Agricultural Census

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Agricultural Census

• Variables Available
• Disturbance Land Use Variables
• Grain Crops
• Row Crops Vegetables
• Farm Size

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• 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)
• As and Cs 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).

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• 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.

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• 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.

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• 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.

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VEMAP

• Modeled from instrumental record, summarized to
  county boundaries
• www.cgd.ucar.edu/vemap

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STATSGO

• Sand, silt, clay, depth of A-layer
• www.ncgc.nrcs.usda.gov/branch/ssb/products/statsgo
  /index.html

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• 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

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