Sparse Versus Dense Spatial Data R.L. (Bob) Nielsen Professor of Agronomy Purdue University West Lafayette, IN 47907-1150 Email: [email protected] Web:

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

Yield maps are believable …

Very little interpolation required to create yield map.

Data

Map

Soil sample data are sparse

Typical 2.5 acre sampling grid Only 0.4 point per acre

Organic matter surface map

Interpolated from o.m. values of 2.5 acre soil sample data

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!

Did you concentrate hard?

I believe your card is missing!