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9 th International Conference on Precision Agriculture July 20-23, 2008 — Denver, Colorado So Where Is Precision Ag? …a brief history, current expression and future directions

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So where is precision ag a brief history current expression and future directions l.jpg

9th International Conference on Precision Agriculture

July 20-23, 2008 — Denver, Colorado

So Where Is Precision Ag? …a brief history, current expression and future directions

Joseph K. BerryW. M. Keck Visiting Scholar in Geosciences, Geography, University of DenverPrincipal, Berry & Associates // Spatial Information SystemsEmail jberry@innovativegis.com — Web www.innovativegis.com/basis/


What is precision agriculture l.jpg

Things to keep in mind—

PA is about doing the right thing at the

right place and at the right time

…it identifies and responds to the

variability within a field

…it augments indigenous knowledge

(not a replacement)

(PA has been around awhile, Circa 1992)

…it is a radically different technology

with extremely high expectations

What Is Precision Agriculture?

(Berry)


Slide3 l.jpg

8,000 years of mapping

Computer Mappingautomates the

cartographic process (70s)

Spatial Database Managementlinks

computer mapping techniques with traditional database capabilities (80s)

…CM + SDBM of the first two decades is often referred to asDesktop GIS

Mapping and Inventory — What is Where

Map Analysis — Why and So What

Map Analysisrepresentation of

relationships within and among mapped data (90s)

Toolbox supporting Precision Ag…focus of this presentation

Historical Setting and Evolution

Multimedia Mappingfull integration of GIS, GPS, RS, Internet and visualization technologies (00s)

Note: U.S. Dept. of Labor identifiesGeotechnology(GPS, GIS, RS)as one of three "mega technologies" for the 21st century and promises to change how we conceptualize, utilize and visualize spatial relationships in scientific research and commercial applications

(the other two are Biotechnology and Nanotechnology)

(Berry)


Slide4 l.jpg

  • Water

  • Weather

  • Topography

  • Nutrients

  • Weeds

  • Pests

  • Genetics

  • Seeding Rate

  • Other…

On-Farm Studies (Research?)

  • Candidate for Precision Agriculture andSite-specific Management

  • if and only if —

    • the factor is asignificantdriving variable

    • it hasmeasurablespatial variability

    • its variability can be explained andspatial relationships established

    • it exhibits aspatial responseto practical management actions

      …and results in production gains, increased profitability and/or improved stewardship

Yield Limiting Factors (the basis of PA)

(Berry)


Whole field vs site specific management l.jpg

Whole-fieldassumes the “average” conditions are the same everywhere within the field (uniform/homogenous)

Management action is the same throughout the field

Discrete Management Zones break the field into areas of similar conditions (zones)

Management action is the

same within each zone

Z2

Continuous Surfacesbreak the field into small consistent pieces (cells) that track specific

conditions at each location

Z1

Z1

Z3

Management action varies throughout the field

Z2

Whole Field vs. Site Specific Management

The bulk of agricultural research has been

“non-spatial”

…but PA is all about

spatial relationships/patterns

Research Opportunity

Is Smart Sampling really dumb?

(Berry)


Slide6 l.jpg

Shading

Manager

Click on…

Zoom Pan Rotate

Display

Analysis

Frame(Grid )

…each map layer is organized as a geo-registeredmatrix of numbers

:

--, --, --, --,

--, --, --, --,

--, --, --, --,

--, 149.0, --,

--, --, --, --,

:

Grid

Table

Map

Stack

Continuous regular grid cells (objects)

MAP Analysis Framework (Keystone Concept)

(Berry)


Slide7 l.jpg

Grid-Based Map Analysis (workshop topics)

  • Surface Modelingmaps the spatial distribution and pattern of point data…

  • Map Generalization— characterizes spatial trends (tilted plane)

  • Spatial Interpolation— deriving spatial distributions (e.g. IDW, Krig)

  • Other— roving windows and facets (e.g., density surface; tessellation)

  • Spatial Data Mininginvestigates the “numerical” relationships in mapped data…

  • Descriptive— aggregate statistics (e.g. average, stdev, similarity; clustering)

  • Predictive— relationships among maps (e.g., regression)

  • Prescription— appropriate actions (e.g., decision rules; optimization)

  • Spatial Analysisinvestigates the “contextual” relationships in mapped data…

  • Reclassify— reassigns map values (position, value, size, shape, contiguity)

  • Overlay— map coincidence (point-by-point; region-wide; map-wide)

  • Distance— proximity and connection (movement; optimal paths; visibility)

  • Neighbors— roving windows (slope; aspect; diversity; anomaly)

Surface Modeling Techniques

(Berry)


Slide8 l.jpg

Geographic Distribution(Mapping the Variance)

Numeric Distribution — Average, Standard Deviation

Continuous Surface —Geographic Distribution

The “iterative smoothing” process is similar to slapping a big chunk of modeler’s clay over the “data spikes,” then taking a knife and cutting away the excess to leave a continuous surface that encapsulates the

peaks and valleys implied in the original field samples

(Berry)


Slide9 l.jpg

Spatial Interpolation maps the geographic distribution inherent in the data

Corn Field Phosphorous (P)

Data “Spikes”

IDW Surface

Spatial Interpolation (soil nutrient levels)

(Berry)


Slide10 l.jpg

Comparison of the

IDWinterpolated surface

to the

whole field average

shows large differences

in localized estimates

(-16.6 to 80.4 ppm)

Comparison of the

IDW interpolated surface

to the

Krig interpolated surface shows small differences

in localized estimates

(-13.3 to 11.7 ppm)

Comparing Spatial Interpolation Results

(Berry)


Slide11 l.jpg

Grid-Based Map Analysis (workshop topics)

  • Surface Modelingmaps the spatial distribution and pattern of point data…

  • Map Generalization— characterizes spatial trends (tilted plane)

  • Spatial Interpolation— deriving spatial distributions (e.g. IDW, Krig)

  • Other— roving windows and facets (e.g., density surface; tessellation)

  • Spatial Data Mininginvestigates the “numerical” relationships in mapped data…

  • Descriptive— aggregate statistics (e.g. average, stdev, similarity; clustering)

  • Predictive— relationships among maps (e.g., regression)

  • Prescription— appropriate actions (e.g., decision rules; optimization)

  • Spatial Analysisinvestigates the “contextual” relationships in mapped data…

  • Reclassify— reassigns map values (position, value, size, shape, contiguity)

  • Overlay— map coincidence (point-by-point; region-wide; map-wide)

  • Distance— proximity and connection (movement; optimal paths; visibility)

  • Neighbors— roving windows (slope; aspect; diversity; anomaly)

Spatial Data Mining Techniques

(Berry)


Visualizing spatial relationships l.jpg

Interpolated Spatial Distribution

Phosphorous (P)

What spatial relationships do you see?

Visualizing Spatial Relationships

…do relatively high levels of P often occur with high levels of K and N?

…how often? …where?

HUMANS can “see” broad generalized patterns

in a single map variable

(Berry)


Clustering maps for data zones l.jpg

COMPUTERS can “see” detailed patterns in multiple map variables

Clustering Maps for Data Zones

…groups of “floating balls” in data space identify locations in the field with similar data patterns– data zones

(Berry)


Slide14 l.jpg

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 (dependent map variable).

Steps 1–3)

“As-applied” maps

On-the-Fly

Yield Map

Intelligent Implements

Prescription Map

Step 4)

Step 5)

Derived Nutrient Maps

Zone 3

Zone 2

The yield map 4) is analyzed in combination with soil, terrain and other maps (independent map variables) to derive a “Prescription Map” …

Variable Rate Application

Zone 1

5) …that is used to adjust fertilization levels every few feet in the field (action).

…more generally termed the Spatial Data Mining Process(e.g., Geo-Business application)

(Berry)


Slide15 l.jpg

Data Analysis Perspectives (Data vs. Geographic Space)

Traditional Analysis

Map Analysis

(Data Space — Non-spatial Statistics)

(Geographic Space — Spatial Statistics)

Field Data

Standard Normal Curve

fit to the data

Spatially Interpolated data

Central Tendency

Average = 22.0

StDev = 18.7

Typical

How Typical

Discrete

Spatial Object

(Generalized)

Continuous

Spatial Distribution

(Detailed)

22.0

28.2

Identifies the Central Tendency

Maps the Variance

(Berry)


So where are we in precision ag l.jpg

Yield Mapping…done deal for many crops

Soil Nutrient Mapping…procedures need validation

Mgt Zone Mapping…alternative approaches need study & validation

PA Nugget

So Where Are We in Precision Ag?

  • The Full Precision Farming Process…a fair piece to go

  • IF <condition> THEN <action>…based on spatial relationships

  • Description (Where is What) …coming on line

  • Prediction (Why and So What) …needs lots of work

  • Prescription (Do What Where)…barely on the research radar

  • Action (Precisely Here) …done deal for many farm inputs

(Berry)


Slide17 l.jpg

Grid-Based Map Analysis (workshop topics)

  • Surface Modelingmaps the spatial distribution and pattern of point data…

  • Map Generalization— characterizes spatial trends (tilted plane)

  • Spatial Interpolation— deriving spatial distributions (e.g. IDW, Krig)

  • Other— roving windows and facets (e.g., density surface; tessellation)

  • Spatial Data Mininginvestigates the “numerical” relationships in mapped data…

  • Descriptive— aggregate statistics (e.g. average, stdev, similarity; clustering)

  • Predictive— relationships among maps (e.g., regression)

  • Prescription— appropriate actions (e.g., decision rules; optimization)

  • Spatial Analysisinvestigates the “contextual” relationships in mapped data…

  • Reclassify— reassigns map values (position, value, size, shape, contiguity)

  • Overlay— map coincidence (point-by-point; region-wide; map-wide)

  • Distance— proximity and connection (movement; optimal paths; visibility)

  • Neighbors— roving windows (slope; aspect; diversity; anomaly)

Spatial Analysis Techniques

(Berry)


Slide18 l.jpg

Determining Erosion Potential: slope and flow classes are combined into a single map identifying erosion potential

times 10 plus

renumber

Field Elevationis formed by assigning an elevation value to each cell in an analysis grid

(1cm Lidar)

Micro Terrain Analysis(a simple erosion model)

(Berry)


Precision conservation compared to precision ag l.jpg

Precision Conservation

(Farm, Watershed,… Focus)

Precision Ag

(Individual Field Focus)

Wind Erosion

Chemicals

SoilErosion

Terrain

Runoff

Leaching

Soils

Leaching

Leaching

Yield

Potassium

3-dimensional

CIR Image

(Stewardship Focus)

(Production Focus)

2-dimensional

Interconnected Perspective

Isolated Perspective

Precision Conservation(compared to Precision Ag)

(Berry)


Slide20 l.jpg

Deriving Erosion Potential(regional scale)

Maps of surface flow confluence and slope are calculated by considering relative elevation differences throughout a project area

(Berry)


Slide21 l.jpg

Calculating Effective Distance(variable-width buffers)

Effective erosion buffers around a stream expand and contract depending on the erosion potential of the intervening terrain

(Berry)


Slide22 l.jpg

Revisit Analytics

(2020s)

Multimedia Mapping

(2000s)

2D Planar

(X,Y Data)

3D Solid

(X,Y,Z Data)

Revisit Geo-reference

(2010s)

Square

(4 sides)

Hexahedron

(6 squares)

Map Analysis(1990s)

Spatial dB Mgt(1980s)

Hexagon

(6 sides)

Dodecahedron

(12 pentagons)

Mapping focus

Computer Mapping

(1970s)

Data/Structure focus

Analysis focus

Cyclical Development(future directions)

Future Directions

Contemporary GIS

The Early Years


Slide23 l.jpg

Five critical questions underlying Precision Agriculture…

1)Is the “scientific method” relevant in the data-rich age of knowledge engineering?

Is GIS Technology Ahead of Science?

2)Is the “random thing” pertinent in deriving mapped data?

3)Are geographic distributions a natural extension of numerical distributions?

4)Can spatial dependencies within a map variable (spatial autocorrelation) and among map variables (spatial correlation) be modeled?

5)How can “site-specific” analysis and on-farm studies contribute to the scientific body of knowledge?

(Berry)


Slide24 l.jpg

www.innovativegis.com/basis/

Online References

Analyzing Precision Ag Data

…workbook with hands-on exercises

Textbook

Presentation handout

Workbook

Where To Go From Here…


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