Bibliographic record
Abstract
Rising oil prices have been a concern for both developed and developing countries, especially in more recent years as it tends to have a crippling effect on production and transportation. Many countries have moved towards the development of fossil fuel alternatives as a means of achieving energy independence and achieving environmental targets (for example the Kyoto Protocol). Developments in both these types of energy markets (fossil fuel and renewable fuels) may impact Canadian Prairie agriculture. Most of Canadian prairie crops are exported. The Canadian prairies are land locked to some extent. The closest ocean access to the eastern portion of the prairies is the port of Churchill, but is closed during the winter season. Crops are therefore transported west through the Rocky Mountains or east through the Great Lakes to get to a port. This requires hundreds of kilometres of truck and rail transportation, which is fuel dependent. To a lesser extent, at the micro-level farmers depend on fossil fuels to operate machinery to facilitate efficient crop production. If oil prices continue on an upward trajectory, will farmers cropping behaviour change? Furthermore, the development of the bioethanol industry on the Canadian prairies has given wheat farmers another crop option. As oil prices increase, the price of ethanol increases as well. Also, demand is bolstered by renewable fuel standards and government tax exemptions or subsidies. This study seeks to put forward the notion that as oil prices increase, crop production and transportation costs also increase thereby reducing farmers’ gross margins. Also, ceteris paribus, as oil prices increase there will be an increased demand for, and an increase in the price of biofuels thereby increasing the price of biofuel feedstock. Higher feedstock prices are expected to increase the gross margins of farmers. Therefore higher oil prices drive increased crop competition between traditional cropping (cropping for food exports) and energy cropping. This thesis seeks to ascertain at what level of oil prices would farmers, in general, be willing to switch from producing wheat for traditional (hard/food wheat) purposes to bioenergy (soft/ biofuel wheat) cropping alternatives. Also under varying scenarios of oil price growth and government support to the biofuel industry, this thesis seeks to ascertain the impact of biofuel industry expansion on grain elevator pricing behaviour and the structure of the elevator industry, assuming elevators spatially compete with each other for farmers’ crops. An agent based model (ABM) is employed for this study. The model is selected over other types as the researcher wants to capture the increased complexity stemming from the competition between crops that belong to at least one distribution chain. Agent based networks allow for emergent behaviour that is obtained from the spatial competition of elevators. Finally, the agent based model allows for spatial heterogeneity in location of farmers in terms of soil quality and their proximity to an elevator, which affects crop productivity and transportation costs, respectively. The ABM (also called the FARMCHAIN model) is comprised of over 35000 farmer agents, 176 elevator agents, 6 canola crushing plant agents, 5 ethanol plant agents and 1 biodiesel plant agent located on the 20 census agricultural regions (CARs) of Saskatchewan. Farmers allocate land based on their expected gross margins. Farmers produce and truck crops to the designated distribution chain. Crops move through the chain and at every stage the associated costs are computed and apportioned to the farmer. At the end of the period, gross margins are computed and these gross margins are used in computing the expected gross margins for the subsequent period. It is found that real annual crude prices would have to be greater than $133 before farmers begin to switch to producing biofuel wheat (soft wheat) from food wheat (hard wheat). This would have to be approximately 30% higher than that of 2008 in which crude prices were at record levels. Also, if biofuel support is declining then it would take a considerably higher price to entice farmers, in aggregate, to switch.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".