Analyzing Precision Ag Data …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 Email: [email protected] Web Site: www.innovativegis.com/basis 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) 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) What Is Precision Agriculture? (Site-specific) Site-Specific Management recognizes the variability within a field and changes management actions throughout a field. Management-zones breaks the field into areas of similar conditions Z2 Z1 Z1 Z3 Z2 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) 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) What Is Precision Agriculture? (Processing Steps) 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” Where is What • Mapped Data Analysis 3) Relationships “Now what could’ve caused that?” • Spatial Modeling 4) Prescription Map “Do this here, but not over there” Why and So What Do What and Where (Berry) Precision Agriculture’s Big Picture Enabling technologies A new application of the Spatial Technologies… www.innovativegis.com/basis/pfprimer/ Where is What Why and So What …that utilizes spatial relationships in a field for sitespecific management of fields Do What and Where Data processing approach (Berry) 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 Fall 2002 in analysis of these data Example Applications– several annotated examples of grid-based map analysis Install MapCalc– software system for hands-on experience Ex#1 (Berry) 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) 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) 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) 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) 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) 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) 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) 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) Spatial Interpolation (Average vs. IDW) Comparison of the interpolated surface to the whole field average shows large differences in localized estimates Difference Map (Berry) Spatial Interpolation (Compare maps) Comparison of the IDW and Krig interpolated surfaces shows small differences in in localized estimates Difference Map (Berry) Spatial Interpolation Techniques Characterizes the spatial distribution by fitting a mathematical equation to localized portions of the data (roving window) (Berry) Spatial Interpolation Assessing Interpolation Results (Residual Analysis) …the best map is the one that has the best “guesses” (Berry) Spatial Interpolation A Map of Error (Residual Map) …shows you where your estimates are likely good/bad (Berry) 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) 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) 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) 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) Clustering Maps for Data Zones …a map stack is a spatially organized set of numbers …groups of “floating balls” in data space identify locations in the field with similar data patterns– data zones …fertilization rates vary for the different clusters “on-the-fly” Ex#5 (Cyber-Farmer, Circa 1992) Variable Rate Application (Berry) Evaluating Clustering Results …if the boxes do not overlap (much), the clusters are distinct. (Berry) 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) RS Imagery as GIS Data Layers A RS image is just a “shishkebab of numbers” like any other grid map (raster) Image 52 148 46 26 (Beyond our sight) Color Infrared 34 44 Remote sensing images are composed of numbers, just like any other map in a grid-based GIS… “Map-ematical Processing” 43 NIR (R) Red (G) Green (B) 57 P 312 K 257 7.5 7.2 ph etc. (Berry) 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) 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 for the sample grid location NDVI= ((NIR – Red) / (NIR + Red)) NDVI= ((121-14.7) / (121 + 14.7))= 106.3 / 135.7= .783 (Berry) 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) 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) Assessing Prediction Results Stratified Prediction Error Map Actual Yield Error Map for Stratified Prediction none 80% Whole Field Prediction none (Berry) 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 Steps 1)–3) 6) adjust fertilization levels every few feet in the field. Prescription Map Step 5) 45.00 On-the-Fly Yield Map 40.00 Map Analysis Step 4) Farm dB Step 6) 35.00 Cyber-Farmer, Circa 1992 …you’ve come a long ways baby Zone 3 30.00 25.00 Zone 2 20.00 15.00 10.00 5.00 Zone 1 5.00 10.00 Variable Rate Application 15.00 20.00 25.00 30.00 (Berry) 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 (Berry) 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) 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) 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) 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) 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) Gaps in Our Thinking • 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) Analyzing Precision Ag Data …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 http://www.innovativegis.com/basis/pfprimer/Default.html …to access the online Precision Farming Primer See http://www.innovativegis.com/basis/MapAnalysis/Default.html …to access the online Map Analysis book Berry & Associates // Spatial Information Systems 2000 South College, Suite 300, Fort Collins, CO 80525 Email: [email protected] Web Site: www.innovativegis.com/basis

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# Simplot_Introduction Presentation