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published_or_final_version / Mathematics / Master / Master of Philosophy

Climate change is an important issue for public policy. Unfortunately, although there are volumes of data about climate change, many members of the public are informed about the issue by politicized interpretations of the data. This is an impediment to planning policies and strategies to counter the impact of climate change, and identifies a need for climate awareness in the public. This thesis explores using gamification to motivate people to learn about long term trends in climate data. As a model for this edutainment activity, we choose a medium that engages millions of players to learn about large sets of data - Fantasy Sports. Fantasy sports have been shown to increase the player’s knowledge and understanding about the domain of the sport being played. With the huge amount of weather data available, we have designed and developed a fantasy weather game. People manage a team of cities with the goal of predicting weather better than other players in their league, and in the process gain an understanding of the weather patterns and climate change trends for those cities. We do a user-study to evaluate our application and prove its feasibility. An evaluation of the fantasy weather game indicates that the game had the desired effect of causing players to explore weather data in more detail. The evaluation also pointed out a number of potential improvements to the current prototype. Overall, the evaluation supports using the model of fantasy sports to motivate people to learn more about weather and climate data.

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A study was conducted to quantify weather conditions at specific growth stages of Canadian western Spring wheat (Triticum aestivum) and relate those growing conditions to variations in wheat grade and quality characteristics and to develop pre-harvest prediction models for wheat quality using weather input data. Six Canadian western spring wheat genotypes were grown in five locations across the Canadian prairies during the 2003 and 2004 growing seasons. Intensive weather data was collected during the growing season at each location and used to calculate accumulated heat stress, useful heat, moisture demand, moisture supply, moisture use and moisture stress variables for numerous crop development stages. Grain samples were graded, milled and underwent an extensive analysis of flour, dough, and bread making quality. ANOVA indicated that genotype, environment and their interactions had significant effects on most quality parameters tested. Environmental contribution to wheat quality variance was considerably larger than the variance contribution of either genotype or GxE interaction. Using the weather and crop development stage information, significant regression equations with high regression coefficients were developed for most quality parameters using just a single independent weather variable. Multiple regression equations with even higher R2 values were developed using three complex weather variables, leading to the opportunity to predict wheat quality 2-5 weeks prior to harvest. Equally strong prediction models were developed utilizing basic weather variables which could be obtained from weather stations monitoring only daily maximum and minimum air temperature and precipitation. The development periods of planting to jointing and anthesis to soft dough were the stages most frequently exhibiting the highest correlation to wheat quality indicating weather needs to be monitored during the entire growing season to accurately predict quality. Grain quality forecast models were validated using 2005 weather and crop data. Prediction models developed from the 2003 and 2004 data required modification in order to accurately and consistently predict the grain properties in 2005.

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