Particulars in Context
707
Particulars in Context:
Maintaining a Balance in Soil Geography
Linda R. Barrett
Department of Geography and Planning, University of Akron
A
s society faces increasingly complex
environmental and social problems, calls
for integrative, interdisciplinary solutions are growing (e.g., Cairns 1993; Patten
1994; Lubchenco 1998). As participants in an
integrative discipline, geographers have often
considered themselves ideally situated to tackle
these questions. “Geography has always been
(and remains) a generalized—as opposed to a
specialized—discipline. Its viewpoint is one of
broad understanding,” states one introductory
physical geography textbook (McKnight 1996:
3). Another emphasizes, “Geography is in a
unique position to synthesize the environmental, spatial, and human aspects of all these concerns” (Christopherson 1997:5). We often make
similar statements to our students or others—
whenever we need to explain our discipline to
outsiders.
But privately, among ourselves, we may question the veracity of these assertions. The bewildering multiplicity of subspecialties within
geography makes communication across subdisciplinary boundaries difficult, and we wonder
whether, as a discipline, we have carved up the
world to such an extent that we have lost the
right to make claims to synthesis or integration.
Have we prodigally sold our integrative inheritance and run off to squander it on the glittering, reductionist high life?
Behind these vague feelings of unease in the
discipline lie valid methodological questions. In
this paper, I discuss a few of the methodological
implications from the vantage point of the subdiscipline of soil geography.1 I argue that, despite
the difficulties, we must strive to bridge the gap
between subdisciplinary reductionist research,
on the one hand, and society’s real-world concerns on the other. I begin by describing two
approaches to research in soil geography, the
state-factor approach and the process-based
approach. The tension between these two orientations embodies a key methodological issue in
soil geography today. Next, I examine the relative merits of reductionism and synthesis in soil
geography. I conclude by reflecting on the
nature of disciplinary identity and the struggle of
integrative disciplines like soil geography to
maintain a balance between the particulars (i.e.,
research involving detailed scales in space and
time) and their overall context (the broad-scale
outlook generally considered “integrative”).
State Factors and Processes:
Context and Particulars
As in the related disciplines of geology and
biology, within soil geography, there exists a
basic tension over the relative merits of what
Spedding (1997) terms “(timebound) descriptive regional studies” versus “(timeless) analysis
of process,” or between what Hoosbeek and
Bryant (1992) call a “functional” and a “mechanistic” understanding. This tension has its roots
in soil geography’s status as a “compositional
historical science” focused on changing, individual historical phenomena as well as the properties and processes producing them (Simpson
1963; Spedding 1997). Recently, geology and
biology have been converging towards physics
and mathematics, tending to become more lawand process-oriented (Goodwin 1994; Spedding
1997).
Parallel trends in soil geography are manifested in pressures to adopt new methodological
frameworks for examining the mechanisms and
processes of soil genesis. In soil geography, timebound descriptive studies have traditionally utilized a functional-factorial or state-factor
approach. In a state-factor model, the soil is
viewed in the context of the environmental conditions within which soil development occurs.
The state factors are not pedogenic processes or
mechanisms, but instead are independent variables defining the state of the soil system (Jenny
1980; Phillips 1989).
The most widespread state-factor model is
Jenny’s (1941) equation, which relates a soil
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Barrett
property (s) to the five independent soil-forming
factors, namely, climate (cl), organisms (o),
relief (r), parent material (p), and time (t):
s = f (cl, o, r, p, t, ...),
but other state-factor models have also
emerged. For example, Jenny (1961) reformulated his original state factors into a new equation with three separate categories of factors:
interior conditions, external energy-matter
potentials, and time. Runge’s (1973) energy
model relates soil development to the three factors of organic-matter production, water, and
time. Recognition that factors and processes
may promote either horizonation (progressive
pedogenesis) or haploidization (regressive pedogenesis) in soils led to the development of two
alternative conceptual models, the evolution
model of pedogenesis and the dynamic-rate
model (Johnson and Watson-Stegner 1987;
Johnson et al. 1990).
State-factor models situate the soil in a particular historical location and thereby provide a
useful framework for extrapolating soil characteristics across time and space (Amundson and
Jenny 1997). By carefully selecting study sites,
quantifiable relationships between soil properties and the state factors, as determined by the
soil’s location, can be defined using statistical
linear-regression techniques (Richardson and
Edmonds 1987). For example, chronofunctions
describe change in soil properties with increasing surface age (Bockheim 1980; Schaetzl et al.
1994). The state-factor model can also be used
qualitatively to assess the stability of a particular
characteristic or evolutionary trend in the soil
(Phillips 1989).
Technical shortcomings of the state-factor
approach are numerous. For one, most soils are
polygenetic; i.e., state factors have changed
over the course of soil development (Johnson et
al. 1990). State factors may also influence soil
properties over a variety of time and space
scales (Phillips 1989). The complexity of the
interrelationships between factors in Jenny’s
(1941) formulation of the model suggests that a
general solution of the equation is not possible
(Yaalon 1975). The prospect of complex
(chaotic) sensitivity to initial conditions is
another barrier to using statistical regression to
quantify a state-factor model (Phillips 1993;
Phillips et al. 1996).
From a methodological standpoint, however,
the state-factor approach appears limited prima-
rily because it ignores the mechanisms of soil
development. Competing frameworks emphasizing the timeless analysis of process have been
advanced. For example, Simonson (1959) proposed that four types of processes are involved in
soil development: additions, removals, transformations, and translocations. The long time
frame over which soil development takes place
makes observation of processes difficult.
Researchers may deduce processes by observing
the resultant soil itself, as in a mass-balance
approach (Chadwick et al. 1990; Jersak et al.
1995). Alternatively, they may attempt to measure processes directly by monitoring the chemical composition of the soil solution (e.g.,
Dahlgren and Ugolini 1989; Barrett and
Schaetzl 1998). Processes may be studied at the
level of a single pedon, or by monitoring flux of
materials into, out of, and through an entire
landscape (Huggett 1975; Sommer and
Schlichting 1997).
State-factor approaches, despite their drawbacks, have retained a measure of prominence
in soil studies due to their power to extrapolate
soil characteristics across time and space
(Amundson and Jenny 1997), and their consequent value where detailed soil surveys have
not yet been completed (Hoosbeek and Bryant
1992). Soil development, like geomorphic
activity, looks very different depending on the
temporal and spatial scales at which we examine it. Over long intervals of time and in large
areas, the action of processes is concealed by
the observable historical states, but in small
areas and over short periods of time, processes
become a priority (Spedding 1997). Thus, as we
seek to understand soil behavior over short
time periods, as, for example, in response to
anthropogenic pressures, process models may
become more appropriate (Hoosbeek and
Bryant 1992).
As geographers, however, we must not abandon the advantages of the timebound functionalfactorial approach to soils, even while we make
excursions into the realm of process-based
inquiry. Soil geography, like geology, is an interpretive (hermeneutic) and historical science
(Frodeman 1995). It is to our peril that we neglect the opportunity to construct narratives, even
timebound ones, of soil development at each
individual location.
Particulars in Context
Reductionism, Synthesis,
and Scale
Reductionist studies, which have become the
modus operandi of much scientific research,
often take place at very detailed spatial and temporal scales. Reductionism functions by studying
small systems in detail in order to aggregate the
information into a coherent body of knowledge
about a broader system. The difficulty of generalizing from a more- to a less-detailed spatial
scale is, however, a major weakness in the reductionist enterprise. Computer-based technologies, with their ability to handle massive
amounts of data, may be able to aid in this
endeavor. In geomorphology and soil geography,
geographic information systems and remotesensing technologies may be particularly effective vehicles for the necessary data synthesis, as
they are able to simultaneously accommodate a
variety of scales (Walsh et al. 1998). The sheer
size of the database necessary to span spatial
scales across several orders of magnitude, as
would be required to truly integrate data from
detailed to broad scales, however, makes this
hope more theoretical than practical, at least
with current technologies.
In soils research, the ubiquitous soil cover has
often been broken down into more detailed
units by means of a hierarchical subdivision.
This pedological spatial framework extends from
individual soil components at the microscopic
scale up through global systems at the broadest
scale (Figure 1). The hierarchical nature of this
framework is convenient for examining soils at
709
variable scales of resolution (Dijkerman 1974;
Wilding 1994). Theoretically, appropriate techniques are applied to each scale of study, and the
results can be hierarchically grouped for component integration and system extrapolation
(Wilding 1994). The hierarchy conceptually
provides the means to organize the disparate
techniques and methodologies that are applied
in the study of soil. This diagram, in fact,
embodies precisely the reductionist hope that
details, represented by the right-hand side of the
diagram, may be aggregated into a coherent
body of knowledge about the system as a whole,
on the left side.
In practice, however, generalizing from more
detailed scales, even with computer technology
and the pedological hierarchical framework,
may be difficult. For example, attempts to estimate values of a particular soil property (such as
organic carbon content) over a broad region,
from information derived at more detailed
scales, face a number of hurdles, including
unspecified heterogeneity and missing or imprecise data at the detailed scales (e.g., Kern 1994,
1995; Batjes 1997). The very fact that different
techniques are involved in studying different
scales (Figure 1) hinders communication among
researchers focused at the different levels of the
hierarchy. High levels of spatial variability, often
ascribed to randomness, plague many types of
soil studies (Wilding and Drees 1983; Burrough
1983, 1993). Recent work has suggested that
soil systems possess complexity that is not random, but is the result of many complex, deterministic processes, and therefore can be
Hierarchical Levels
Soil
Landscape
Soil
Continuum
Pedon
Soil
Landscape
Body
Remote Sensing
Soil
Aggregate
Soil
Horizon
Visual
Soil
Particle
Mineral
Molecular
Structure
Microscopy Spectroscopy
Figure 1. Schematic illustration of the hierarchical levels of the pedological spatial framework, with techniques
considered appropriate for use at each scale. Adapted from Wilding (1994), with terminology from Dijkerman
(1974).
710
Barrett
explained in terms of deterministic chaos
(Phillips 1994; Werritty 1997). If this is the case,
prediction of these systems cannot be improved
by reductionist modeling and further attempts
to use reductionist methods to model them are
likely to prove futile.
Disciplinary Identity and
Interdisciplinarity
The academic landscape has been split into
tiny subspecialties in a manner analogous to
reductionism. The current structure of the academic disciplines began as an organizational
response to the knowledge revolution of the
nineteenth century, with disciplinary fragmentation following the growth of knowledge
(Rothblatt 1997; Clark 1997). The failure of
reductionist science to provide meaningful
answers to complex real-world questions (e.g.,
global climate change, hunger, and poverty),
however, has led to many calls for interdisciplinary, integrative approaches to these problems.
Changing the nature of disciplinary identity
itself may help to counteract reductionist tendencies in science. Views that most scholarly
activities should take place within the boundaries of a subdiscipline limit intellectual activity
and foster reductionism. In fact, exchanges
across disciplinary boundaries tend to increase
intellectual creativity: innovation within a discipline depends largely on exchanges with other
disciplines, while fields that do not interact
across disciplinary boundaries tend to stagnate
(Dogan 1997). From this viewpoint, disciplines
and subdisciplines become necessary more as a
social arrangement of scholars and for administration than as a logically dictated boundary
around one’s studies (Sigma Xi 1988). The discipline, however, while it can restrict inspiration
or methodology, does provide a useful, even
essential, context for intellectual activity
because scholars (as people) require social and
cultural structure (Weiland 1995).
Yet Geography’s disciplinary self-identification as integrative suggests an affinity for
approaches that favor flexible and permeable
disciplinary boundaries. The geographic subdisciplines, particularly soil geography, easily qualify as “complex disciplines,” which are composed
of distinct parts of other types of study, with specific and generally agreed-upon goals requiring
various scientific and technological approaches
of investigation to meet those objectives
(Osterkamp and Hupp 1996). The challenges to
effective scholarship within a complex discipline, however, are considerable. For example,
Weiland (1995) claims that being a historian
means being accountable to problems and innovations in many fields; the same could certainly
be said of soil geography. Keeping track of
advances in multiple fields can be overwhelming
to individual researchers. Furthermore, as scientific techniques become more complex, they
require large investments of time for mastery.
The training required to learn a technique probably contributes to reductionist thinking, as the
very time required to master a technique provides an incentive to apply it wherever possible.
Since the soil results from the total of the geomorphological, biological, climatic, physical,
chemical, and anthropogenic forces that have
acted at a specific location, we soil geographers
ought to view our subject in a broad, integrative
fashion. The soil lends itself to approaches that
will put the particulars of process into a broader
context, if we allow ourselves to be “reenchanted” by our subject (Baker and Twidale
1991). We should fight the institutional and disciplinary forces that box us into reductionist,
process-only questions, and allow ourselves to
think across multiple spatial and temporal
scales. Forays outside of our own subdisciplinary
comfort areas, if only to read and learn, may
bear unexpected fruit in our own research by
permitting us to bridge previously impassable
chasms.
To some extent, interdisciplinary research
teams of scholars from multiple specialties may
help to provide the necessary broad context
(e.g., Church et al. 1985; McFadden and
Knuepfer 1990; Patten 1994; Rothman and
Robinson 1997). Nevertheless, interdisciplinary
teams cannot accomplish truly integrative goals
if the participants are narrowly focused on
reductionist subdisciplines. No amount of collaboration can substitute for broad-ranging
views and interdisciplinarity in training.
In the current era of increasingly complex realworld problems, we must make every effort to
allow ourselves to use approaches that consider
the broader context while not ignoring the particulars. Such problems demand that we accept
the challenge to retain a state-factor mindset
while not neglecting the need to also address
process. If observation is theory-dependent
(Frodeman 1995; Rhoads and Thorn 1996), we
Particulars in Context
should contemplate our observations in light of a
variety of theoretical viewpoints, while attempting to maintain a balance between the necessity
of understanding particulars and placing them in
a broader real-world context. The ideal method
of producing this balance has not yet been found
(Easton 1991), but we should strive for it as we
study soils and the larger problems of society.
Conceptual, qualitative analysis and locationspecific narratives, grounded where appropriate
in a functional-factorial framework, will provide
a starting point towards this goal (Frodeman
1995; Spedding 1997).
Note
1.
I use the term “soil geography” to denote activity of geographers involved in studying soils. It
includes both general studies of soil distribution
(e.g., Hole and Campbell 1985; Steila and Pond
1989), as well as “soil geomorphology” (Dixon
1986), which is usually focused on the interface
between landform and soils (e.g., Birkeland
1984; Daniels and Hammer 1992). Considerable
overlap exists between soil geography and disciplines such as pedology and geological soil geomorphology; although I focus on efforts of
geographers, I make no overt attempt to distinguish among these disciplines.
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Correspondence: Department of Geography and Planning, University of Akron, Akron, OH 44325-5005, email
[email protected].
Methodology in Climatology
Andrew M. Carleton
Department of Geography and Earth Systems Science Center, The Pennsylvania State University
D
uring the final quarter of the twentieth
century, the “science of climate” (Landsberg 1987) has gained enormously in
both scope and prestige, in stark contrast to its
former role as the “ugly duckling” of national
meteorological services, when it was concerned
largely with the compilation of station annual
precipitation and temperature statistics. This
metamorphosis has also involved “geographical
climatology” (Mather et al. 1980; Yarnal et al.
1987), which used to consist of classifying largescale climate regions of the Earth (Sanderson
1999) from some combination of thermal and
moisture indices, usually in the context of
explaining patterns of vegetation or soil-forming
factors. The primary catalysts involved in this
(r)evolution in climatology include:
• The recognition of an integrated “climate system” (e.g., Bryson 1997), encompassing not
just the atmosphere (the province of meteorologists) but the biosphere, the cryosphere,
and the hydrosphere (especially the oceans),
and linked by nonlinear feedback processes
that are radiative, thermal, chemical, and
dynamical.
• A perceived increase in the occurrence of
both extreme weather (e.g., tropical cyclones,
tornadoes, heat waves) and climate anomalies (droughts, extended wet periods), as well
as decade-scale changes in temperature and
precipitation (e.g., Parker and Folland 1988;
Jones et al. 1999).
• A growing awareness of the potential and real
impacts of human activities in some climate
changes (Cotton and Pielke 1995), including
“global warming” due to increases in trace
(“greenhouse”) gases; emissions of chlorofluorocarbons (CFCs) and their effects on the
stratosphere’s ozone layer; land-cover modifications, particularly deforestation and conversion to agriculture, urbanization, and
large-scale irrigation; tropospheric air pollution, particularly the emissions of sulfate
aerosols; and cloud-cover increases, especially in regions of heavy jet air traffic (e.g.,
Changnon 1981, 1992; Karl et al. 1988, 1991;
Hunter et al. 1993; Raymond et al. 1994;
Parungo et al. 1995; Gallo et al. 1996; Lyons
et al. 1996; Travis 1997; O’Brien 1998).
• Application of the physical and mathematical
underpinnings of meteorology and atmospheric science to determining how the climate
system works, how it varies (i.e., climate as a
boundary condition [Bryson 1997]), and how it
might also be predicted (or climate dynamics
[Gates 1979]), particularly as revealed by
General Circulation Models (GCMs). Developed originally for numerical weather prediction, GCMs have been applied to both climate
prediction and retrodiction (paleoclimatology,
e.g., CLIMAP, COHMAP).
• The availability of new sources of data for
studying the climate system, especially those
derived remotely from Earth-orbiting satellites
(e.g., Barrett 1987), and also from intensive
field programs such as GEWEX (Global Energy
and Water Cycle Experiment), FIFE (First
ISLSCP: International Satellite Land Surface
Climatology Project, Field Experiment), FIRE
(First ISCCP: International Satellite Cloud
Climatology Project, Regional Experiment),