The Forest Service's Forest Inventory and Analysis Program (FIA) provides information about the Nation's forests such as forest land area, tree growth, and tree mortality. FIA's estimates are based on a sample design that is unbiased and representative of all lands, including privately owned forest lands. When a large proportion of plots in a given area cannot be measured, for example when private landowners do not grant permission to access plots on their property, the area of forest land may be underestimated, often referred to as nonresponse bias. FIA's annual inventory in New Mexico served as an ideal test case for compensating for nonresponse bias. From 2008-2012, researchers sampled nearly 12,000 forest inventory plots and more than 1,000 of these were nonresponse plots. This study produced a stratification key, based on factors that affect nonresponse rates, to stratify in a way that compensates for nonresponse.Key findings include: (1) In New Mexico, the traditional FIA stratification underestimated forest land area by about one million acres compared to the stratification that compensates for nonresponse. (2) One unexpected consequence of accounting for high nonresponse is that strata with very low percentages of nonresponse, such as those with a majority of plots on National Forest lands, were assigned disproportionately high weights under the traditional stratification. Thus failure to address high nonresponse may lead to underestimation at the state level, as well as overestimation for specific areas with a low percentage of nonresponse. (3) Investigation of the impact of stratifying to compensate for nonresponse on tree-level attributes, such as growth and mortality, is ongoing. An accurate baseline of New Mexico's forests and future estimates now exists and can be compared against this baseline to detect changes over time. In other states where a large percentage of plots cannot be measured, the stratification key helps improve the accuracy of FIA's estimates by compensating for nonresponse bias. As a result, forest managers now have higher quality data.