Beyond the Pasture: A Review of Business Risk and Rangeland Management Decisions
Bibliographic record
Abstract
Business risk management (BRM) programs in agriculture aim to enable producers to better protect their farms against threats to the sustainability and resilience of their business. While BRM programs are broadly considered beneficial to producers, the extent to which these programs could evoke negative environmental externalities remains understudied, especially concerning rangeland management. The purpose of this review was to investigate the link between BRM programs and the voluntary conservation of grasslands and its associated multiple species habitat, as well as the factors driving adoption of BRM, extension, or other government programs by beef cattle producers. A secondary focus of this review included identifying the factors motivating the adoption of beneficial management practices (BMP) and grassland conversion. Using a variety of search tools and terms, the application of our search strategy resulted in 30 articles meeting inclusion criteria, of which four were reviews, 14 were producer focused surveys, 11 conducted empirical analyses, and one utilized spatial analysis. From these articles, we were able to answer our five main global research objectives. Several studies found a link between BRM programs and negative environmental effects (including motivating grassland conversion). Producer characteristics such as age, education level, previous participation in government programs, and external motivations were found to impact the likelihood of adopting a BMP or new BRM program. Producers were also found to favour individually-led risk management strategies, and preferred learning about risk through independent self-study or from a trusted professional. Research gaps included the level of governmental support desired by producers, a connection between non-crop insurance BRM programs and rangeland conversion, and connections between BRM and rangeland management decisions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".