1. Figure 2-1. Two different renderings (categorizations) of corn yield data. Analyzing Precision Ag Data – text figures © 2002, Joseph K. Berry—permission to copy granted

2. Figure 2-2. Statistical summary of yield data. © 2002, Joseph K. Berry—permission to copy granted Analyzing Precision Ag Data – text figures

3. Figure 2-3. A reference grid is used to directly link the map display and the stored data. © 2002, Joseph K. Berry—permission to copy granted Analyzing Precision Ag Data – text figures

4. Figure 2-4. Comparison of original and “goal normalized” data. © 2002, Joseph K. Berry—permission to copy granted Analyzing Precision Ag Data – text figures

5. Figure 2-5. The map values at each grid location form a single record in the exported table. © 2002, Joseph K. Berry—permission to copy granted Analyzing Precision Ag Data – text figures

6. Figure 2-6. Mapped data can be imported into standard statistical packages for further analysis. © 2002, Joseph K. Berry—permission to copy granted Analyzing Precision Ag Data – text figures

7. Figure 3-1. Discrete Yield Maps for Consecutive Years. © 2002, Joseph K. Berry—permission to copy granted Analyzing Precision Ag Data – text figures

8. Figure 3-2. Coincidence Map Identifying the Conditions for Both Years. © 2002, Joseph K. Berry—permission to copy granted Analyzing Precision Ag Data – text figures

9. Figure 3-3. Coincidence Summary. © 2002, Joseph K. Berry—permission to copy granted Analyzing Precision Ag Data – text figures

10. Figure 3-4. 3-D Views of Yield Surfaces for Consecutive Years. © 2002, Joseph K. Berry—permission to copy granted Analyzing Precision Ag Data – text figures

11. Figure 3-5. A Difference Surface Identifies the Change in Yield. © 2002, Joseph K. Berry—permission to copy granted Analyzing Precision Ag Data – text figures

12. Figure 3-6. A 2-D Map and Statistics Describe the Differences in Yield. © 2002, Joseph K. Berry—permission to copy granted Analyzing Precision Ag Data – text figures

13. Figure 4-1. Spatial interpolation involves fitting a continuous surface to sample points. © 2002, Joseph K. Berry—permission to copy granted Analyzing Precision Ag Data – text figures

14. Figure 4-2. A Wizard interface guides a user through the necessary steps for interpolating sample data. © 2002, Joseph K. Berry—permission to copy granted Analyzing Precision Ag Data – text figures

15. Figure 4-3. Interpolated Phosphorous, Potassium and Nitrogen surfaces. © 2002, Joseph K. Berry—permission to copy granted Analyzing Precision Ag Data – text figures

16. Figure 4-4. Variogram plot depicts the relationship between distance and measurement similarity (spatial autocorrelation). © 2002, Joseph K. Berry—permission to copy granted Analyzing Precision Ag Data – text figures

17. Figure 4-5. Spatial Comparison of a Whole-Field Average and an IDW Interpolated Map. © 2002, Joseph K. Berry—permission to copy granted Analyzing Precision Ag Data – text figures

18. Figure 4-6. Statistics summarizing the difference between the maps in figure 4-5. © 2002, Joseph K. Berry—permission to copy granted Analyzing Precision Ag Data – text figures

19. Figure 4-7. Spatial Comparison of IDW and Krig Interpolated Maps. © 2002, Joseph K. Berry—permission to copy granted Analyzing Precision Ag Data – text figures

20. Figure 4-8. Residual Analysis table identifying the relative performance of average, IDW and Krig estimates. © 2002, Joseph K. Berry—permission to copy granted Analyzing Precision Ag Data – text figures

21. Figure 5-1. Map surfaces identifying the spatial distribution of P,K and N throughout a field. © 2002, Joseph K. Berry—permission to copy granted Analyzing Precision Ag Data – text figures

22. Figure 5-2. Conceptually linking geographic space and data space. © 2002, Joseph K. Berry—permission to copy granted Analyzing Precision Ag Data – text figures

23. Figure 5-3. A similarity map identifying how related locations are to a given point. © 2002, Joseph K. Berry—permission to copy granted Analyzing Precision Ag Data – text figures

24. Figure 5-4. Identifying areas of unusually high measurements. © 2002, Joseph K. Berry—permission to copy granted Analyzing Precision Ag Data – text figures

25. Figure 5-5 Identifying joint coincidence in both data and geographic space. © 2002, Joseph K. Berry—permission to copy granted Analyzing Precision Ag Data – text figures

26. Figure 5-6. Level-slice classification using three map variables. © 2002, Joseph K. Berry—permission to copy granted

27. Figure 5-7. Examples of Map Clustering. © 2002, Joseph K. Berry—permission to copy granted Analyzing Precision Ag Data – text figures

28. Figure 5-8. Data patterns for map locations are depicted as floating balls in data space. © 2002, Joseph K. Berry—permission to copy granted

29. Figure 5-9. Clustering results can be roughly evaluated using basic statistics. © 2002, Joseph K. Berry—permission to copy granted Analyzing Precision Ag Data – text figures

30. Figure 6-1. The corn yield map (top) identifies the pattern to predict; the red and near-infrared maps (bottom) are used to build the spatial relationship. © 2002, Joseph K. Berry—permission to copy granted

31. Analyzing Precision Ag Data – text figures Figure 6-2. The joint conditions for the spectral response and corn yield maps are summarized in the scatter plots shown on the right. © 2002, Joseph K. Berry—permission to copy granted

32. Analyzing Precision Ag Data – text figures Figure 6-3. The red and NIR maps are combined for NDVI value that is a better predictor of yield. © 2002, Joseph K. Berry—permission to copy granted

33. Figure 6-4. A field can be stratified based on prediction errors. © 2002, Joseph K. Berry—permission to copy granted Analyzing Precision Ag Data – text figures

34. Figure 6-5. After stratification, prediction equations can be derived for each element. © 2002, Joseph K. Berry—permission to copy granted Analyzing Precision Ag Data – text figures

35. Figure 6-6. Stratified and whole-field predictions can be compared using statistical techniques. © 2002, Joseph K. Berry—permission to copy granted

36. Figure 7-1. A Distance tool can be used to buffer special features in the field. © 2002, Joseph K. Berry—permission to copy granted Analyzing Precision Ag Data – text figures

37. Figure 7-2. A region-wide summary generates statistics of just the buffered areas. © 2002, Joseph K. Berry—permission to copy granted Analyzing Precision Ag Data – text figures

38. Figure 7-3. Map of surface flow confluence. © 2002, Joseph K. Berry—permission to copy granted

39. Analyzing Precision Ag Data – text figures Figure 7-5. Calculation of slope considers the arrangement and magnitude of elevation differences within a roving window. © 2002, Joseph K. Berry—permission to copy granted

40. Analyzing Precision Ag Data – text figures Figure 7-6. Areas of Gentle, Moderate, and Steep slopes are (S_class)combined with areas of Light, Moderate and Heavy flows (F_class) into a single map (SF_combo) that is reclassified to identify areas of Little, Moderate, Lot erosion (Erosion_Potential). © 2002, Joseph K. Berry—permission to copy granted