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While many "alien" plant and animal species are purposefully introduced into new areas as ornamentals, livestock, crops, and even pets, these species can escape into other areas and threaten agricultural and native ecosystems causing economic and environmental harm, or harm to human health. Increasingly, scientists are using Geographic Information Systems (GIS) to track and manage the invaders, mitigate the potential rate of spread and level of impact, and protect the native economy and ecosystem.





Beginning with an introduction to the use of GIS technology to capture, store, analyze, manage, and present data, GIS Applications in Agriculture, Volume Three: Invasive Species examines five relevant categories of geographic information including dispersal and transport, prediction and forecasting, mapping of current infestations, maps for management and control tactics, and impact assessment and method of control. It address GIS for studying the population ecology of a new species, niche requirements for species success, and the monitoring and control of several different species including Australian examples of intentionally introduced invasive species, insects and other animals that may also vector a disease, and invasive weed management from prediction to management.





Chapters cover maps and imageries available on various Web sites and provide step-by-step tutorials or case studies that allow manipulation of datasets featured on the accompanyingdownloadable resources to make maps, perform statistical analyses, and predict future problems. It offers hands-on experience with a variety of software programs that create interactive queries (user-created searches), analyze spatial information, edit data and maps, and present the results of these operations in several different formats. Some of the programs are freeware, others are not, but each can be used to integrate, edit, share, and display geographic information. Color figures are
Series Preface ix
Preface xi
Acknowledgments xiii
Editor xv
Contributors xvii
Chapter 1 Introduction: Remote Sensing and GIS Techniques for the Detection, Surveillance, and Management of Invasive Species
1(8)
Kevin Dalsted
1.1 Executive Summary
1(1)
1.2 Introduction
2(5)
1.2.1 Remote Sensing
2(2)
1.2.2 Geographic Information Systems
4(3)
1.2.3 Data Synergy
7(1)
1.3 Conclusions
7(1)
Acknowledgments
8(1)
References
8(1)
Chapter 2 Obtaining Spatial Data
9(20)
Mary O'Neill
Kevin Dalsted
2.1 Executive Summary
9(1)
2.2 Definitions
9(3)
2.3 Factors to Consider When Acquiring and Using Spatial Data
12(1)
2.4 Data Types: Raster and Vector
13(1)
2.5 Raster Data Sources and Examples
13(12)
2.5.1 Digital Raster Graphic
13(4)
2.5.2 Satellite and Aerial Imagery
17(6)
2.5.3 Digital Elevation Data
23(2)
2.6 Vector Data Sources and Examples
25(1)
2.7 Software for Spatial Data Visualization and Analysis
26(1)
2.8 Conclusion
27(1)
Acknowledgments
27(1)
References
27(2)
Chapter 3 Population Ecology Considerations for Monitoring and Managing Biological Invasions
29(30)
Patrick C. Tobin
Laura M. Blackburn
Shelby J. Fleischer
E. Anderson Roberts
3.1 Executive Summary
30(1)
3.2 Introduction
30(1)
3.3 Arrival
31(3)
3.3.1 Invasion Pathways
32(1)
3.3.2 Monitoring the Arrival of Biological Invaders
33(1)
3.4 Establishment
34(3)
3.4.1 Factors That Influence Establishment Success
34(1)
3.4.2 Monitoring the Establishment of Nonnative Species: Space-Time Population Persistence
35(2)
3.5 Spread
37(3)
3.5.1 Types of Spread
37(1)
3.5.2 Estimating Invasive Species Spread
38(2)
3.6 Managing Biological Invasions
40(2)
3.7 GIS Tutorial: Estimating Spread Rates of Nonnative Species
42(8)
3.7.1 Introduction
42(1)
3.7.2 Calculating Distance to an Initial Outbreak Location
43(1)
3.7.3 Performing OLS Regression Analysis
43(3)
3.7.4 Understanding Residuals
46(1)
3.7.5 Testing for Spatial Autocorrelation
46(1)
3.7.6 Calculating Temporal Spread Rates
47(2)
3.7.7 Calculating Regional Spread Rates
49(1)
3.8 Conclusions
50(1)
Questions
50(1)
Answers to Questions
50(1)
Acknowledgments
51(1)
References
51(8)
Chapter 4 Integrating GPS, GIS, and Remote Sensing Technologies with Disease Management Principles to Improve Plant Health
59(32)
Forrest W. Nutter, Jr.
Emmanuel Z. Byamukama
Rosalee A. Coelho-Netto
Sharon K. Eggenberger
Mark L. Gleason
Andrew Gougherty
Alison E. Robertson
Neil van Rij
4.1 Executive Summary
60(1)
4.2 Introduction
60(1)
4.3 Disease Management Principles
61(5)
4.3.1 Disease Management Principle 1: Exclusion
61(1)
4.3.1.1 Quarantine (y0)
62(1)
4.3.1.2 Seed/Plant Certification Programs (y0)
62(1)
4.3.2 Disease Management Principle 2: Avoidance (y0 and/or t)
62(1)
4.3.2.1 Avoidance of Disease Risk in Space (t)
62(1)
4.3.2.2 Avoidance of Disease Risk in Time (t)
62(1)
4.3.3 Disease Management Principle 3: Eradication (y0)
63(1)
4.3.3.1 Roguing of Diseased Plants (y0)
63(1)
4.3.3.2 Removal and Burial of Crop Residues/Debris (y0)
64(1)
4.3.3.3 Soil Fumigation (y0)
64(1)
4.3.4 Disease Management Principle 4: Protection (y0 and/or r)
64(1)
4.3.4.1 Use of Chemical Barriers to Protect Crops (y0 and r)
64(1)
4.3.5 Disease Management Principle 5: Host Resistance
65(1)
4.3.5.1 Resistance That Reduces Initial Inoculum (y0)
65(1)
4.3.6 Disease Management Principle 6: Therapy (y0 and Sometimes r)
65(1)
4.4 Case Study: Asian Soybean Rust
66(2)
4.5 Case Study: Ash Yellows Disease of Green Ash
68(3)
4.6 Case Study: Plum Pox Virus of Prunus spp
71(2)
4.7 Case Study: Moko Disease of Banana
73(3)
4.8 Case Study: Stewart's Disease of Corn
76(2)
4.9 Case Study: Gray Leaf Spot of Corn
78(1)
4.10 Case Study: Bean Pod Mottle Virus of Soybean
79(5)
4.11 GIS Tutorial: Moko Disease in Amazon Region of Brazil
84(3)
4.11.1 Saving
Chapter 4 Files to Your Computer
84(1)
4.11.2 Opening Data in ArcMap
84(1)
4.11.3 Changing Map Symbology
84(1)
4.11.4 Creating and Printing Map Layouts
85(2)
4.12 Conclusions
87(1)
Acknowledgments
88(1)
References
88(3)
Chapter 5 Mapping Actual and Predicted Distribution of Pest Animals and Weeds in Australia
91(38)
Peter West
Leanne Brown
Christopher Auricht
Quentin Hart
5.1 Executive Summary
92(1)
5.2 Introduction
93(2)
5.3 Information Needs
95(3)
5.4 Previous Mapping Initiatives
98(1)
5.5 Current Initiatives
99(1)
5.6 Predicting Invasive Species Distributions
100(3)
5.7 Methods
103(10)
5.7.1 Agreed Data Attributes and Standards
104(1)
5.7.2 Field Manuals for Monitoring
104(1)
5.7.3 Consistent Data Collection Methods/Protocol
104(1)
5.7.4 Collection, Collation, and Reporting of Information
105(1)
5.7.4.1 Geographic Information Systems Tool
105(2)
5.7.4.2 Stepwise Data Collection and Collation
107(3)
5.7.4.3 Data Consolidation
110(1)
5.7.4.4 Data Aggregation and Scaling-Up
110(1)
5.7.4.5 Climate/Habitat Matching Methods
111(1)
5.7.4.6 CLIMATE Software
112(1)
5.7.4.7 Land-Use Classifications
112(1)
5.8 Results
113(7)
5.8.1 Challenges for Large-Scale Mapping and Monitoring Efforts
113(1)
5.8.2 Outcomes of Australian Invasive Species Monitoring Efforts
114(1)
5.8.2.1 Reporting Single Attribute Data
114(1)
5.8.2.2 Multiple Attribute Maps
114(1)
5.8.2.3 Reporting Multiple Species Data
114(1)
5.8.2.4 Data Aggregation and Scaling-Up: Implications
114(1)
5.8.2.5 Reporting Predictive Model Outputs Using Habitat and Climate Suitability
114(2)
5.8.3 Limitations of Methods
116(1)
5.8.3.1 Data Collation and Reporting
116(2)
5.8.3.2 Climate/Habitat Matching: Potential Distribution Prediction Models
118(1)
5.8.3.3 Habitat Matching Using Land Use Data
119(1)
5.9 Conclusion
120(1)
5.9.1 Reporting at the National Level
120(1)
5.9.2 Way Forward for Invasive Species Monitoring and Reporting in Australia
121(1)
Acknowledgments
121(1)
Appendix 5.A
122(3)
5.A.1 Monitoring Protocol for Extent, Distribution, and Abundance of Invasive Species
122(1)
5.A.1.1 Step 1 Species Occurrence
122(1)
5.A.1.2 Step 2 Distribution: Spatial Pattern
122(1)
5.A.1.3 Step 3 Abundance: Relative Numbers
122(1)
5.A.1.4 Step 4 Trend
122(1)
5.A.1.5 Step 5 Data Quality
123(1)
5.A.2 Classes for the Occurrence, Distribution, and Density Attributes for Pest Animals and Weeds (Modified from Queensland Government's Pest Survey Group)
123(2)
References
125(4)
Chapter 6 Use of GIS Applications to Combat the Threat of Emerging Virulent Wheat Stem Rust Races
129(30)
David Hodson
Eddy DePauw
6.1 Executive Summary
130(1)
6.2 Introduction
130(1)
6.3 Significance of the Ug99 Lineage (What Is Special about Ug99?)
131(4)
6.3.1 Basic Biology of Ug99
131(1)
6.3.2 Dispersal
132(1)
6.3.3 Resistance Mechanisms and Virulence of Ug99
133(2)
6.4 GIS Applications and Ug99
135(8)
6.4.1 GIS-Based Surveillance and Monitoring Systems
135(1)
6.4.2 Where Is Ug99?---Known Distribution and Range Expansion of Stem Rust (Ug99 Lineage)
136(1)
6.4.3 Movements of Ug99
137(6)
6.5 Deposition/Colonization Factors
143(5)
6.5.1 Wheat Areas
144(1)
6.5.2 Susceptibility of Wheat Cultivars
145(1)
6.5.3 Crop Calendars/Crop Growth Stage
145(1)
6.5.4 Climate/Environment
146(2)
6.6 Information Tools
148(1)
6.6.1 RustMapper
148(1)
6.6.2 RustMapper Web
148(1)
6.7 Challenges/Future Activities
149(4)
6.8 Conclusion
153(1)
References
154(5)
Chapter 7 Online Aerobiology Process Model
159(8)
Joseph M. Russo
Scott A. Isard
7.1 Executive Summary
159(1)
7.2 Introduction
160(1)
7.3 Principles of an Aerobiology Process Model
160(2)
7.4 Configuration of the Aerobiology Process Model
162(1)
7.5 Online Simulation of the Aerobiology Process Model
163(3)
7.6 Conclusion
166(1)
Acknowledgment
166(1)
References
166(1)
Chapter 8 Site-Specific Management of Green Peach Aphid, Myzus persicae (Sulzer)
167(24)
Ian MacRae
Matthew Carroll
Min Zhu
8.1 Executive Summary
157(11)
8.2 Introduction
168(6)
8.3 Methods
174(12)
8.3.1 Exercise 1: Describe the Spatiotemporal Colonization Patterns of M. persicae in Seed Potato
174(1)
8.3.1.1 Description of Dataset
175(1)
8.3.1.2 Assessing Spatial Autocorrelation Using Semivariograms in GS+
176(3)
8.3.1.3 Plot the Data from the Dataset in ArcMap
179(3)
8.3.1.4 Discussion of Observed Colonization Patterns
182(1)
8.3.2 Exercise 2: Using the HYSPLIT Model to Determine If the Wind Vectors at Specific Dates Provided a Significant Risk of Aphid Immigration into the Red River Valley
183(1)
8.3.2.1 Using HYSPLIT to Examine LLJ to Facilitate Movement of Aphids into the Red River Valley
183(3)
8.3.2.2 Discussion of HYSPLIT Results
186(1)
8.4 Conclusions
186(1)
References
186(5)
Chapter 9 Analysis of the 2002 Equine West Nile Virus Outbreak in South Dakota Using GIS and Spatial Statistics
191(16)
Michael C. Wimberly
Erik Lindquist
Christine L. Wey
9.1 Executive Summary
192(1)
9.2 Introduction
192(1)
9.3 Methods
193(1)
9.3.1 Mapping WNv Cases in a GIS
193(1)
9.4 Results
194(7)
9.4.1 Smoothed Maps of Disease Risk
194(3)
9.4.2 Spatial Autocorrelation Analysis
197(1)
9.4.3 Spatiotemporal Clustering
198(3)
9.5 Summary and Conclusions
201(1)
9.6 Step-by-Step Exercise Using GeoDa 0.9.5 Software
202(3)
9.6.1 Opening a Shapefile in GeoDa
202(1)
9.6.2 Computing a Spatial Weights File
203(1)
9.6.3 Creating a Map of Raw Disease Rates
203(1)
9.6.4 Creating a Map of Disease Rates Using Empirical Bayes Smoothing
203(1)
9.6.5 Adding Calculated Rates to the Attribute Table
203(1)
9.6.6 Computing the Global Moran's I Index of Spatial Autocorrelation
204(1)
9.6.7 Computing the Local Moran's I Index of Spatial Autocorrelation
204(1)
References
205(2)
Chapter 10 Designing a Local-Scale Microsimulation of Lesser Grain Borer Population Dynamics and Movements
207(26)
J. M. Shawn Hutchinson
James F. Campbell
Michael D. Toews
Thomas J. Vought, Jr.
Sonny B. Ramaswamy
10.1 Executive Summary
208(1)
10.2 Introduction
208(4)
10.2.1 Lesser Grain Borer Economic Impact and Management
209(2)
10.2.2 Behavior and Ecology outside Grain Storage
211(1)
10.3 Geocomputation
212(2)
10.3.1 Agent-Based Simulation and Modeling
212(1)
10.3.2 About NetLogo
213(1)
10.4 Methods
214(14)
10.4.1 Overview: Creating a NetLogo Model
214(1)
10.4.2 Model Setup
215(1)
10.4.2.1 Setup and Go Buttons and Energy Switch
215(1)
10.4.2.2 Turtle Variables and Setup Procedure
216(2)
10.4.2.3 Defining Initial Variables with Sliders
218(1)
10.4.2.4 Go Procedure
219(1)
10.4.2.5 Bug Movement, Eating, Reproduction, and Death Procedures
220(2)
10.4.2.6 Setting Up Gain-from-Grain, Bug-Birth-Energy Procedures, and Control Sliders
222(1)
10.4.2.7 Forest Regrowth Procedure and Control Slider
222(1)
10.4.2.8 Show Energy and Display Labels
223(2)
10.4.3 Plot Procedure
225(1)
10.4.3.1 Create Plot Window and Monitors
226(2)
10.5 Conclusions
228(5)
Chapter 11 Geographic Information Systems in Corn Rootworm Management
233(22)
B. Wade French
Kurtis D. Reitsma
Amber A. Beckler
Laurence D. Chandler
Sharon A. Clay
11.1 Executive Summary
233(1)
11.2 Introduction
234(1)
11.3 Materials and Data Collection
235(1)
11.3.1 Field and Insect Trap Locations
235(1)
11.3.2 System Requirements
235(1)
11.4 Getting Started with ArcGIS™
236(4)
11.4.1 Importing Latitude-Longitude Trap Data
237(1)
11.4.2 Symbolizing Map Layers
238(2)
11.4.3 Coordinate Systems and ESRI® Shapefiles
240(1)
11.5 Analysis of Adult CRW Population and Distribution
240(11)
11.5.1 Spatial Autocorrelation, Moran's I
241(2)
11.5.2 Interpolation, Inverse Distance Weighting
243(2)
11.5.3 Comparing CRW Population with Soil Texture
245(6)
11.6 Conclusion
251(1)
Acknowledgments
252(1)
References
252(3)
Chapter 12 Improving Surveillance for Invasive Plants: A GIS Toolbox for Surveillance Decision Support
255(22)
Julian C. Fox
David Pullar
12.1 Executive Summary
256(1)
12.2 Introduction
256(2)
12.3 Methods
258(12)
12.3.1 Design Elements of the Toolbox
258(1)
12.3.2 Modeling Seed Dispersal
259(3)
12.3.3 Wind Dispersal Kernel
262(1)
12.3.3.1 Modeling the Influence of Wind Direction and Strength
262(1)
12.3.3.2 Modeling Terrain Influences Wind Dispersal
263(1)
12.3.3.3 Modeling Dispersal along Roads and Rivers
264(1)
12.3.3.4 Multiple Dispersal Events
265(1)
12.3.4 Modeling Life History
265(1)
12.3.5 Simulating Surveillance
266(1)
12.3.6 Parameterization for Chilean Needle Grass
267(1)
12.3.6.1 Potential Habitat for Invasion
267(1)
12.3.6.2 Dispersal Parameters
267(1)
12.3.6.3 Life History Parameters
268(1)
12.3.6.4 Evaluating Surveillance
269(1)
12.3.6.5 Evaluating Eradication
270(1)
12.4 Results and Discussion
270(4)
12.4.1 Evaluating Surveillance
270(3)
12.4.2 Evaluating Eradication
273(1)
12.4.3 Implications for Management of CNG
273(1)
12.5 Summary
274(1)
References
274(3)
Chapter 13 Tracking Invasive Weed Species in Rangeland Using Probability Functions to Identify Site-Specific Boundaries: A Case Study Using Yellow Starthistle (Centaurea solstitialis L.)
277(24)
Lawrence W. Lass
Timothy S. Prather
Bahman Shafii
William J. Price
13.1 Executive Summary
278(1)
13.2 Introduction
278(1)
13.2.1 Background
278(1)
13.3 Methods
279(6)
13.3.1 Model of Development
279(2)
13.3.2 Productivity Model Components
281(2)
13.3.3 Spatial Network Models
283(2)
13.4 Case Study
285(13)
13.4.1 IDRISI Software
285(1)
13.4.1.1 Additional Software Required for the Exercise
285(1)
13.4.2 Topographic Correlates of the Site (Slope, Aspect, and Sunlight Difference between Spring and Summer "Sundiff")
285(1)
13.4.2.1 Preliminary Steps
286(1)
13.4.2.2 Steps for Importing and Calculating Slope, Aspect, and Sun Angle Differencing
287(2)
13.4.3 Developing Vegetation Indices (NDVI and TSAVII) from Landsat Images
289(1)
13.4.3.1 Atmospheric Correction
289(3)
13.4.3.2 Georectification
292(1)
13.4.3.3 Vegetation Index
293(2)
13.4.4 Productivity Modeling with the Logit Regression Module
295(1)
13.4.4.1 Specifying a Sampling Scheme
296(1)
13.4.5 Network Modeling
296(2)
13.5 Conclusions
298(1)
References
298(3)
Chapter 14 Using GIS to Map and Manage Weeds in Field Crops
301(19)
Mary S. Gumz
Stephen C. Weller
14.1 Executive Summary
301(1)
14.2 Introduction
302(2)
14.3 Materials and Methods
304(10)
14.3.1 Uploading Images into ERDAS
306(1)
14.3.2 Unsupervised Image Classification and Accuracy Assessment
306(3)
14.3.3 Supervised Image Classification in ERDAS and Accuracy Assessment
309(5)
14.4 Results
314(2)
14.4.1 Crop Health Assessment
314(1)
14.4.2 Weed Detection by Supervised Classification
315(1)
14.4.3 On-Farm Use of GIS-Based Weed Mapping
315(1)
14.5 Conclusions
316(1)
Acknowledgments
316(1)
References
317(3)
Chapter 15 Adapting Geostatistics to Analyze Spatial and Temporal Trends in Weed Populations
319
Nathalie Colbach
Frank F. Forcella
15.1 Executive Summary
320(1)
15.2 Introduction
320(1)
15.3 Analysis Steps
321(1)
15.4 Data Collection
322(1)
15.4.1 Experimental Field
322(1)
15.4.2 Sampling Grid
322(1)
15.5 Exploratory Data Analysis
323(7)
15.5.1 Objective
323(1)
15.5.2 Method
324(1)
15.5.3 Results
325(5)
15.6 Data Transformation
330(1)
15.6.1 Objective
330(1)
15.6.2 Method
330(1)
15.6.3 Results
330(1)
15.7 Detrending Data
330(4)
15.7.1 Objective
330(1)
15.7.2 Median Polishing
330(1)
15.7.3 Estimating Trend with Linear Regressions
331(3)
15.8 Empirical Semivariograms
334(1)
15.8.1 Objective
334(1)
15.8.2 Method
335(1)
15.8.3 Results
335(1)
15.9 Semivariogram Model Fitting
335(11)
15.9.1 Objective
335(5)
15.9.2 Method
340(1)
15.9.3 Results
340(6)
15.10 Analysis of Variogram Parameters
346(8)
15.10.1 Objective
346(1)
15.10.2 Method
346(8)
15.10.3 Results
354(1)
15.11 Kriging
354(2)
15.11.1 Objective
354(2)
15.11.2 Method
356(1)
15.11.3 Results
356(1)
15.12 Cross-Semivariograms and Cokriging
356(7)
15.12.1 Objective
356(4)
15.12.2 Cross-Semivariograms
360(1)
15.12.3 Cokriging
360(3)
15.13 Error Analysis
363(6)
15.13.1 Prediction of Weed Means
363(1)
15.13.2 Prediction of Weed Locations
363(6)
15.14 Summary: Using Geostatistical Information for Decision Making
369(1)
Glossary
369(1)
References
370(3)
Chapter 16 Using GIS to Investigate Weed Shifts after Two Cycles of a Corn/Soybean Rotation
373(32)
Kurtis D. Reitsma
Sharon A. Clay
16.1 Executive Summary
374(1)
16.2 Introduction
374(1)
16.3 Materials and Methods
375(12)
16.3.1 Minimum Recommended System Requirements to Reproduce These Analyses
375(1)
16.3.2 Field Methods
375(1)
16.3.3 Analyses Method Overview
375(1)
16.3.4 Aggregating Data in MS Excel
376(1)
16.3.4.1 Aggregating Weed Densities by Year
376(1)
16.3.4.2 Weed Density and Species Change Calculations
377(1)
16.3.4.3 Data for Estimating Direction Distribution
378(2)
16.3.5 ArcMap™
380(1)
16.3.5.1 Creating Layers Using ArcMap™
380(3)
16.3.5.2 Creating Data Subsets
383(1)
16.3.5.3 Data Exploration in ArcMap™
384(3)
16.4 Results
387(15)
16.4.1 Spatial Data Exploration
387(5)
16.4.2 Creating Interpretive Maps
392(1)
16.4.2.1 Spatial Data Interpolation Using an Ordinary Kriging Method
393(4)
16.4.2.2 Spatial Data Interpolation Using an Inverse Distance Weighting Method
397(5)
16.5 Conclusions
402(1)
Acknowledgments
402(1)
References
403(2)
Chapter 17 Creating and Using Weed Maps for Site-Specific Management
405(14)
J. Anita Dille
Jeffrey W. Vogel
Tyler W. Rider
Robert E. Wolf
17.1 Executive Summary
405(1)
17.2 Introduction
406(2)
17.2.1 Obtaining Weed Spatial Distribution Information
406(1)
17.2.2 Determining Economic Optimal Herbicide Rate Based on Weed Spatial Distribution
406(2)
17.2.3 Developing the Prescription Map
408(1)
17.3 Materials and Methods
408(3)
17.3.1 General Procedures
408(1)
17.3.2 Development of a Prescription Map
409(2)
17.3.3 Collecting and Analyzing Data
411(1)
17.4 Results and Discussion
411(6)
17.4.1 Weed Species Composition and Herbicide Usage
411(2)
17.4.2 Yield Results
413(4)
17.5 Conclusion
417(1)
Acknowledgments
417(1)
References
417(2)
Index 419
The Editor:





Sharon A. Clay, PhD., is a professor of weed science at South Dakota State University where she has research and teaching responsibilities. She received a B.S. degree in Horticulture from the University of Wisconsin-Madison in 1977, an M.S. degree in Plant Science from the University of Idaho in 1983 examining barley variety sensitivity to various herbicides, and a Ph.D. in Agronomy from the University of Minnesota in 1987 where she examined weed management in wild rice production systems of northern Minnesota.

She has conducted weed management studies in range and cropping systems that include corn, soybean, wheat, barley, wild rice, flax, and sunflower, as well as studies in weed physiology and site specific weed management strategies. Dr. Clay has published over 100 scientific articles and has served on the editorial boards for Agronomy Journal, Weed Science, and Site-Specific Management Guidelines. Dr. Clay has served on numerous national committees and review panels and has active memberships in, and has served as president of the SD Chapters of the honorary societies Sigma Xi and Gamma Sigma Delta. She served as the chairperson of the Agricultural Systems division in the American Society of Agronomy, participated in numerous ASA committees, was elected to ASA Fellow in 2009, and has held several positions in the Weed Science Society of America.
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