Advances in the productivity with which food is produced around the world have been made possible through the intensive use of industrial inputs that have important environmental impacts. Like standard measures of macroeconomic performance, however, commonly used measures of agricultural efficiency and productivity account only for marketed commodities and inputs, but ignore the environmental effects of these production processes. A more complete analysis of trends in the sector's productivity requires the use of models that incorporate these environmental effects to provide better measures of the contributions of the sector from the social point of view. This paper compares the conceptual merits and empirical performance of alternative approaches that can be employed for this purpose: input distance functions, output distance functions, nonparametric methods, and index number approaches. Each of the methods has relative strengths and weaknesses. The methods are empirically illustrated using data from the Canadian pulp and paper industry.