices. Taking advantage of ruminants’ capacity to digest human-inedible plant material such as hay and pasture on marginal land reduces the competition between livestock feed and human food for arable land. Taking advantage of monogastric animals’ capacity to digest food waste or byproducts limits the need for grain feed. Pasturing spreads manure directly on the field and allows for the expression of natural animal behavior. Animals raised on alternative livestock farms, however, grow slower and live longer than those raised on large specialized farms. This causes them to consume more feed and to emit more greenhouse gases per unit of meat produced. Direct or short supply chain marketing fosters geographical and relational proximity, but alternative livestock farms’ contribution to the social equity and responsibility principles of agroecology are not well documented. Policy aimed at promoting practices currently in place on alternative livestock farms is compatible with agroecology but has to be envisioned in parallel with a reduction in animal consumption in order to balance nutrient and carbon cycles.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".