PR - An Agent-based Simulation Model Of Western Canadian Prairie Agricultural Structural Change
Bibliographic record
Abstract
Western Canadian prairie farms are commonly stereotyped as large-scale grain farms located on flat lands that stretch to the horizon, but this misrepresents the diversity of farm operators, the landscape and the prairie ecosystem. This diversity has a profound impact on farm structure and competitiveness. Of particular interest are economically transitional or marginal lands between use in annual crops, forage and pasture. The primary objective of this research is to assess transitional land use, beef cow numbers, farm structure and performance under alternative price scenarios. Individual and sector performance is simulated over a period of 30 years using an agent based simulation model (ABSM). A “synthetic” farm population of 600 individual farming agents is constructed based on statistical data and located on an existing landscape of 341,530 hectares. Important model features are 1) segmented farmland auctions consisting of a primary farmland purchase market and a secondary leasing market; 2) a formalized business and farm expansion model that takes into account farm size, asset lumpiness, machinery technology and replacement policy; 3) individual agent expectations based on prior experience and risk aversion; 4) two basic farm types: grain farms and “mixed grain-cow” farms and 5) farm succession. Individual farming agent land use, success or failure in farmland markets and business prosperity are tracked over 30 years and through 100 different price and yield time paths. These are consolidated into a database and sector population statistics and farm structure are analyzed. Past economic trends such as declining farm numbers and increasing farm size are projected to continue; these trends are robust as they are generated under many different time paths and scenarios. Beef cow numbers depend upon land use which is sensitive to agent farm type preferences and wheat-beef price ratios. Large grain price increases have a more dramatic impact on industry structure by creating large structural shifts towards more grain and eventually fewer mixed farms. However, large changes in livestock prices generate smaller structural shifts over time because of the many lags and difficulties in expanding beef cow production.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".