An Agent Based Simulation Model of the Potential Impact of Second Generation Bioenergy Commodities on the Grain – Livestock Economy of South-Eastern Saskatchewan
Bibliographic record
Abstract
Second-generation biofuel technology is in its early stages of development in Canada and their impact on the Canadian Prairies is currently unclear. The development of policy incentives for second-generation biofuels must be examined carefully to give the correct signals to encourage farmers to shift land-use into the socially optimal land-use. Traditionally the policy process involves Prairie farmers and the landscape commonly modeled as being homogenous. Agricultural policy tends to be formed on the one size fits all notion through the use of aggregated data and the homogenous stereotype of Prairie farmers. The complex nature of the various soil productivity levels amongst the landscape and farmer characteristics and attitudes create impractical representations at the farm-level using traditional modelling (typically econometric or general equilibrium analysis). In this thesis an agent based simulation modelling (ABSM) methodology was used to examine the competitiveness of second-generation biofuel crops with existing crops and beef cows at the farm level and their impact on the farm structure building on the work of Stolniuk (2008) and Freeman (2005). ABSM are well suited to problems involving large numbers of interacting actors located on a heterogeneous landscape. In assessing alternative policies, ABSM considers actions between individual farmers in land markets and allows an individual agent (farmer) to make decisions representative to their farm and not from aggregated regional data, avoiding the aggregation bias found in many regional models. In addition, three sequential (strategic, tactical and recourse) optimization stages are used in order to better reflect the uncertainty and recourse decisions available to Prairie farmers to determine short-run and long-run production decisions using linear and integer programming techniques. In the first decision stage, a Mixed Integer Programming (MIP) model is used to determine long-run strategic decisions associated with herd size, perennial crops, and machinery used in annual cropping systems along with short-run decisions that optimize annual crop rotations to maximize profits. The second-stage decision is a tactical decision process in the sense that it supports the strategic investment decisions of the farm enterprise by maximizing short-run profits that utilizes linear programming (LP). The third-stage, also a LP model, is a maximization problem, as these are short-run recourse decisions using stochastic yields and stochastic prices to balance feed rations for beef cow enterprises that minimize feeding costs. Each farmer agent’s optimal decision is influenced by their own expected prices and yields, variable costs, operating capital/cash flow, and the constraints endowed by the farm agent’s land allocation. The farmer agent profiles are developed using actual census of agriculture and whole farm survey data, with each farmer agent developed differently from the next. The landscape is modelled using the actual soil productivity ratings from Saskatchewan Assessment Management Agency (SAMA) for each 640 acre farmland plot. Due to the importance of transitional and marginal lands, the landscape employed as the case study area is Census Agricultural Region (CAR) 1A of the Assiniboine River Basin of Saskatchewan. Following Stolniuk (2008), a bootstrapping procedure on historical price and yield data is used to generate 50 different price and yield time paths. The 50 different time paths are used in the model, simulating 30 years into the future to identify the structural change implications from the introduction of energy crops at the farm-level. Three scenarios are simulated including a base case scenario (no energy crops), along with two energy price scenarios ($2/GJ and $4/GJ) based on the identical 50 price and yield time paths. Perhaps not surprisingly, the simulation results indicate that energy crops have the potential to change the structure of agriculture in this region. Energy crops emerge in the model in both of the energy price scenarios, while total farm sector equity and total sector net income is improved over the base scenario. Farmers with significant quantities of marginal land would experience the greatest change in their farm structures by adopting energy crops if they chose to go down this path. Marginal land-use has a large effect on the energy crop scenarios, primarily on hay and forage acres. Beef cow farmer agents improve their situation the most over the base scenario due to the introduction of energy crops.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".