The bovine spongiform encephalopathy (BSE) crisis was a significant shock to the Canadian agricultural sector. On May 20, 2003, it was announced that an animal infected with BSE had been identified. The economic aftermath of this discovery was described as horrendous (AGO, 2004). Economic crises, such as the Canadian BSE agricultural crisis, are rare events. The rarity of these episodes supplies a unique opportunity for analysis. According to a policy review by the Alberta Auditor General (AGO, 2004), the agricultural economics discipline appeared to be of little assistance in the crisis policy design process. This research addresses this problem by exploring economic theory and policy via detailed empirical investigation. Specifically, this study evaluates agricultural support policies and producer risk preferences in the aftermath of the Canadian BSE crisis. Three research chapters address questions related to cow-calf producer behaviour and government policy. Chapter 2 focuses on designing emergency aid programs and calculating short-run quantitative benchmarks for crisis relief at the farm-level. Chapter 3 estimates observed risk preferences for a sample of Albertan cow-calf producers. Differential risk preferences help to explain diverse production responses following agricultural crises. The final research chapter, chapter 4, examines Canadas primary risk management program when there is potential for catastrophic price risk. In particular, vertical and horizontal equity criteria are used to scrutinize the distribution of net AgriStability benefits across a heterogeneous sample of cow-calf producers.

No description available.

The objective of this thesis is to examine the impacts of changes in Canada’s AgriStability program on crop allocation, particularly the change in the payment trigger associated with the shift from Growing Forward (GF) to Growing Forward 2 (GF2). To examine whether this change could affect production decisions, and thereby potentially violate the WTO’s ‘green box’ criteria, farm management models are constructed for representative farms in six different Alberta regions. To incorporate risk and uncertainty into the farm model, I assume that, instead of maximizing overall gross margin, a farmer varies her crop activities to maximize expected utility subject to technological and market constraints. The models are calibrated using positive mathematical programming (PMP), which then facilitates their use for policy analysis; however, PMP is not straightforward in the case of expected utility maximization because a risk parameter also needs to be calibrated. Possible ways to address this issue are examined. Results indicate that the initial introduction of the AgriStability program tilted farmers’ planting decisions towards crops with higher returns and greater risk, but that a change in the AgriStability payout trigger (going from GF to GF2) would not further alter land-use decisions. However, the latter shift does reduce indemnities and farmers’ expected profits. Meanwhile, increases in farmers’ aversion to risk will lead to changes in crop allocations. / Graduate / 0503 / 0508 / sheriliu@uvic.ca

Insurance has been considered as a useful tool for farmers to mitigate income volatility. However, there remain concerns that insurance may distort crop production decisions. Positive mathematical programming (PMP) models of farmers’ cropping decisions can be applied to study the effect of agricultural business risk management (BRM) policies on farmers’ decisions on land use and their incomes. Before being used to examine agricultural producer responses to policy changes under the expected utility framework, the models must first be calibrated to obtain the values of the risk aversion coefficient and the cost function parameters. In chapter 2, three calibration approaches are compared for disentangling the risk parameter from the parameters of the cost function. Then, in chapter 3, to investigate the impacts on production incentives of changes in Canada’s AgriStability program, farm management models are calibrated for farms with different cost structures for three different Alberta regions. Results indicate that farmers’ observed attitudes towards risk vary with cost structure. After joining the program, all farmers alter their land allocations to some extent. The introduction of a reference margin limit (RML) in the AgriStability program under Growing Forward 2 (2013-2018), which was retained in the replacement legislation until 2020, has the most negative impact on farmers with the lowest costs. The removal of RML significantly increases the benefits to low-cost farmers. Traditional insurance products provide financial support to farmers. However, for fruit farmers, the products’ quality can be greatly affected by the weather conditions during the stage of fruit development and ripening, which may lead to quality downgrade and a significant loss in revenue with little impacts on yields. Hence, chapters 4 and 5 investigate the conceptual feasibility of using weather-indexed insurance (WII) to hedge against non-catastrophic, but quality-impacting weather conditions to complement existing traditional insurance. Prospect theory is applied to analyze a farmer’s demand for WII. The theoretical model demonstrates that an increase in the volatility of total revenue and the revenue proportion from blueberries increases the possibility of farmers’ participation in WII. On the other hand, the increase in the value loss aversion coefficient and WII’s basis risk leads to less demand for WII. To design a WII product for blueberry growers to hedge against quality risk, a quality index must be constructed and the relationship between key weather conditions, such as cumulative maximum temperature and cumulative excess rainfall, and the quality index should be quantified. The results from a partial least squares structural equation modeling (PLS-SEM) show that the above goals are achievable. Further, rainfall and temperature can be modelled via a time-series model and statistical distributions, respectively, to provide reasonable estimates for calculating insurance premia. / Graduate / 2022-08-05