1. Analyzing Precision Ag Data
August, 2002
…a mini-workshop on instructional materials for moving
precision agriculture beyond mapping
AgKnowledge GIS Faculty Development Workshop
August 12-15, 2002
Kirkwood Community College, Cedar Rapids, Iowa
Joseph K. Berry
Berry & Associates
2000 South College, Suite 300
Fort Collins, CO 80525
Web Site:
© 2002, Joseph K. Berry—permission to copy granted

2. What Is Precision Agriculture? (new technology)
…about doing the right thing at the right place and time
…identifies and responds appropriately to the variability within a field
…augments (not replaces) indigenous knowledge
(Circa 1992)
…stackable livestock
(Berry)

3. What Is Precision Agriculture? (Whole-field)
Whole-Field Management is based on broad averages of field data with management actions directed by “typical” conditions.
Whole-field assumes the “average” conditions are the same everywhere
Weigh-wagon, or grain elevator measurements, established a field’s yield performance. Soil sampling determined the typical nutrient levels within a field. From these and other data the best overall seed variety was chosen and a constant rate of fertilizer applied, as well as a bushel of other decisions— treating the entire field as uniform within its boundaries.
(Berry)

4. What Is Precision Agriculture? (Site-specific)
Site-Specific Management recognizes the variability within a field and changes management actions throughout a field. Z1 Z3 Z2
Management-zones breaks the field into areas of similar conditions
Surface-maps breaks the field into consistent pieces that track the specific conditions at each location
It involves assessing and reacting to field variability by tailoring management actions, such as fertilization levels, seeding rates and variety selection, to match changing field conditions. It assumes that managing field variability leads to both cost savings and production increases, as well as improved stewardship and environmental benefits.
(Berry)

5. What Is Precision Agriculture? (Technologies)
A new application of existing technologies to assist in managing field variability…
Global Positioning System (GPS) – Establishes position in a field
Data Collection Devices (IDI- monitors) – Collects data “on-the-fly”
Geographic Information Systems (GIS) – Used for data visualization and analysis
Intelligent Implements (IDI- controls) – Provides variable rate control “on-the-fly”
(Berry)

6. What Is Precision Agriculture? (Processing Steps)
Analyzing Precision Ag data
August, 2002
Utilizes spatial relationships in a field for site-specific management…
Continuous Data Logging 1) Yield Map
“What you see is what you get”
Discrete Point Sampling 2) Condition Maps
“Guessing over the whole field”
Mapped Data Analysis 3) Relationships
“Now what could’ve caused that?”
Spatial Modeling 4) Prescription Map
“Do this here, but not over there”
Where is What
Why and So What
Do What
and Where
(Berry)
© 2002, Joseph K. Berry—permission to copy granted

7. Precision Agriculture’s Big Picture
Enabling technologies
A new application of the Spatial Technologies…
Where is What
Why and So What
Do What and Where
…that utilizes spatial relationships in a field for site-specific management of fields
Data processing approach
(Berry)

8. Overview of the Case Study
Workshop objectives are to ) introduce the concepts, procedures and issues surrounding precision agriculture data and
2) provide hands-on experience
in analysis of these data
Fall 2002
Example Applications– several annotated examples of grid-based map analysis
Install MapCalc– software system for hands-on experience
Ex#1
(Berry)

9. Map Data Visualization and Summary
Table 1. Workbook Topics
Overview of the Case Study
Mapped Data Visualization and Summary
Comparing Mapped Data
Spatial Interpolation
Characterizing Data Groups
Developing Predictive Models
Analyzing Spatial Context
(Berry)

10. Map Data Visualization and Summary
Numerical statistics …min, max, range, mean, median standard deviation, variance
Geographic statistics …total area, area by class, drill-down
Ex#2
Descriptive statistics
Drill-Down
(Berry)

11. Comparing Map Data Table 1. Workbook Topics Overview of the Case Study
Mapped Data Visualization and Summary
Comparing Mapped Data
Spatial Interpolation
Characterizing Data Groups
Developing Predictive Models
Analyzing Spatial Context
(Berry)

12. Comparing Discrete Maps (Joint coincidence)
What differences do you see? –
“How different are the maps?”
“How are they different?”
“Where are they different?”
…but map patterns can be quantitatively compared
A Coincidence Table reports the number of cells for each joint condition with diagonal cells identifying agreement.
(Berry)

13. Comparing Map Surfaces (Difference map)
1997_Yield_Volume
- 1998_Yield_Volume
Yield_Diff
Map Variables… map values within an analysis grid can be mathematically and statistically analyzed
Ex#3
…green indicates areas of increased production
…yellow indicates minimal change
…red indicates decreased production
(Berry)

14. Spatial Interpolation
Table 1. Workbook Topics
Overview of the Case Study
Mapped Data Visualization and Summary
Comparing Mapped Data
Spatial Interpolation
Characterizing Data Groups
Developing Predictive Models
Analyzing Spatial Context
(Berry)

15. Spatial Expression of Arithmetic Average
“Mapping the Variance”
…Soil Samples are collected with GPS coordinates
…the location and nutrient levels of the sample points (Discrete Data) are used to estimate the nutrient pattern throughout the field (Continuous Data)
(Berry)

16. Spatial Interpolation (Mapping spatial variability)
Ex#4
…the geo-registered
soil samples form a pattern of “spikes” throughout the field. Spatial Interpolation is similar to throwing a blanket over the spikes that conforms to the pattern.
…all interpolation algorithms assume that 1) “nearby things are more alike than distant things” (spatial autocorrelation),
2) appropriate sampling intensity, and
3) suitable sampling pattern.
…the continuous surfaces produced “map the spatial variation in the data samples.
(Berry)

17. Spatial Interpolation (Average vs. IDW)
Comparison of the interpolated surface to the whole field average shows large differences in localized estimates
Difference Map
(Berry)

18. Spatial Interpolation (Compare maps)
Comparison of the IDW and Krig interpolated surfaces shows small differences in in localized estimates
Difference Map
(Berry)

19. Spatial Interpolation Techniques
Characterizes the spatial distribution by fitting a mathematical equation to localized portions of the data (roving window)
(Berry)

20. Spatial Interpolation
Assessing Interpolation Results (Residual Analysis)
(Berry)
…the best map is the
one that has the best
“guesses”

21. Spatial Interpolation
A Map of Error (Residual Map)
…shows you where your estimates are likely good/bad
(Berry)

22. Characterizing Data Groups
Table 1. Workbook Topics
Overview of the Case Study
Mapped Data Visualization and Summary
Comparing Mapped Data
Spatial Interpolation
Characterizing Data Groups
Developing Predictive Models
Analyzing Spatial Context
(Berry)

23. Visualizing Spatial Relationships
Interpolated Spatial Distribution
Phosphorous (P)
What spatial relationships do you see?
…do relatively high levels of P often occur with high levels of K and N?
…how often?
…where?
(Berry)

24. Calculating Data Distance
…an n-dimensional plot depicts the multivariate distribution; the distance between points determines the relative similarity in data patterns
…the closest floating ball is the least similar (largest data distance) from the comparison point
(Berry)

25. Identifying Map Similarity
…the relative data distance between the comparison point’s data pattern and those of all other map locations form a Similarity Index
The green tones indicate field locations with fairly similar P, K and N levels; red tones indicate dissimilar areas.
(Berry)

26. Clustering Maps for Data Zones
Ex#5
…groups of “floating balls” in data space identify locations in the field with similar data patterns– data zones
…a map stack is a spatially organized set of numbers
(Cyber-Farmer, Circa 1992)
Variable Rate Application
…fertilization rates vary for the different clusters “on-the-fly”
(Berry)

27. Evaluating Clustering Results
…if the boxes do not overlap (much), the clusters are distinct.
(Berry)

28. Developing Predictive Models
Table 1. Workbook Topics
Overview of the Case Study
Mapped Data Visualization and Summary
Comparing Mapped Data
Spatial Interpolation
Characterizing Data Groups
Developing Predictive Models
Analyzing Spatial Context
(Berry)

29. RS Imagery as GIS Data Layers “Map-ematical Processing”
A RS image is just a “shishkebab
of numbers” like any other
grid map (raster)
Image 52
NIR (R)
148 46
Red (G) 26
(Beyond our sight) Color Infrared 34 44
Green (B)
Remote sensing images are composed of numbers, just like any other map in a grid-based GIS…
“Map-ematical Processing” P 57 43
312 K
257
7.5 ph
7.2
etc.
(Berry)

30. Creating Prediction Models (Scatter Plot)
…a Scatter Plot identifies the “joint condition” at each map location; the trend in the plot forms a prediction equation
Map Set New Graph Scatter Plot
(Berry)

31. Deriving a Predictive Index (NDVI)
…an index combining the Red and NIR maps can be used to generate a better predictive model
Normalized Difference Vegetation Index NDVI= ((NIR – Red) / (NIR + Red))
for the sample grid location NDVI= (( ) / ( ))= / 135.7= .783
(Berry)

32. Evaluating Prediction Maps (Spatial error analysis)
…the regression equation is evaluated and the predicted map is compared to the actual measurements to generate an error map
Error = Predicted - Actual
for the sample grid location Yest = 55 + (180 * .783) = 196 …error is 196 – 218 = 22 bu/ac
Error = Predicted - Actual
Note that the average error is 2.62 and 67% of the predictions are within +/- 20 bu/ac
Also, most of the error is concentrated along the edge of the field
(Berry)

33. Stratifying Maps for Better Predictions
Stratifying by Error Zones
Other ways to stratify mapped data—
1) Geographic Zones, such as proximity to the field edge; 2) Dependent Map Zones, such as areas of low, medium and high yield; 3) Data Zones, such as areas of similar soil nutrient levels; and 4) Correlated Map Zones, such as micro terrain features identifying small ridges and depressions.
The Error Zones map is used as a template to identify the NDVI and Yield values used to calculate three separate prediction equations.
A Composite Prediction map is created by applying the equations to the NDVI data respecting the template map zones.
(Berry)

34. Assessing Prediction Results
Whole Field
Prediction
Stratified
Actual
Yield
none
Error Map for
Stratified Prediction
80%
Error Map
(Berry)

35. The Precision Ag Process (Fertility example)
As a combine moves through a field 1) it uses GPS to check its location then 2) checks the yield at that location to 3) create a continuous map of the yield variation every few
feet. This map 4) is combined with soil, terrain and other
maps to derive a 5) “Prescription Map” that is used to
6) adjust fertilization levels every few feet in the field.
On-the-Fly
Yield Map
Steps 1)–3)
Prescription Map
Step 5)
Zone 1
Zone 3
Zone 2
Cyber-Farmer, Circa 1992
…you’ve come a long ways baby
Farm dB
Step 4)
Map Analysis
Variable Rate Application
Step 6)
(Berry)

36. Spatial Data Mining …making sense out of a map stack
Mapped data that exhibits high spatial dependency create strong prediction functions. As in traditional statistical analysis, spatial relationships can be used to predict outcomes
…the difference is that spatial statistics
predicts where responses will be high or low
…making sense out of a map stack
(Berry)

37. Analyzing Spatial Context
Table 1. Workbook Topics
Overview of the Case Study
Mapped Data Visualization and Summary
Comparing Mapped Data
Spatial Interpolation
Characterizing Data Groups
Developing Predictive Models
Analyzing Spatial Context
(Berry)

38. Micro Terrain Analysis (Deriving Slope and Flow)
Characterizing Slope
A digital terrain surface is formed by assigning an elevation value to each cell in an analysis grid. The “slant” of the terrain at any location can be calculated— inclination of a plane fitted to the elevation values of the immediate vicinity
Slope and Flow maps draped over vertically exaggerated terrain surface
Characterizing Surface Flow
A map of surface flow is simulated by aggregating the “steepest downhill paths” from each cell— confluence
(Berry)

39. Micro Terrain Analysis (Slope and Flow Classes)
Calibrating Slope and Flow Classes: Areas of Gentle, Moderate, and Steep slopes are identified; areas of light, moderate and heavy flows are identified
(Berry)

40. Micro Terrain Analysis (a simple erosion model)
Determining Erosion Potential: The slope and flow classes are combined into a single map identifying erosion potential
(Berry)

41. Analyzing Precision Ag data
August, 2002
Map Analysis Macros (Fat buttons)
Assembly language
Programming Languages (Visual C++)
Programming Objects (ActiveX Controls)
Ex#6
Input
Parameter Specification
Output
General Software System
Application Languages (MapCalc scripting)
Fat Buttons (Embedded Macros)
Application-Specific System
Execution Environment (Visual Basic)
Fat Buttons (Embedded Controls)
(Berry)
© 2002, Joseph K. Berry—permission to copy granted

42. Analyzing Precision Ag data
Gaps in Our Thinking
August, 2002
Limited Approach – Mapping vs. Data Analysis; Tools vs. Science
Science Link – “Scientific Method” Doctrine, The “Random” Thing, Appropriate Driving Variables, Correlation vs. Causation
Market Confusion – Empirical Verification, Economic Validation, Rationalization (Productivity vs. Stewardship)
…Environmental Trump Card
…Education/Training is Key
Education, Enlightenment, Economics, Environment
(Berry)
© 2002, Joseph K. Berry—permission to copy granted

43. Analyzing Precision Ag Data
August, 2002
…a mini-workshop on instructional materials for moving precision agriculture beyond mapping
Presented by Joseph K. Berry
"Precision farming isn't just a bunch of pretty maps, but mapped data and a set of procedures linking these data to appropriate management actions."  
See
…to access the online Precision Farming Primer
See
…to access the online Map Analysis book
Berry & Associates // Spatial Information Systems
2000 South College, Suite 300, Fort Collins, CO 80525
Web Site:
© 2002, Joseph K. Berry—permission to copy granted