The agricultural marketing environment is inherently risky. Having accurate measures of risk helps farmers, policy-makers and financial institutions make better informed decisions about how to deal with this risk. This article examines three tail quantile-based risk measures applied to the estimation of extreme agricultural financial risk for corn and soybean production in the US: Value at Risk, Expected Shortfall and Spectral Risk Measures. We use Extreme Value Theory to model the tail returns and present results for these three different risk measures using agricultural futures market returns data. We compare estimated risk measures in terms of size and precision, and find that they are all considerably higher than Gaussian estimates. The estimated risk measures are also quite imprecise, and become more so as the risks involved become more extreme.