We pursue two distinct approaches to measuring the stability and validity of conventional approaches to measuring acreage response. Our focus is on corn and soybeans, which have perhaps been the most significantly impacted of the main commodities by market and policy changes. As noted, much of the change impacting commodity markets has been triggered by bio–energy policies, with ethanol from corn being the most prominent renewable fuel targeted by these policies. These policies have included ethanol tariffs and tax–credits for gasoline blenders. We first consider the structural stability of a standard acreage response model of the form often estimated in the empirical literature (see, for example, the seminal paper of Chavas and Holt (1990)). We apply structural change tests capable of identifying structural changes occurring at unknown break points and at the ends of a data series. The latter approach to testing is especially important in this application since the most substantial changes in markets have occurred since the 2007 Energy Independence Act. We then consider an analogous empirical evaluation of acreage response using panel data made up of annual observations taken at the crop reporting district (CRD) in the major corn producing states (i.e., the Corn Belt). We apply the newly developed inferential technique suggested by Cameron, Gelbach, and Miller (2011) that permits one to account for multi–dimensional clustering in panel data. Implications for modeling acreage response under changing market and policy conditions are discussed.