Farm Behaviour and Incentives for Animal Welfare: On Stimulating Interest in Cow Life Expectancy by Industry Attentiveness
Bibliographic record
Abstract
This contribution deals primarily with a new concept derived from institutional economics, to improve animal health (eventually welfare, depending on the use of synonyms and actually measured as cow life expectancy, i.e. in figures: number of lactations). Based on consumer willingness to pay, it investigates a potential collaboration between a dairy industry whose aim is to diversify products and some farmers whose intention is to request compensation for a change of practices. For finding practical attributes for health, we have a focus on practices promoting numbers of lactations, currently at a low level in conventional farming. We distinguish farm types by strategies asking why most farms are primarily aiming at maximal efficiency (feeding concentrates for high milk yields and having no grazing). Vice versa: this has raised public concern because (with big herds, high milk yields and minimal lactations) farmers seem to stress animal welfare. We assume WTP exists for an improvement in animal health (though diffuse so far). I.e. on the one hand as a symptom of crisis, successes for gestation are low (almost half compared to those of farms “caring” for animals). On the other hand better practice can be financed if targeted by WTP. Further assumptions are: even the industry may admit problems with animal health, and within consumers’ and citizens’ circles, there is an increasing awareness and that WTP (finance) may enable private solutions. WTP could be used for those farmers doing better on animal welfare; but so far, markets have failed. We are confronted with different strategic behaviour of farmers (by sectors) whose commencing points (as observation and deliberation) must be a willingness to change practices. A starting point should be insight into behaviour(al) change and willingness to increase animal health (gestation), yet based on compensation. Compensation can be used to get more farmers interested in animal health, but it must be differentiated according to actions for improvement. In an institutional economics analysis of animal welfare, we will work out a concept of optimal compensation, preferably achieving cooperation between a dairy industry and willing farmers to lodge payments received from consumers. It means working on participation of actors in product diversification (milk identified by different sources) and transfer of money to those farmers who are actually working for animal health concerns. The paper further addresses selection of farms which manage to achieve set health goals and assure confirmation of achievements in increasing health. The goal is to increase the number of lactations. By utilizing contracted numbers of lactations as the basis for modelling a quantitative criterion which adequately shall reflect aspects of working for animal health (such as feeding practices, grazing, better husbandry (space and straw), caring (stress recovery), etc., is worked out and animal welfare shall improve.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".