Economics of Beneficial Management Practices Adoption by Beef Producers in Southern Alberta
Bibliographic record
Abstract
Beneficial Management Practices (BMPs) are a means by which the provision of ecosystem services and sustainability of agricultural production systems may be enhanced. However, achieving widespread adoption of BMPs may require policy intervention because studies have shown that the adoption and implementation of many BMPs are costly. The research carried out in this project involves an analysis to assess the economics of adoption by southern Alberta cow-calf producers for a specified set of BMPs The BMPs examined in this study are intended to improve water quality, soil quality and other environmental attributes. The analysis is conducted for a representative mixed crop-beef farm assumed to be located in the Dark Brown soil zone of Alberta. Stochastic crop prices and yields as well as stochastic beef prices are incorporated in the analysis, along with participation in public business risk management programs (e.g., crop insurance). The study uses dynamic Monte Carlo Simulation and Net Present Value analysis methods to estimate farm-level costs and benefits of BMPs. The BMPs examined in the study include rotational grazing, crop residue management, enhancing tame pasture productivity through incorporation of legumes (alfalfa), manure management, and conservation of natural areas (i.e., retirement of native pasture area). Results obtained from the analysis are mixed. Manure management results in a relatively small annual benefit per acre of land affected. The effects of rotational grazing and enhancing tame pasture productivity through incorporation of legumes depend on the degree to which tame pasture productivity is improved by the BMP. Conservation of natural areas and crop residue management BMPs result in a net cost per acre of land affected. Overall, economic incentives may be necessary to motivate producers to adopt BMPs that are costly. Conversely, information programs may be all the policy required in the cases of BMPs that are economically feasible on their own.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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, 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".