Advances in agronomy volume 98

V O LU M E

N I N E T Y

ADVANCES

IN

E I G H T

AGRONOMY


ADVANCES IN AGRONOMY
Advisory Board

PAUL M. BERTSCH

RONALD L. PHILLIPS

University of Kentucky

University of Minnesota

KATE M. SCOW

LARRY P. WILDING

University of California,
Davis

Texas A&M University

Emeritus Advisory Board Members

JOHN S. BOYER

KENNETH J. FREY

University of Delaware

Iowa State University

EUGENE J. KAMPRATH

MARTIN ALEXANDER

North Carolina State
University

Cornell University

Prepared in cooperation with the
American Society of Agronomy, Crop Science Society of America, and Soil
Science Society of America Book and Multimedia Publishing Committee
DAVID D. BALTENSPERGER, CHAIR
LISA K. AL-AMOODI

CRAIG A. ROBERTS

KENNETH A. BARBARICK

MARY C. SAVIN

HARI B. KRISHNAN

APRIL L. ULERY

SALLY D. LOGSDON


V O LU M E

N I N E T Y

ADVANCES

E I G H T

IN

AGRONOMY
EDITED BY

DONALD L. SPARKS
Department of Plant and Soil Sciences
University of Delaware
Newark, Delaware

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CONTENTS

Contributors
Preface

1. Advances in Precision Conservation

ix
xiii

1

Jorge A. Delgado and Joseph K. Berry
1. Introduction
2. Geospatial Technologies
3. Identifying Spatial Patterns and Relationships
4. Field Level Flows
5. Connection of Field with Off-Site Transport
6. Watershed Scale Considerations
7. Current Applications and Trends
8. Summary and Conclusions
References

2. Reaction and Transport of Arsenic in Soils: Equilibrium
and Kinetic Modeling

2
4
9
12
17
22
28
39
39

45

Hua Zhang and H. M. Selim
1. Introduction
2. Environmental Toxicity
3. Arsenic in Soils
4. Biogeochemistry
5. Transport in Soils
6. Modeling
7. Remediation of Contaminated Soils
8. Summary and a Look Ahead
References

3. Crop Residue Management for Lowland Rice-Based Cropping
Systems in Asia

46
47
48
52

73
81
101
104
105

117

Bijay-Singh, Y. H. Shan, S. E. Johnson-Beebout,
Yadvinder-Singh, and R. J. Buresh
1. Introduction
2. Criteria for Evaluating Crop Residue Management Options

118
121
v


vi

Contents

3.
4.
5.
6.

Type and Abundance of Crop Residues
Existing and Emerging Residue Management Options
Evaluation of Options with Residues Managed During a Rice Crop

Evaluation of Options with Residues Managed During
a Non-Flooded Crop
7. Crop Residue and Bioenergy Options
8. Summary
Acknowledgment
References

4. Sampling and Measurement of Ammonia at Animal Facilities

123
125
135
160
181
183
185
186

201

Ji-Qin Ni and Albert J. Heber
1. Introduction
2. A General View of Ammonia Determination
3. Ammonia Sampling
4. Ammonia Concentration Measurement
5. Measurement Methods and Devices
6. Ammonia Concentration Data
7. Summary and Conclusions
Acknowledgments
References

5. Will Stem Rust Destroy the World’s Wheat Crop?

203
205
206
221
225
243
255
257
257

271

Ravi P. Singh, David P. Hodson, Julio Huerta-Espino, Yue Jin,
Peter Njau, Ruth Wanyera, Sybil A. Herrera-Foessel, and Richard W. Ward
1.
2.
3.
4.
5.

Introduction
Stem Rust Disease, Pathogen, and Epidemiology
Breeding for Resistance
Race UG99 and Why it is a Potential Threat to Wheat Production
Breeding Strategies to Mitigate the Threat from UG99 and
Achieve a Long-Term Control of Stem Rust
6. Conclusion and Future Outlook

Acknowledgments
References

272
274
277
281
288
305
306
306

6. Genetic Improvement of Forage Species to Reduce the
Environmental Impact of Temperate Livestock Grazing Systems

311

M. T. Abberton, A. H. Marshall, M. W. Humphreys, J. H. Macduff,
R. P. Collins, and C. L. Marley
1. Introduction
2. Reducing Diffuse Nitrogenous Pollution of Watercourses

312
315


Contents

3. Reducing P Pollution of Watercourses
4. Reducing Emissions to Air

5. Improving Soil Quality and Reducing Flood Damage
6. Enhancing Persistency and Resilience
7. Enhancing C Sequestration in Grasslands
8. Future Prospects
Acknowledgments
References

7. Mutagenesis and High-Throughput Functional Genomics
in Cereal Crops: Current Status

vii

321
325
335
339
341
344
345
345

357

H. S. Balyan, N. Sreenivasulu, O. Riera-Lizarazu, P. Azhaguvel,
and S. F. Kianian
1. Introduction
2. Insertional Mutagenesis
3. Non-Transgenic TILLING, DEALING, and DeleteageneTM Approaches
4. Phenomics Platform for Screening Mutagenized Population
5. Outlook

Acknowledgments
References
Index

358
361
380
398
399
401
401
415


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CONTRIBUTORS

Numbers in parentheses indicate the pages on which the authors’ contributions begin.

M. T. Abberton (311)
Plant Breeding and Genetics Programme Institute of Grassland Environmental
Research, Plas Gogerddan, Aberystwyth, Ceredigion SY23 3EB, United Kingdom
P. Azhaguvel (357)
Texas A&M University Agricultural Research and Extension Center, 6500
Amarillo Blvd West, Amarillo, Texas 79106
H. S. Balyan (357)
Department of Genetics and Plant Breeding, Ch. Charan Singh University,
Meerut 250 004, India

Joseph K. Berry (1)
Berry and Associates, Spatial Information Systems, Fort Collins, Colorado 80525
Bijay-Singh (117)
Department of Soils, Punjab Agricultural University, Ludhiana 141 004, Punjab,
India
R. J. Buresh (117)
International Rice Research Institute, Los Ban˜os, Philippines
R. P. Collins (311)
Plant Breeding and Genetics Programme Institute of Grassland Environmental
Research, Plas Gogerddan, Aberystwyth, Ceredigion SY23 3EB, United Kingdom
Jorge A. Delgado (1)
USDA-ARS, Soil Plant Nutrient Research Unit, Fort Collins, Colorado 80526
Albert J. Heber (201)
Agricultural and Biological Engineering Department, Purdue University, West
Lafayette, Indiana 47907
Sybil A. Herrera-Foessel (271)
International Maize and Wheat Improvement Center (CIMMYT), 06600 Mexico,
DF, Mexico
David P. Hodson (271)
International Maize and Wheat Improvement Center (CIMMYT), 06600 Mexico,
DF, Mexico

ix


x

Contributors

Julio Huerta-Espino (271)

INIFAP-CEVAMEX, 56230 Chapingo, Mexico
M. W. Humphreys (311)
Plant Breeding and Genetics Programme Institute of Grassland Environmental
Research, Plas Gogerddan, Aberystwyth, Ceredigion SY23 3EB, United Kingdom
Yue Jin (271)
USDA-ARS, Cereal Disease Laboratory, St. Paul, Minnesota 55108
S. E. Johnson-Beebout (117)
International Rice Research Institute, Los Ban˜os, Philippines
S. F. Kianian (357)
Department of Plant Sciences, North Dakota State University, Fargo, North
Dakota 58105
J. H. Macduff (311)
Plant Breeding and Genetics Programme Institute of Grassland Environmental
Research, Plas Gogerddan, Aberystwyth, Ceredigion SY23 3EB, United Kingdom
C. L. Marley (311)
Plant Breeding and Genetics Programme Institute of Grassland Environmental
Research, Plas Gogerddan, Aberystwyth, Ceredigion SY23 3EB, United Kingdom
A. H. Marshall (311)
Plant Breeding and Genetics Programme Institute of Grassland Environmental
Research, Plas Gogerddan, Aberystwyth, Ceredigion SY23 3EB, United Kingdom
Ji-Qin Ni (201)
Agricultural and Biological Engineering Department, Purdue University, West
Lafayette, Indiana 47907
Peter Njau (271)
Kenya Agricultural Research Institute, Njoro Plant Breeding Research Center
(KARI-NPBRC), Njoro, Kenya
O. Riera-Lizarazu (357)
Department of Crop and Soil Science, Oregon State University, Corvallis,
Oregon 97331
H. M. Selim (45)

School of Plant, Environmental and Soil Sciences, Louisiana State University,
Baton Rouge, Louisiana 70803
Y. H. Shan (117)
College of Environmental Science and Engineering, Yangzhou University,
Yangzhou 225009, China


Contributors

xi

Ravi P. Singh (271)
International Maize and Wheat Improvement Center (CIMMYT), 06600 Mexico,
DF, Mexico
N. Sreenivasulu (357)
Leibniz Institute of Plant Genetics and Crop Plant Research, Corrensstrasse-03,
Gatersleben 06466, Germany
Ruth Wanyera (271)
Kenya Agricultural Research Institute, Njoro Plant Breeding Research Center
(KARI-NPBRC), Njoro, Kenya
Richard W. Ward (271)
International Maize and Wheat Improvement Center (CIMMYT), 06600 Mexico,
DF, Mexico
Yadvinder-Singh (117)
Department of Soils, Punjab Agricultural University, Ludhiana 141 004, Punjab,
India
Hua Zhang (45)
School of Plant, Environmental and Soil Sciences, Louisiana State University,
Baton Rouge, Louisiana 70803

This page intentionally left blank


PREFACE

Volume 98 contains seven comprehensive and timely reviews. Chapter 1
covers advances in precision conservation, including cutting-edge technologies and applications and trends. Chapter 2 deals with equilibrium and
kinetic modeling of arsenic reactions and transport in soils and includes
background material on the biogeochemistry of arsenic, a toxic element of
concern worldwide. Chapter 3 covers crop residue management for lowland rice-based cropping systems in Asia with discussions on existing and
emerging residue management options. Chapter 4 is a timely review on
sampling and measurement of ammonia at animal facilities, including measurement methods and advances in data collection. Chapter 5 is concerned
with stem rust and its effects on wheat production, including breeding
efforts and strategies for reducing its impact. Chapter 6 covers genetic
improvement of forage species with a goal of reducing the environmental
impact of temperate livestock grazing systems. Topics dealing with reducing
nitrogen and phosphorus pollution and air emissions are included. Chapter 7
is a comprehensive chapter on the current status of mutagenesis and
high-throughput functional genomics in cereal grains.
I am grateful for the authors’ outstanding reviews.
DONALD L. SPARKS
University of Delaware

xiii


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C H A P T E R

O N E

Advances in Precision Conservation
Jorge A. Delgado* and Joseph K. Berry†
Contents
2
4
9
12

1.
2.
3.
4.

Introduction
Geospatial Technologies
Identifying Spatial Patterns and Relationships
Field Level Flows
4.1. Variable erosion and transport (flows of gases,
nutrients, and water)
4.2. Precision conservation for management of flows
5. Connection of Field with Off-Site Transport
5.1. Variable flows from field to nonfarm areas
5.2. Precision conservation buffers and riparian zones
6. Watershed Scale Considerations
6.1. Variable hydrology
6.2. Models and tools

6.3. Precision conservation at a watershed scale
7. Current Applications and Trends
8. Summary and Conclusions
References

12
16
17
17
20
22
22
22
24
28
39
39

Population growth is expected to increase, and the world population is projected
to reach 10 billion by 2050, which decreases the per capita arable land. More
intensive agricultural production will have to meet the increasing food demands
for this increasing population, especially because of an increasing demand for
land area to be used for biofuels. These increases in intensive production
agriculture will have to be accomplished amid the expected environmental
changes attributed to Global Warming. During the next four decades, soil and
water conservation scientists will encounter some of their greatest challenges to
maintain sustainability of agricultural systems stressed by increasing food and
biofuels demands and Global Warming. We propose that Precision Conservation
will be needed to support parallel increases in soil and water conservation

*
{

USDA-ARS, Soil Plant Nutrient Research Unit, Fort Collins, Colorado 80526
Berry and Associates, Spatial Information Systems, Fort Collins, Colorado 80525

Advances in Agronomy, Volume 98
ISSN 0065-2113, DOI: 10.1016/S0065-2113(08)00201-0

#

2008 Elsevier Inc.
All rights reserved.

1


2

Jorge A. Delgado and Joseph K. Berry

practices that will contribute to sustainability of these very intensively-managed
systems while contributing to a parallel increase in conservation of natural areas.
The original definition of Precision Conservation is technologically based, requiring the integration of a set of spatial technologies such as global positioning
systems (GPS), remote sensing (RS), and geographic information systems (GIS)
and the ability to analyze spatial relationships within and among mapped data
according to three broad categories: surface modeling, spatial data mining, and
map analysis. In this paper, we are refining the definition as follows: Precision
Conservation is technologically based, requiring the integration of one or more
spatial technologies such as GPS, RS, and GIS and the ability to analyze spatial

relationships within and among mapped data according to three broad categories: surface modeling, spatial data mining, and map analysis. We propose
that Precision Conservation will be a key science that will contribute to the
sustainability of intensive agricultural systems by helping us to analyze spatial
and temporal relationships for a better understanding of agricultural and natural
systems. These technologies will help us to connect the flows across the landscape, better enabling us to evaluate how we can implement the best viable
management and conservation practices across intensive agricultural systems
and natural areas to improve soil and water conservation.

1. Introduction
Population growth is expected to increase, and the world population is
projected to reach 10 billion by 2050, which will decrease the per capita
arable land from 0.23 ha in 1995 to 0.14 ha by 2050 (Lal, 1995). More
intensive agricultural production will have to meet the increasing food
demands for this increasing population, especially because of an increasing
demand for land area to be used for biofuels. These increases in intensive
production agriculture will have to be accomplished amid the expected
environmental changes attributed to Global Warming. Scientists are
projecting future changes of weather patterns that include regions with
higher evapotranspiration rates, lower precipitation in some areas, and
higher precipitation in other areas, which may contribute to higher erosion
rates (Hatfield and Prueger, 2004; Lal, 1995, 2000; Nearing et al., 2004;
Pimentel et al., 1995). During the next four decades, soil and water conservation scientists will encounter some of their greatest challenges to maintain
sustainability of agricultural systems stressed by increasing food and biofuel
demands.
Several scientists have reported on the potential impacts of global population increase, increase in greenhouse gases, and potential effects of climate
change on soil and water quality and on soil erosion (Hatfield and Prueger,
2004; Lal, 1995, 2000; Nearing et al., 2004; Pimentel et al., 1995). There is a
concern that if precipitation patterns continue to change, certain future

Advances in Precision Conservation

3

scenarios may cause conservation practices such as crop residue, no-till, and
incorporation of manure to lose effectiveness very rapidly, resulting in dramatic increases in runoff, and higher impacts to soil and water quality (Hatfield
and Prueger, 2004). It is also estimated that for every 25.4 mm increase in
precipitation rate, erosibility increases by 1.7% (Nearing et al., 2004).
Nearing et al. (2004) reported that the relationship between increases in
rain, biomass production, and erosion is more complex. Although an
increase in rain could increase biomass production, a decrease in biomass
may also increase erosion rates. The more difficult area to evaluate was
effects of climate change on land use and erosion rates, yet they concluded
from their analysis that the average increase in erosibility will be 1.7% per
25.4 mm increase in precipitation. It is important to note that Meisinger and
Delgado (2002) reported an average 10–30% of total N inputs in cropping
systems are lost due to nitrate leaching. Thus, increases in precipitation and/or
more intensive storms could potentially contribute to higher nitrate leaching
rates as well. These assessments from Nearing et al. (2004) and Hatfield and
Prueger (2004) clearly show the continuing need for soil and water conservation scientists and practitioners to continue looking for alternatives for
managing future impacts to soil and water quality.
Scientists and conservation practitioners will have to work together with
farmers across all types of soils and weather to increase and sustain higher
production to meet the demands of the increasing population, while managing for potential changes in weather patterns. This cooperation will also be
necessary to develop cropping systems that produce enough to meet the
increasing food and biofuel demands while maximizing soil and water
conservation. The implementation of soil and water conservation will be
necessary for the sustainability of these intensive efforts to maximize agricultural production. New technologies will help us to increase yields per hectare
and these technologies will also be applied to understand and manage
agricultural systems and to connect the flows from agricultural systems to

natural areas in an effort to manage these regions for maximum yield and
agroenvironmental sustainability.
Precision Conservation was originally defined as a set of spatial technologies and procedures linked to mapped variables, which is used to implement
conservation management practices that take into account spatial and temporal variability across natural and agricultural systems (Berry et al., 2003).
Contrary to Precision Farming that was oriented to maximize yields in
agricultural fields, Precision Conservation connects farm fields, grasslands,
and range areas with the natural surrounding areas such as buffers, riparian
zones, forest, and water bodies (Fig. 1). The goal of Precision Conservation
is to use information about surface and underground flows to analyze the
systems in order to make the best viable decisions for application of management practices that contribute to conservation of agricultural, rangeland, and
natural areas.


4

Jorge A. Delgado and Joseph K. Berry

Precision conservation
Precision Ag
Wind erosion

Chemicals

Soil
erosion
Runoff
Leaching

Leaching

Terrain

Leaching

Soils
Yield
Potassium

3-dimensional
Flows
Cycles

Coincidence

CIR image

2-dimensional
Interconnected perspective

Isolated perspective

Figure 1 The site-specific approach can be expanded to a three-dimensional scale
approach that assesses inflows and outflows from fields to watershed and region scales.
(From Berry et al., 2003.)

Berry et al. (2003) acknowledged that there could be different degrees of
Precision Conservation such as the use of nondigital, non-GIS maps and the
use of survey methods that can help in the application of spatial conservation
practices. However, the original definition of Precision Conservation is
technologically based, requiring the integration of spatial technologies

such as global positioning systems (GPS), remote sensing (RS), and
geographic information systems (GIS) and the ability to analyze spatial
relationships within and among mapped data according to three broad
map analysis categories: spatial analysis, surface modeling, and spatial data
mining (Fig. 2). Since Berry et al. (2003), several other papers related to the
topic of Precision Conservation have been published describing how these
new technologies can be applied for maximizing Precision Conservation.

2. Geospatial Technologies
New GIS, GPS, RS, modeling, and computer program technologies
are rapidly increasing our capacity to analyze large sets of information in
space and time. Traditional statistics used for soil and water conservation
studies and assessment of best management practices were initially nonspatial and analyzed a data set by fitting a numerical distribution (e.g., standard


Surface modeling

Point samples are spatially interpolated
into a continuous surface

53.2 ppm

4.2 ppm

Field sample locations
Phosphorus
surface

Discrete data spikes

Min = 4.2
Max = 53.2
Avg = 13.4
SDev = 5.2

Spatial data mining
32c,62r

45c,18r

Map surfaces are
clustered to identify
data pattern groups

P
53.2

Relatively low responses in P, K, and N
Relatively high responses in P, K, and N

11.0

Cluster 2
Cluster 1

N

K
412.0

177.0

27.9

32.9

N
K
P
Geographic space

Data space

Clustered data
zones

Figure 2 Surface modeling is used to derive map surfaces that utilize spatial data mining techniques to investigate the numerical relationships
in mapped data.(From Berry et al., 2005.)


6

Jorge A. Delgado and Joseph K. Berry

normal curve) to generalize the central tendency of the data. The values
used for soil and water conservation have traditionally used mean and
standard deviation to describe the responses to a traditional conservation
practice, informing its numerical distribution without any reference to the
spatial distribution of the data sources. The basic assumption for this method
of analysis was that these relationships among the data were randomly

(or uniformly) distributed in geographic space. Many of the analysis techniques
were considered less valid if the data exhibited spatial autocorrelation.
New methods and advances in models use spatial technologies to analyze
spatial relationships within and among mapped data for highly detailed
insight into the field of Precision Conservation and the potential for sitespecific applications that can contribute to environmental sustainability
(Berry, 1999, 2003a,b; Mueller et al., 2005; Qiu et al., 2007; Renschler
and Lee, 2005; Schumacher et al., 2005). These new soil and water conservation analysis capabilities enabled by GIS can be grouped into two broad
map analysis categories: Spatial Statistics, involving numerical relationships
of surface modeling and spatial data mining and Spatial Analysis, involving
geographical relationships, such as proximity and terrain configuration
(Berry, 1999, 2003a,b). These new spatial techniques will contribute to an
integrated evaluation of topography, hydrology, weather, management, and
other physical and chemical parameters, providing new insight into sitespecific Precision Conservation for management of flow-interconnected
agricultural and natural resources.
Figure 3 outlines the fundamental differences between the traditional
GIS mapping approach and the map analysis approach used in Precision
Conservation. Most desktop mapping applications take a set of spatially
collected data (e.g., parts per million, kilogram per hectare, etc.), then
reduces the data set to a single value (total, average, median, etc.), and
‘‘paints’’ a fixed set of polygons with colors reflecting the scalar statistic of
the field data occurring within each polygon.
For example, the left side of Fig. 3 depicts the position and relative values
for a set of field collected data; the right side shows the derived spatial
distribution of the data for an individual reporting parcel. The average of the
mapped data is shown as a superimposed plane ‘‘floating at average height of
22.0’’ and assumed to be the same everywhere within the polygon. But the
data values themselves, as well as the derived spatial distribution, suggest that
higher values occur in the northeast and lower values in the western
portion.
The first thing to notice in the figure is that the average exists hardly

anywhere, forming just a thin band cutting across the parcel. Most of the
mapped data is well above or below the average. That is what the standard
deviation attempts to reveal—just how typical the computed typical value
really is. If the dispersion statistic is relatively large, then the computed
typical is not typical at all. The limitation inherent in previous computer


Map analysis
Desktop mapping
Field data
Standard normal curve
fit to the data

Spatially
interpolated data

34.1% 34.1%

68.3%
+/−1 standard deviation

Average = 22.0
StDev = 18.7

22.0

28.2

Discrete
spatial object

(generalized)

80
60
40
20
0
−20
−40
−60

High = 50

80
60
40
20
Average = 22.0

0
−20
−40
−60

N

Continuous
spatial distribution
(detailed)

Figure 3 Desktop mapping uses aggregated, nonspatial statistics to summarize spatial objects (points, lines, and polygons), whereas map
analysis uses continuous spatial statistics to characterize gradients in geographic space (surfaces).


8

Jorge A. Delgado and Joseph K. Berry

applications arises from the fact that most desktop mapping applications
ignore data dispersion and simply ‘‘paint’’ a color corresponding to the
average regardless of numerical or spatial data patterns within a parcel.
However, the central tendency assumption can be misleading. Assume
the data is characterizing a toxic chemical in the soil that, at high levels,
poses a serious health risk. The mean values for both the parcel on the left
(22.0) and the right (28.2) are well under the ‘‘critical limit’’ of 50.0.
Desktop mapping would paint both parcels a comfortable green tone, as
their typical values are well below the level of concern. Even when considering the upper-tails of the standard deviations, the limit is not exceeded
(22.0 + 18.7 = 40.7 and 28.2 + 19.8 = 48.0). So from a nonspatial perspective, the aggregated results indicate acceptable levels of the chemical in
both parcels.
However, the lower right portion of the figure portrays a radically
different set of conditions. The left and right parcels are displayed as an
increasing gradient from low levels (green) through areas that are above the
critical limit (red tones). The high regions, when combined, represent a
contiguous subarea of nearly 15% of the combined area that likely extends
into adjacent parcels. The aggregated, nonspatial treatment of the spatial
data fails to uncover the spatial pattern by assuming the average value is
everywhere within the parcels.
Similar surface modeling investigations can be used to compile point
data into a continuous surface representation of data across the landscape to
explain any variance. Point density mapping, spatial interpolation, and map

generalization are examples of uses of surface modeling. Point density
mapping can be used to evaluate the number of aggregate points within a
specified distance (e.g., number of occurrences per hectare). Conservation
practitioners and scientists will collect point-sampled data to derive maps of
nutrient concentrations such as soil carbon. For example, we could use
kriging for spatial interpolation of weight-average measurements within a
localized area to assess carbon sequestration potential. An example of map
generalization is the use of polynomial surface fitting to the entire data set.
There are new techniques for spatial data mining that can be used to try
to uncover relationships within and among multiple mapped data layers
such as water tables, erosion potential, topography, soil texture, yields,
vegetative cover, soil depths, and others (Berry, 1999, 2003a). Berry
(2002) reported that these procedures, including coincidence summary,
proximal alignment, statistical tests, percent difference, level-slicing, map
similarity, and clustering can be used to assess similarities in data patterns.
Another type of spatial data mining is the use of predictive models that
use crop biomass cover (straw biomass production-dependent variable) and
the soil nutrient values [soil texture, soil carbonates, topography, hydrology,
water levels, and runoff (independent variables)], then quantify the data
pattern. As thousands of map locations are analyzed, a predictable pattern


Advances in Precision Conservation

9

between crop biomass and the variables may appear. This crop residue
production may be correlated to potential for reduction of erosion, of
surface runoff, and other soil and water conservation outcomes. Scientists
and practitioners can analyze the numerical relationships of spatial patterns

inherent in mapped data using surface modeling and spatial data mining.
These approaches can be used to explain variance by mapping and analyzing
spatial distributions (Berry, 2002).

3. Identifying Spatial Patterns
and Relationships
For more than 8000 years, we have been using maps with features that
identify special locations in the landscape to help us navigate. Precision
Conservation is a new way to use advanced technologies to integrate
thousands of data points and multiple layers of information contained in
maps for management and conservation of the agricultural and natural areas.
Specifically, Precision Conservation allows us to identify those management
landscape combinations that produce or receive significant impact. Scientists have been using spatial information for soil and water conservation for
decades. However, since the development of new computers and GIS
technology in the early 1970s, mapped data have changed to digital representations that are linked to larger databases, thereby increasing the number
of possible applications for Precision Conservation.
These new developments and the capability to integrate thousands of
points and multiple map layers of information to analyze spatial and temporal relationships are providing new answers for applications of Precision
Conservation. There is even potential to use these map analyses to contribute to air quality conservation. We could use these new analyses to evaluate
how conservation practices could be applied to reduce wind erosion from
the most sensitive areas. Spatial emissions of trace gases such as nitrous oxide
(N2O) and ammonia (NH3) volatilization could also be managed using
Precision Conservation. There is potential to use these layers of information
to develop Precision Conservation Management plans (Kitchen et al., 2005;
Knight, 2005; Lerch et al., 2005).
These advances in evolving technologies will continue to increase
during the next four decades, which will facilitate and speed the collection
and use of thousands of data points and multiple map layers. An example of
these new technologies is the mote, a quarter-sized wireless smart sensor
that fits anywhere. These smart sensors, initially developed by researchers at

University of California at Berkeley and Intel, could have future applications in soil and water conservation. These sensors, called ‘‘smart dust’’ by
their developers, Professors Kristofer Pister and Joseph Kahn of University


10

Jorge A. Delgado and Joseph K. Berry

of California at Berkeley, can be scattered, sending information from
remote locations. These and other new developments may contribute to
the collection of information that will be used to generate maps for use in
analysis in the field of Precision Conservation (Berry et al., 2003).
Map analysis procedures can be used to study landscape relationships
among map features. These analyses can assess the relative position of
features in the landscape and their connectivity to flows in the environment.
We can use these map analyses to evaluate effective distances, indexes,
optimal path connectivity, flows, biomass cover, soil texture, microterrain
analysis, elevation, distances to water bodies, and other landscape characteristics. We could simulate the flows over an elevation map to estimate the
erosion potential as described by Berry et al. (2003) by using an analysis that
follows the downhill path over a terrain. Berry (2003b) described this type
of map analysis as a method to account for all the areas sharing common
paths (Fig. 4).
The ability to model flows and interconnected cycles will benefit from
the current evolutionary phase of GIS involving new geo-referencing
approaches. In the 1970s, the research and early applications centered on
Computer Mapping (display focus) that yielded to Spatial Data Management
(data structure/management focus) in the next decade as we linked digital
maps to attribute databases for geo-query (left side of Fig. 5). The 1990s
centered on GIS Modeling (analysis focus) that laid the groundwork for whole
new ways of assessing spatial patterns and relations, as well as for entirely new

applications such as Precision Agriculture.
Today, in its fourth decade, GIS is centered on Multimedia Mapping
(mapping focus) which brings the technology full circle to its beginnings
(Berry, 2007b). While advances in virtual reality and three-dimensional
visualization can ‘‘knock your socks off,’’ they represent incremental progress in visualizing maps that exploit dramatic computer hardware/software
advances. Radical innovation is being addressed by current geospatial
research that is refocusing on data structure and analysis (Berry, 2007a).
The bulk of the current state of geospatial analysis relies on ‘‘static
coincidence modeling’’ using a stack of geo-registered map layers. However,
the frontier of GIS research is shifting focus to ‘‘dynamic flows modeling’’ that
tracks movement over space and time in three-dimensional geographic
space. But a wholesale revamping of data structure is needed to make this
leap. The impact of the next decade’s evolution will be huge and will shake
the very core of GIS—the Cartesian coordinate system itself, a spatial
referencing concept introduced by mathematician, Rene Descartes over
400 years ago.
The current two-dimensional square for geographic referencing is fine
for ‘‘static coincidence’’ analysis over relatively small land areas, but is
woefully lacking for ‘‘dynamic three-dimensional flows.’’ It is likely that
Descartes’ two-dimensional squares will be replaced by hexagons