Best Management Practices and the Production of Good and Bad Outputs
Bibliographic record
Abstract
Agricultural activities simultaneously produce good and bad outputs. A translog cost function is used to evaluate the cost associated with reduction of chemical runoff and how it is influenced by the scale of crop and animal production. The results show that reducing runoff entails increasing costs and that these costs decrease with the level of crop production, but are unaffected by the level of animal production. The estimates of the cost elasticities of Best Management Practices (BMPs) were all positive, but many have large standard errors that imply that the true elasticities can be much lower or much higher. Also, the cost elasticities decrease with the scale of crop production for most BMPs whereas the scale of animal production has the opposite effect for crop rotation and herbicide control practices. Our results reaffirm that there are economies of size in production. L’agriculture produit de bons et de mauvais extrants. Une fonction de coût translog est utilisée pour évaluer les coûts associés à l’amélioration de la qualité de l’eau en réduisant les problèmes de ruissellement et de transfert de nutriments. Nous évaluons comment ces coûts changent avec les niveaux des productions animales et de cultures. Nos résultats indiquent que la réduction des transferts de nutriments est coûteux mais que l’élasticité‐coût décroit avec la valeur des cultures produites tout en étant indépendant de l’envergure de la production animale. Les élasticités‐coût associés aux pratiques environnementales (PE) sont positives et ont de grandes erreur‐types, ce qui veut dire que les vraies élasticités peuvent être beaucoup plus grandes ou plus petites. Les élasticités‐coût pour la plupart des PE diminuent avec la production de cultures produit tandis que la taille de la production animale a un effet contraire sur les élasticités pour la rotation de cultures et le contrôle des herbicides. Nos résultats confirment qu’il existe d’importantes économies de taille à exploiter en agriculture.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".