Production Flexibility in Extensive Beef Farming Systems
Bibliographic record
Abstract
The aim of this work is to assess the flexibility of production allowed by extensive production conditions faced with variations in the environment, i.e., market variations and climatic fluctuations, of Limousin beef systems. The study used a case-based methodology in which seven beef farms with less than 1 LU/ha were chosen. Data collection was based on three interviews using a semistructured questionnaire and on the analysis of productive and economic results over a 15-year period (1991-2005). The main evolution of these farms is related to a rise in work productivity associated with an increase in herd size. Herd increase was made possible by enlarging the area, the margin of intensification being limited in these regions. To take advantage of the enlarged land area, females were reared for fattening or for reproduction instead of selling them at weaning. The Limousin female provides a wide product mix because of its plasticity, as has been studied by several researchers. This mix flexibility is achieved by delaying product differentiation, a form of production flexibility that can reduce the risk of under-producing or over-producing varied product configurations. On the other hand, calves sold to the Italian market after weaning are generic products, associated with a flexible production process to overcome fluctuations in forage availability due to climatic variations. The introduction of maize silage for feeding acts as an alternative route, actual and potential, through the system to overcome unexpected forage shortage from natural grasslands as a result of droughts. The study shows that extensive farming systems have developed types of flexibility to match different factors of uncertainty from the environment. Finally, the issue of farm system performance is thus not so much a question of whether a farm is fit at a specific moment in time, but whether it transforms into a less or more sustainable orientation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".