Analyzing the factors driving the adaptability and robustness of mixed ruminant herds in grassland systems
Bibliographic record
Abstract
CONTEXT Diversified systems that work to agroecology principles are a pathway worth exploring. However, the complexity of these systems and their high dependency on environmental conditions creates issues around the methods needed to assess them and the proxies needed to design them. OBJECTIVE The aim of this article is to characterize the robustness and adaptability of mixed herds in the face of environmental hazards. It also aims to identify the structural drivers (herd size and composition) of these two properties. METHODS Here we used the viability theory modelling approach calibrated on data from a long-term experiment to investigate the adaptability and robustness of mixed ruminant herds to meteorological and economic hazards, and their structural drivers. We applied our model to grass-based dairy-cattle and suckler-sheep herds. RESULTS AND CONCLUSIONS Results show that expected economic constraint is a determinant factor in the shape and composition of viable herds. Herd size and proportion of adult cattle in the herd are drivers of robustness in situations of uncertainty. The results also show that mixed herds are particularly valuable in situations with low economic requirements, especially in terms of herd adaptability to environmental hazards. SIGNIFICANCE Our results are consistent with existing mixed systems in western Europe but call for a change in the scale of analysis to include farm-level dynamics, associated management practices (land-use trade-offs, forage management, etc.) and uncertainties. This work questions the specialization of livestock farms and public policies to support agroecological transition and emergency aid for farmers.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".