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The resiliency of the Canadian
crop insurance system under
climate change: testing
quantitative methods
Robert MacGregor and Timothy John Colwill
Agriculture and Agri-Food Canada, Ottawa, Ontario, Canada
Resilience is a key attribute identified for systems that can respond to climate change and
this includes programmes that help farmers manage risk. Farmers deal with weather variability and extreme events as part of their day- to-day operations. In Canada, they utilize
a number of risk management tools provided by the Government with AgriInsurance,
Canada’s crop insurance programme, being a cornerstone of the suite of business risk managements programmes that are available to farmers. A key policy question is whether crop
insurance can continue to provide a robust response when the effects of climate change alter
the prevailing weather patterns. This analysis employs a number of models to test methodologies to determine if we have the capacity to undertake this type of policy analysis and
to provide at least some preliminary findings.
Using an integrated assessment framework, regionally downscaled climate data from
two Global Circulation Models (GCM) – the Canadian GCM and the Hadley GCM – were
used to generate daily data for the period 2040 to 2069. The EPIC (Environmental Policy
Integrated Climate) crop growth model was calibrated to better reflect growing conditions
in the Prairie region and used to generate a new set of yields for the principal Prairie crops
for each of the 22 crop districts based on the GCM-derived weather data. To establish a
baseline, EPIC was also run on weather data for 1971 to 2000. From this annual yield data,
both means and variance-covariances were generated for the baseline and the scenarios
with climate change, with and without a CO2 fertilization effect. These data were incorporated into a regional, partial equilibrium, optimization model (CRAM), developed by
Agriculture and Agri-Food Canada (AAFC) to undertake policy analysis. A risk version
of the model is employed for this analysis to reflect the fact that farmers would respond
to relative change in expected yields owing to climate change, as well as to the expected
change in the variance of those yields. To ascertain how resilient crop insurance would be
with climate change, a Monte Carlo simulation with CRAM was run with draws from the
generated yield data, allowing farmers to continue to employ crop insurance to manage
yield risk related to weather.
Based on 10 000 simulations for each experiment, Canada’s crop insurance programme
proved to be resilient in that the average annual deficit for the programme (extent to which
payouts exceeded premiums paid by both producers and government) remained similar
when the results for the historic period were compared to the future climate change scenarios. In fact the deficit declined slightly with climate change, in part owing to lower
average yields and therefore slightly lower exposure. The modeling system employed
here can help to analyse real policy questions, but further developmental work is required
to ensure all the complexities related to this type of analysis are dealt with, for example
that weather patterns can exhibit longer run dry and wet cycles that should be reflected in
the experimental design. A key advantage of the approach here is a risk framework with
changes in means and variances effecting production decisions for a cropping rotation at
the regional level.