Integrated Crop-Livestock Systems and the Prairie Landscape: A Feasibility Assessment
Bibliographic record
Abstract
Sustainability is a broad term and consequently, there is no concrete definition of what constitutes a sustainable farm. Inherently there is no correct way to improve on-farm sustainability, and it is recommended that farms find their best production fit by assessing a variety of possible methods for their established operation (Sustainable Agriculture Initiative Platform 2015). Integrated crop-livestock systems (ICLS) are opportunities that take ecological advantage of both cattle grazing and cover cropping, but the strength of that option in Canadian prairie conditions is relatively uncertain. The combination of missing research, economic valuations, and innovative supports have potentially established barriers preventing mainstream adoption of ICLS. If ICLS is to be a viable option for Canadian producers, then the system must be evaluated with the rigid and exogenous facets of the agricultural environment in mind. The analyses made in this thesis are from 503 farmers who responded to an April 2022 survey that assessed opinions surrounding integrated crop-livestock systems and their motivations for use. Due to resource-sharing convenience, ICLS is largely approached by mixed farms (Thiessen Martens et al. 2015), unintentionally moving the system toward a ‘niche’ standing exclusionary of single-output enterprises. In response to this trend, the survey frames ICLS as a partnership between a neighbouring crop producer and cattle rancher, and question sets partially differ based on (self-identified) farm type. Field data collected from 2019-2021 at the Swift Current Research and Development Centre provides the economic context (performance indicators) for grazing cover crops in the discrete choice experiment (DCE). Results suggest that there is an interest in integrated systems, but not necessarily in an ICLS partnership. Approximately 75 per cent of participants place trust in their neighbours for novel information and 30 per cent of respondents declared they would not work with someone they do not know, despite assurances. Partnership aside, crop farmers appear to have more ICLS apprehension than cattle producers, suggesting the necessity of stronger information networks for (performance) reassurance. This thesis suggests the social structure of Canadian agriculture is designed to prioritize independence rather than collaboration, which may be just as strong a barrier to integrated adoption as the enviro-economic trade-offs.
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 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 | 0.025 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".