Agricultural Census ► Variables Available  Disturbance – Land Use Variables  Grain Crops  Row Crops & Vegetables  Farm Size

► There were two sampling frames for the agricultural census in 1969 and 1974  One for all farms and one just for those farms deemed to be commercial in nature (selling produce of $2500 or more)  A’s and C’s in the tables denote which universe applies. The green shading indicates the years for which data will have to be collected (outside of Konza and SGS).

► The naming conventions of the Great Plains project made use of underscores and a standard variable name length.  An underscore _A at the end of a variable name indicates that the variable is measured in acres  An underscore _Q tells you that the variable represents a count.  An underscore _V at the end of the variable name to denote an amount in dollars.

► List of variables for the study sites focuses on land use information.  The total proportion of land in agriculture can best be tracked by a combination of improved land in farms (the best approximation of total cropland in the late nineteenth century)  combinations of cropland and pasture in the early twentieth century  then total cropland (CRP_XX_A) beginning in 1945.

► Thinking about outcomes of interest and the drivers in the dataset that help explain spatial patterns  Why does farmland shift to western edge of Konza? ► Adding data from supplementary datasets  Weather data from VEMAP modeling of instrumental weather records fitted to county boundaries  STATSGO soils data fitted to county boundaries with levels of sand, silt, clay and depth of A layer.

VEMAP ► Modeled from instrumental record, summarized to county boundaries ►

STATSGO ► Sand, silt, clay, depth of A-layer ► ts/statsgo/index.html

► Nesting lower level, individual level, repeated measures data in the county-level data, like the wildlife data from TNC ► Using the county-level data longitudinally. Treating counties as time-varying individual level units, nested in contextual predictors that reflect time-invariant, between unit, differences

► What questions of interest should we pursue with the sample data sets? ► What outcome would you like to model?  Let’s explore the data series  How complete is the information to attack our question?