Fall Seminar Series - September 18, 2003
University of Minnesota
School of Statistics
College of Liberal
Arts
Modelling Replicated Weed Growth Data Using
Spatially-Varying Growth Curves
Sudipto Banerjee
Division of Biostatistics
University of Minnesota
Thursday, September 18, 2003
4:00 PM, 115
Ford Hall
Minneapolis, East Bank Campus
Social at 3:30 PM, 300
Ford Hall
Abstract
Weed growth in agricultural fields constitutes a major deterrent to the
growth of crops, often resulting in low productivity and huge losses for
the farmers. Therefore, proper understanding of patterns in weed growth is
vital to agricultural research. Recent advances in Geographical Information
Systems (GIS) now allow geocoding of agricultural data, which enable more
sophisticated spatial analysis. Our current application concerns the development
of statistical models for conducting spatial analysis of growth patterns
in weeds. Our data comes from an agricultural experiment conducted in ten
locations over a sixteen hectare field in Waseca, Minnesota, that recorded
growth of the weed Setaria spp., which is known to be particularly detrimental
to the growth of crops. We capture the spatial variation in Setaria spp.
growth using spatially-varying growth curves. An added challenge is that
these designs are spatially replicated, with each plot being a lattice of
sub-plots. Therefore, spatial variation may exist at different resolutions
- a \emph{macro} level variation between the plots and \emph{micro} level
variation between the sub-plots nested within each plot. We develop a Bayesian
hierarchical framework for this setting. Flexible classes of models result
which are fitted using simulation-based methods.