Agricultural risk managers need forecasts of price volatility that are accurate and meaningful. This is especially true given the greater emphasis on firm level risk measurement and management (e.g., Value-at-Risk and Enterprise Risk Management). Implied volatility is known to provide a readily available, market based forecast of volatility. Because of this, it is often considered to be the "best" available (e.g., optimal) volatility forecast. However, many studies have provided evidence contrary to this claim for many markets (Figlewski). This research examines the forecasting performance of implied volatility derived from the Black-1976 option pricing model in predicting 1-week volatility of nearby live cattle futures prices. Unlike many studies of implied volatility, this research takes a practical approach to evaluating implied volatility, namely from the perspective of an agribusiness risk manager who uses implied volatility in risk management applications, and thus needs to understand its forecasting performance. This research also uses a methodology that avoids overlapping forecast horizons. As well, the methodology focuses on forecast errors that can reduce interpretive issues that can arise from traditional forecast evaluation procedures. Results suggest that implied volatility derived from nearby options contracts on live cattle futures is a biased and inefficient forecast of 1-week nearby futures price volatility, but encompasses all information provided by a time series forecast (i.e., GARCH). As well, our results suggest that implied volatility has improved as a forecast of 1-week volatility over time. These results provide practical information to risk managers on the bias, efficiency, and information content of implied volatility from live cattle options markets, and provide practical suggestions on how to adjust the bias and inefficiency that is found in this forecasting framework.