A Spatial Stochastic Model of Water Use Efficiency in Ontario Field Crop Production




Xu, Qin

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University of Guelph


In 2010, the Ontario legislature passed the Water Opportunities Act. One of the provisions of the Act is that the Minister of Agriculture, Food and Rural Affairs may be required to set water conservation and efficiency targets for the agricultural sector, and implement policies to improve water use efficiency. To help address this goal, the purpose of this thesis is to develop a spatial stochastic simulation model that could be used to project potential future changes in crop production under alternative climate change scenarios and also analyze the effectiveness of alternative adaptation strategies aimed at improving water use efficiency. There are three elements in this thesis. The first element presents a set of empirical bio-economic county level crop yield models for grain corn, soybeans, winter wheat and hay based on data from 1950 to 2013. The crop yield models include climate, physical, and economic variables. The estimated coefficients are applied in the third element of the thesis - a spatial stochastic crop production simulation model. The second element of the thesis consists of a theoretical model of irrigation water demand for field crops in Ontario. I use a two input approach with von Liebig and quadratic functional forms of the relationship between inputs and yield. The approach models irrigation as a damage control input, where the damage agent is a water deficit. This theoretical model is also used in the spatial stochastic crop production simulation model. The third element of the thesis consists of a spatial stochastic bio-economic crop production model for the major agricultural areas of Ontario, based on the crop yield models and the irrigation demand model. This model is used to simulate crop yields, production levels and revenues at the county and regional levels under alternative climate change scenarios and alternative water management practices from 2020 to 2070. I find that winter wheat appears to be the most vulnerable crop with respect to climate change. In addition, although irrigation could raise the crop yields, the current high fixed cost of irrigation makes this option unattractive to farmers.



crop yield model, climate change, water use efficiency, spatial stochastic simulation model, demand for irrigation water