Abstracts
Extreme Weather Events and Crop Yield
Juan Cabas1, Alfons Weersink
1University of Guelph
jcabas@uoguelph.ca
In order to determine how Ontario farmers can best adapt to any changes in climate and weather events, it is necessary to estimate the impacts of those events on crop yields. Estimations can be based on crop biophysical simulation models or regression models but the latter have the flexibility to integrate physiological and socio-economic factors. The purpose of this research is to estimate the effects of climate variability and extreme events on the main crops in Ontario in order to assess the impacts of predicted changes in climate and weather on those crops. The regression model in this study will be estimated using county-level data over a period of 40 years. Yield will be regressed against economic, site and climatic variables. Economic variables include crop and input prices while site variables include land quality measures and location estimates. Previous studies such as Segerson and Dixon (1999) and Chang (2002) have used average climatic variables such as total monthly precipitation and mean monthly temperatures. However, it is not only average temperature or precipitation that is critical to farmers but also the inter- and intra-annual variations in climate. This study is the first yield regression approach to include extreme climatic events including the length of drought periods, frost-events, and the distribution of rainfall amounts. The results of the model will show the impacts of climatic events on crop yield, which can then be used to predict the consequences of climate change on agricultural production.
In order to determine how Ontario farmers can best adapt to any changes in climate and weather events, it is necessary to estimate the impacts of those events on crop yields. Estimations can be based on crop biophysical simulation models or regression models but the latter have the flexibility to integrate physiological and socio-economic factors. The purpose of this research is to estimate the effects of climate variability and extreme events on the main crops in Ontario in order to assess the impacts of predicted changes in climate and weather on those crops. The regression model in this study will be estimated using county-level data over a period of 40 years. Yield will be regressed against economic, site and climatic variables. Economic variables include crop and input prices while site variables include land quality measures and location estimates. Previous studies such as Segerson and Dixon (1999) and Chang (2002) have used average climatic variables such as total monthly precipitation and mean monthly temperatures. However, it is not only average temperature or precipitation that is critical to farmers but also the inter- and intra-annual variations in climate. This study is the first yield regression approach to include extreme climatic events including the length of drought periods, frost-events, and the distribution of rainfall amounts. The results of the model will show the impacts of climatic events on crop yield, which can then be used to predict the consequences of climate change on agricultural production.
2005-04-05 |
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