To analyze the production impacts of changes made in 2013 to Canada's AgriStability risk management program, we calibrate a crop allocation model using positive mathematical programming (PMP).Because PMP is not straightforward if farmers are assumed to maximize expected utility (as a risk parameter also needs to be calibrated), we consider possible ways to address this issue but settle on a traditional approach used in the EU's Farm System Simulator. We calibrate farm management models for six different Alberta regions and use it to determine how changes in the AgriStability's payment trigger affect production incentives. Results indicate that, although the
This paper explores the viability of relying on wind power to replace upwards of 60% of electricity generation in Alberta that would be lost if coal-fired generation is phased out. Using hourly wind data from 17 locations across Alberta, we are able to simulate the potential wind power output available to the Alberta grid when modern, 3.5 MW-capacity wind turbines are spread across the province. Using wind regimes for the years 2006 through 2015, we find that available wind power is less than 60% of installed capacity 98% of the time, and below 30% of capacity 74% of the time. There is only a small amount of correlation between wind speeds at different locations, but yet it remains necessary to rely on fossil fuel generation. Then, based on the results from a grid allocation model, we find that CO2 emissions can be reduced by about 30%, but only through a combination of investment in wind energy and reliance on purchases of hydropower from British Columbia. Only if nuclear energy is permitted into the generation mix would Alberta be able to meet its CO2-emissions reduction target in the electricity sector. With nuclear power, emissions can be reduced by upwards of 85%.
Beginning in the 1960s, agricultural economists used mathematical programming methods to examine producers' responses to policy changes. Today, positive mathematical programming (PMP) employs observed average costs and crop allocations to calibrate a nonlinear cost function, thereby modifying a linear objective function to a nonlinear one to replicate observed allocations. The standard PMP approach takes into account producers' risk aversion, which is not a very satisfying outcome because it intricately entangles the cost parameters and the producer's attitudes -biophysical aspects of production and human behavior are intertwined so that one cannot study the impact of policy on one in the absence of the other. Several approaches that calibrate both the risk coefficient and cost function parameters have been proposed. In this paper, we discuss two methods mentioned in literature -one based on constant absolute risk aversion (exponential utility function) and the other on decreasing absolute risk aversion (logarithmic utility function). We compare these methods to an approach that employs maximum entropy method. Then we use historical data from a region in Alberta's southern grain belt to compare the different outcomes to which the three approaches lead. We find that the latter approach is robust and easier to employ.Abstract: Beginning in the 1960s, agricultural economists used mathematical programming (MP) methods to examine producer responses to policy changes. Today, positive mathematical programming (PMP) employs observed average costs and crop allocations to calibrate the parameters of an assumed nonlinear cost function, thereby modifying a linear objective function to a nonlinear one to replicate observed crop allocations exactly. The standard PMP approach takes into account producers" risk aversion, which is not a very satisfying outcome because it intricately entangles the cost parameters and the decision maker"s attitudesbiophysical aspects of agricultural production and human behavior are intertwined so that one cannot study the impact of policy on one in the absence of the other. Several approaches that calibrate both the risk coefficient and cost function parameters have been proposed by different researchers. In this paper, we discuss two methods mentioned in literatureone based on assumed constant absolute risk aversion (and exponential utility function) and the other on decreasing absolute risk aversion (logarithmic utility function). We compare these methods to a more standard approach that employs maximum entropy (ME) method. Then we use crop insurance and historical data from a region in Alberta"s southern grain belt to compare the different outcomes to which the three approaches lead. We find that the latter approach is robust and easier to employ.
PurposeThe authors investigate whether an index-based weather insurance (WII) product can complement or replace existing traditional crop yield insurance for mitigating farmers' financial risks, with an application to blueberry growers in British Columbia (BC).Design/methodology/approachA hybrid model combining expected utility (EU) and prospect values is developed to analyse farmers' demand for WII.FindingsWhile weather data are used to investigate supply elements, a hybrid model combining EU theory and prospect theory (PT) is developed to analyse farmers' demand for WII. On the supply side, a quality index is constructed and the relationship between the quality index and key weather parameters is quantified using a partial least squares structural model. The authors then model weather parameters via time-series analysis and statistical distributions to provide reasonable estimates for calculating actuarially sound insurance premiums for a rainfall indexed, insurance product. This model indicates that decreases in the proportion of a blueberry grower's total revenue and revenue volatility will decrease the possibility that they participate in WII. At the same time, an increase in the value loss aversion coefficient and WII's basis risk further leads to less demand for WII. In short, a grower may decide not to participate in WII at an actuarially fair premium due to the combined effects of the above factors. Overall, while the supply analysis enables us to demonstrate that WII can potentially help in mitigating farmers' financial risks, it turns out that, on the demand side, blueberry growers are unwilling to pay for such a product without large government subsidies.Originality/valueThe authors argue that the demand for insurance may be affected by the level and the volatility of a berry grower's total revenue. Hence, the authors propose a hybrid expression that assumes a farmer seeks to maximize the total utility function to capture the rational and intuitive parts of a farmer's decision-making process. The EU represents rationality and the prospect value represents the intuitive component. Meanwhile, the authors investigate the possibility of using key weather parameters to construct a berry quality index – one that could be applied to other agricultural areas for studying the relationship between weather conditions and product quality.
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