Qin Xu's Doctor of Philosophy Defence
Posted on Friday, November 24, 2017
Qin Xu’s Doctoral Defence will take place on Thursday, December 14th at 10:00 a.m. in Room 101, J.D. MacLachlan Building. The title of her research presentation is: A Spatial Stochastic Model of Water Use Efficiency in Ontario Field Crop Production.
John Cranfield (Chair)
Diane Dupont (external examiner)
Richard Vyn (internal examiner)
Glenn Fox (advisory cmt.)
Gary Parking (advisory cmt.)
Glenn Fox (advisor)
Brady Deaton (advisory cmt)
Dan McKenney (advisory cmt)
Gary Parkin (advisory cmt)
While Ontario is rich in water resources, water shortages have occurred periodically during the crop growing season in Ontario in recent years. In 2010, the provincial 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, to implement policies to improve water use efficiency and monitor progress toward achieving water conservation goals. To 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 efficiency in water use in crop production in Ontario.
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. Four alternative definitions of the growing season are examined. Because of the different agronomies of the four crops, the climate and economic variables are different for each yield model. I used three panel data regression models to estimate the crop yield models, and ultimately used the estimated coefficients from the Fixed Effects models 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. I find that irrigation is a special case of a Damage Control Model. Since both the damage agent and control input are water and are perfect substitutes, the control function is linear. Second, compared with the engineering method, the demand for irrigation water from an economic approach creates more benefits for farmers and conserves more water. Last, I find that the optimal demand for irrigation water in the von Liebig model is jumpy (i.e. corner solution), but the Quadratic Model produces generally interior solutions. 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. In addition, in spite of the high fixed cost of irrigation, some counties will face water scarcity if irrigation is applied.