The following approach persues five objectives, in order to identify ultimately identify the effects of climate change on agriculture production of Winter Wheat crops in Southern Ontario
Objective 1: To determine factors, data and variables for the project as they relate to crop yield and future climate change
In order to complete the research, multiple data sets and corresponding factors are required. A substantial review of litature on the subject of variables affecting the production of crops is required. The first type of data needed is the historic and present yields of winter wheat in the study area. This will be used in order to create a baseline of crop production with which to evaluate any changes from. This is spatially referenced raster data sourced from Agriculture Canada’s Annual Crop Inventory. The second data set required is the anticipated future climate models for Ontario. The data for this will be sourced from the Ontario Climate Change Data Portal. Within this data set it’s possible to select variables such as max temperature, mean temperature, minimum temperature and precipitation through a time period ranging from 2020-2099. Additionally, it is possible to narrow it down to focus on specific months as well, which is highly applicable as this research is focusing on growing seasons. Next, past climate data for the study area is required. This provides the analysis with a baseline data set to the see the correlation between climate and crop yields. This data will be sourced from Environment Canada’s historic data site. Lastly, data on the current economic impact of winter wheat is required. This data will allow the impact of any yield increase or decrease to be communicated in a tangible way. This data can be sourced from the Ontario Ministry of Agricultural Food and Rural Affairs.
Objective 2: To perform a spatial regression analysis to generate a prediction for future crop yields
This objective is the general overview of the research project from a conceptual level. The objective of the research is to determine future impacts on agriculture from climate change. Using GIS software, a spatial regression analysis is performed to predict future crop yields under the constraints of climate change. The regression equation is based off the formula .In this formula, Y represents the dependent variable, which, for the purposes of this research, is future crop yield (Regression, n.d.). The explanatory variables (X), are future temperature, future precipitation and current crop yield of winter wheat. These are the primary factors of future crop yield, as future crop yield is a function of temperature, precipitation and current crop yield. ß are the regression coefficients of each explanatory variable. These are the values that represent the strength of the relationship between the explanatory and dependent variable (Regression, n.d.). These coefficient values are calculated by the regression tool (Regression, n.d.). For the purposes of this study, the regression tool used will be the Geographically Weighted Regression tool in ArcGIS. This tool performs a regression analysis on data that has spatial attributes (Regression, n.d.). Upon completion of the spatial regression, it will be possible to compare future yields under climate constraints to past and present yields. This process is depicated through the below workflow figure.
Objective 3: To compare and interpret the anticipated changes for winter wheat under past, present and future yield predictions
After accumulating past and present crop yields for winter wheat, and creating future anticipated yields, this data will then be visually displayed. The basic paradigm of visualization is that if users are able to see, they will be more likely to understand, thus providing support for the use of techniques, such as these, to display data (Pundt & Brinkkötter-Runde, 1998). Nine thematic maps are created from the data accumulated. Symbolization techniques will be carefully selected in order to best represent any change in crop yield that may occur over time. For example, if there is a reduction in crop yield over time as a result of climate change, it may be represented in red, in order to easily communicate to the user that this is a negative impact.
Objective 4: Compare the future crop yields to current crop yields and economic impact in order to determine economic impact of future crop yields
This objective will be met by comparing the projected crop yields to current crop yields and determining a percentage increase or decrease in anticipated yields. The equation is A=B/(CX100), where A is the percentage change, B is the projected crop yields and C is past crop yields. This percentage increase will then be applied to the economics of current crop yields to determine how much climate change will impact the economic output of agriculture. This will be measured as the total value of the wheat produced. The data on current economic impact will be sourced from the Ontario Ministry of Agriculture, Food and Rural Affairs Economic Indicators. For example, if it is determined that a certain crop yield will increase 10%, then a 10% increase will be applied to current economic impact in order to determine future economic impact. This will be a simplified analysis based on a steady market and pricing. This analysis is vitally important as it gives weight to the climate change projections. Recently, politicians have started to recognize that it’s impossible to discuss economics without considering climate change (Keohane, N., 2018). By converting the findings on the impacts of climate change into economic terms, it allows politicians and decision makers to appreciate the true impact of climate change.
Objective 5: Evaluate the strengths and weaknesses of this approach
This objective will be completed upon conclusion of the research. The research methods will be critically analyzed to find flaws and areas for improvement. While impossible to fully predict challenges before completing the analysis, a few strengths and some potential weaknesses are known already. A strength is that the data is accurate and current for the research requirements. A potential weakness is the simplicity of the crop yield formula. While the formula has been proven to create accurate predictions of crop yield, based off of accuracy between past actual yields and past predicted yields using the formula, it is still a highly simplistic formula. It is simplistic in that it only calculates yield based on two factors, precipitation and temperature. In reality, there are countless factors affecting crop yield including solar radiation, atmospheric humidity and pests.