** Visualizing Yield Data**:

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**Base Maps.** The

** Yield Map (2D Contour Display)**. The contour map shows seven levels (color
zones) of yield. In constructing the
map, a yield surface was generated using a grid with 66 rows by 64 columns
(cells 50 feet on a side). Yield data
was collected about every six-feet, therefore each
grid space has nearly ten sample points.

** Step 1.** Traditional, non-spatial statistics and plots
summarize a yield map…

A statistical summary shows that yield varies from 2.33bu to 295bu per acre. The average yield was 158bu with a fairly large variation (standard deviation= 41.3bu).

A histogram of the data shows that it is skewed. The mean (or average; blue line) would be in the middle of the curve with both sides equally balanced if the data were “normally distributed.” While most of the field produced above 155 bushels, a large portion produced well below this level.

**Step 2.** General statistics and a histogram (numeric
distribution) summarize the data without any regard to its geographic patterns
(spatial distribution). The objective of
yield mapping, on the other hand, is to effectively convey the spatial patterns
contained in the data. Using MapCalc’s “Shading Manager” tool…

…enables users to display a variety of contour maps.

*Equal** **Ranges*** of 7 intervals.** This display superimposes the 50-foot
analysis grid used to generate the map.
Note the positioning of the seven intervals in the histogram— each
interval is a constant step (41.8bu) along the data’s range. As a result, most of the yield data falls
into the 5

Changing the method used to calculate the intervals radically changes the appearance of the map…

** Equal Count of 7
intervals.** This display
uses the “equal count” method for defining the intervals. Each interval contains approximately the same
number of cells (about 470 of the 3,288 cells with yield values). Note the positioning of the seven intervals
in the histogram— large interval spread over data ranges with few map
occurrences; small interval steps for data ranges with many occurrences (around
histogram peak).

**Step 3.** An alternative way to view the yield data is
as a 3-D surface with color zones draped over it…

*Wireframe** Display of Yield Surface with Standard Deviation Intervals.*** **The accompanying display was
generated by pressing the “Toggle 3-D View” button on the main tool bar. It shows the color zones superimposed on a 3-D
surface map of the yield data. The plot
can be easily sized and rotated using a mouse.
Note the positioning of the seven intervals in the histogram— this time
each interval is based on the standard deviation of the data and centered on
the average value.

The “Use Cells” button on the main tool bar renders a different 3-D view…

** Extruded Grid of Yield Surface with Standard Deviation Intervals. **This view “pushes” each grid cell to
a level equaling its yield value. The
color indicates its contour interval.
Its height visually portrays its precise yield. Note that there is a lot of variation within
each of the intervals that cannot be shown in a contour map. The accompanying map shows the contour map in
3-D. Note that the actual variation
within each contour interval is lost—each interval is assumed to have the average
value (one color “plateau” for each).
For example, contour interval #1 ranges from 2.3 to 116.9bu, but is
“painted” one color implying that 59.6bu is everywhere within the contour
feature. In effect, the assumption pulls
up many grid cells within an interval from their field-measured values, but
pulls down others.

*Step 4.*** **While the underlying yield data does
not change, a wide variety of map displays can be generated. A MapCalc user can make several alternative
displays then chose the one they feel best shows the spatial patterns in the
data…

*Composite Display of Yield
Variability.*** ** This plot shows the histogram of the data, 2-D
Grid display, 3-D Extruded grid display and the assignment colors and
statistics for a 3 interval classification.

** Summary**. Visualizing yield data requires flexible and
comprehensive display tools. Simple 2-D
contour maps generalize the detailed data from in yield monitors. Within a contour interval, all of the information
on the spatial variation is lost. The
choice of breakpoints for the intervals can draw radically different maps. Surface maps (Wireframe
and Extruded Grid) show all of the information and accurately portray spatial
patterns, but are unfamiliar and appear overly complex. By combining both 2-D and 3-D displays with
statistical tables and charts, the “best” presentation can be achieved.