1. Sparse Versus Dense Spatial Data R.L. (Bob) Nielsen Professor of Agronomy Purdue University West Lafayette, IN 47907-1150 Email: rnielsen@purdue.edu Web: www.kingcorn.org

2. Spatial data & GIS n Spatial data are the fundamental components of agricultural GIS. n Growers hope to minimize or manage spatial yield variability in order to increase or maximize profitability. n The causes of yield variability must therefore be determined, which requires the acquisition of additional spatial data sets or ‘layers’ of information.

3. Spatial data sets can be... n Dense u Many data points per acre u e.g., grain yield data sets often consist of 300 to 600 data points per acre n Sparse u Fewer data points per acre u e.g., typical grid soil sampling results in an average of 0.4 data point per acre

4. GIS software … n Interpolates or fills in the spatial 'holes' in the data to create pretty color maps that mysteriously become the essence of truth for believers. u Dense data sets have fewer 'holes' per acre than do sparse F Thus, less interpolation is required F Thus, the resulting map is intuitively more believable

5. Yield data are dense … n One sec. readings at 3 mph equal to 1 data point every 4.4 ft u 600 data points per acre with a 6-row combine header

6. Yield maps are believable … n Very little interpolation required to create yield map. Data Map

7. Soil sample data are sparse n Typical 2.5 acre sampling grid u Only 0.4 point per acre

8. Organic matter surface map n Interpolated from o.m. values of 2.5 acre soil sample data

9. Reality check n Soil surface color from reclassified aerial IR n Soil o.m. surface map interpolated from 2.5- acre samples Mediocre correlation

10. Half-acre soil sampling n More intense sampling u Five times as many data points as before u Still sparse relative to aerial imagery

11. Reality check n Soil surface color from reclassified aerial IR n Soil o.m. surface map interpolated from half- acre samples Improved correlation

12. 2.5 ac soil O.M. map Consequence of sparse sampling Aerial image, reclassified n Poor interpolation of spatial variability half-ac soil O.M. map

13. The challenge … n In order to interpret yield maps wisely, you will need far more data layers than just soil nutrient levels and soil types. u Many factors influence yield! u Acquiring these data will require forethought, time, timeliness, attention to detail, and (of course) money!

14. The good news n Some of the additional data sets you will acquire will be dense and, therefore, satisfactory for creating spatial maps è Topography è Soil EC è Aerial photography è Satellite imagery

15. The bad news n 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’. è Soil nutrients è Plant populations è Stand uniformity è Plant height è Insect pressure è Disease pressure è Weed pressure è Soil compaction

16. Bottom Line: n Data collected by field scouting, including soil nutrient sampling, are often too sparse for GIS programs to accurately interpolate spatial relationships u Yet, more intensive data collection is often cost- and time-prohibitive

17. Example: Plant Counts in Late Planted Soybean n Approx. 10 plant population checks per acre on a fairly equal grid basis u 292 total data points on 30 acres n Cost: Three hikers, two GPS units, one day

18. Directed sampling n Added another 80 population checks on the fly as our eyeballs dictated u 372 data points n Cost: Included in first day’s work

19. Revisited field, second day n GIS map did not agree completely with our eyeballs, so revisited field u Added another 54 population checks u Total of 426 data points on 30 ac. n Cost: Three hikers, one GPS unit, one day

20. Soy population map n Based on original grid samples (10 per acre) < 50k50 to 100k100 to 150k150 to 200k> 200k

21. Original data Original data plus directed samples on the fly Including revisit Minor, but potentially useful improvements Did add’nl sampling help?

22. Reality check n Our map of populations (17 June) n Green vegetation index (NDVI) from IR aerial image (8 July) Not perfect, but acceptable

23. Recommendations n Sample as densely as time and money will allow. u From the perspective of crop scouting or monitoring, you can never have too much data! u Remember, you rarely have a visual idea of what the true spatial pattern is! F So, sometimes directed sampling is not feasible.

24. Recommendations n Sample in as much of an equidistant pattern as is logistically possible. u Better for GIS software, easier on the person in the field. u Begin with a grid pattern, modify with additional directed sampling as suggested by other data layers or your own eyes.

25. Thanks for your attention! n Farming is a gamble, so let’s practice …. n Pick a card and concentrate on it! I will make your card disappear!

26. Did you concentrate hard? I believe your card is missing!