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Sparse Versus Dense Spatial Data
R.L. (Bob) NielsenProfessor of AgronomyPurdue UniversityWest Lafayette, IN 47907-1150
Email: [email protected]: www.kingcorn.org
Spatial data & GIS
Spatial data are the fundamental components of agricultural GIS.
Growers hope to minimize or manage spatial yield variability in order to increase or maximize profitability.
The causes of yield variability must therefore be determined, which requires the acquisition of additional spatial data sets or ‘layers’ of information.
Spatial data sets can be ...
Dense Many data points
per acre e.g., grain yield
data sets often consist of 300 to 600 data points per acre
Sparse Fewer data points
per acre e.g., typical grid
soil sampling results in an average of 0.4 data point per acre
GIS software …
Interpolates or fills in the spatial 'holes' in the data to create pretty color maps that mysteriously become the essence of truth for believers. Dense data sets have fewer 'holes'
per acre than do sparse Thus, less interpolation is required Thus, the resulting map is intuitively
more believable
Yield data are dense …
One sec. readings at 3 mph equal to 1 data point every 4.4 ft 600 data points per acre
with a 6-row combine header
Realitycheck
Soil surface color from reclassified aerial IR
Soil o.m. surface map interpolated from 2.5-acre samples
Mediocre correlation
Half-acre soil sampling More intense sampling
Five times as many data points as before Still sparse relative to aerial imagery
Realitycheck
Soil surface color from reclassified aerial IR
Soil o.m. surface map interpolated from half-acre samples
Improved correlation
2.5 ac soil O.M. map
Consequence of sparse sampling
Aerial image, reclassified
Poor interpolation of spatial variability
half-ac soil O.M. map
The challenge …
In order to interpret yield maps wisely, you will need far more data layers than just soil nutrient levels and soil types. Many factors influence yield! Acquiring these data will require
forethought, time, timeliness, attention to detail, and (of course) money!
The good news
Some of the additional data sets you will acquire will be dense and, therefore, satisfactory for creating spatial maps
Topography Soil EC Aerial photography Satellite imagery
The bad news Some of the additional data sets you will
acquire will be sparse data sets, the maps from which must be taken with the proverbial ‘grain of salt’.
Soil nutrients Plant populations Stand uniformity Plant height Insect pressure Disease pressure Weed pressure Soil compaction
Bottom Line:
Data collected by field scouting, including soil nutrient sampling, are often too sparse for GIS programs to accurately interpolate spatial relationships Yet, more intensive data collection is
often cost- and time-prohibitive
Example: Plant Counts in Late Planted Soybean
Approx. 10 plant population checks per acre on a fairly equal grid basis 292 total data
points on 30 acres
Cost: Three hikers, two GPS units, one day
Directed sampling
Added another 80 population checks on the fly as our eyeballs dictated 372 data points
Cost: Included in first day’s work
Revisited field, second day
GIS map did not agree completely with our eyeballs, so revisited field Added another 54
population checks Total of 426 data
points on 30 ac.
Cost: Three hikers, one GPS unit, one day
Soy population map Based on original grid samples
(10 per acre)
< 50k
50 to 100k
100 to 150k
150 to 200k
> 200k
Original data
Original data plus directed samples on the fly
Including revisit
Minor, but potentially useful improvements
Did add’nl sampling help?
Realitycheck
Our map of populations (17 June)
Green vegetation index (NDVI) from IR aerial image (8 July)
Not perfect, but acceptable
Recommendations
Sample as densely as time and money will allow. From the perspective of crop scouting
or monitoring, you can never have too much data!
Remember, you rarely have a visual idea of what the true spatial pattern is!
So, sometimes directed sampling is not feasible.
Recommendations
Sample in as much of an equidistant pattern as is logistically possible. Better for GIS software, easier on the
person in the field. Begin with a grid pattern, modify with
additional directed sampling as suggested by other data layers or your own eyes.
Thanks for your attention!
Farming is a gamble, so let’s practice ….
Pick a card and concentrate on it!
I will make your card disappear!