  Yield monitor datasets are known to contain a high percentage of unreliable
records. The current tool set is mostly limited to observation cleaning
procedures based on heuristic or empirically-motivated statistical rules for
extreme value identification and removal. We propose a constructive algorithm
for handling well-documented yield monitor data artifacts without resorting to
data deletion. The four-step Rectangle creation, Intersection assignment and
Tessellation, Apportioning, and Smoothing (RITAS) algorithm models sample
observations as overlapping, unequally-shaped, irregularly-sized, time-ordered,
areal spatial units to better replicate the nature of the destructive sampling
process. Positional data is used to create rectangular areal spatial units.
Time-ordered intersecting area tessellation and harvested mass apportioning
generate regularly-shaped and -sized polygons partitioning the entire harvested
area. Finally, smoothing via a Gaussian process is used to provide map users
with spatial-trend visualization. The intermediate steps as well as the
algorithm output are illustrated in maize and soybean grain yield maps for five
years of yield monitor data collected at a research agricultural site located
in the US Fish and Wildlife Service Neal Smith National Wildlife Refuge.
