Plant Ecology 150: 77–96, 2000.
© 2000 Kluwer Academic Publishers. Printed in the Netherlands.
77
Simulated long-term vegetation response to alternative stocking strategies
in savanna rangelands
Gerhard E. Weber1 , Kirk Moloney2 & Florian Jeltsch3
1 Department
of Ecological Modelling, UFZ Centre for Environmental Research, Leipzig, Germany (E-mail: weber@oesa.ufz.de); 2 353 Bessey Hall, Department of Botany, Iowa State University, Ames, IA 50011-1020, USA,
3 Universität Potsdam, Vegetationsökologie und Naturschutz, Potsdam, Germany
Key words: Grazing heterogeneity, Grazing management, Livestock grazing, Rangeland management,
Semiarid rangeland, Vegetation change
Abstract
Increasing cover by woody vegetation, prevalent in semiarid savanna rangelands throughout the world, is a degrading process attributed to the grazing impact as a major causal factor. We studied grazing effects on savanna
vegetation dynamics under alternative stocking strategies with a spatially explicit grid-based simulation model
grounded in Kalahari (southern Africa) ecology. Plant life histories were modeled for the three major life forms:
perennial grasses, shrubs, annuals. We conducted simulation experiments over a range of livestock utilization
intensities for three alternative scenarios of small scale grazing heterogeneity, and two alternative strategies: fixed
stocking versus adaptive stocking tracking herbage production. Additionally, the impact of the duration of the
management planning horizon was studied, by comparing community response and mean stocking rates after
20 and 50 years. Results confirmed a threshold behavior of shrub cover increase: at low, subcritical utilization
intensity little change occurred; when utilization intensity exceeded a threshold, shrub cover increased drastically.
For both stocking strategies, thresholds were highly sensitive to grazing heterogeneity. At a given critical utilization
intensity, the long term effect of grazing depended on the level of grazing heterogeneity: whereas under low heterogeneity, shrub cover remained unchanged, a large increase occurred under highly heterogeneous grazing. Hence,
information on spatial grazing heterogeneity is crucial for correct assessment of the impact of livestock grazing
on vegetation dynamics, and thus for the assessment of management strategies. Except for the least heterogeneous
grazing scenario, adaptive stocking allowed a more intensive utilization of the range without inflating the risk of
shrub cover increase. A destabilizing feedback between rainfall and herbage utilization was identified as the major
cause for the worse performance of fixed compared to adaptive stocking, which lacks this feedback. Given the
usually high grazing heterogeneity in semiarid rangelands, adaptive stocking provides a management option for
increasing herbage utilization and thus returns of livestock produce without increasing degradation risks.
Introduction
Semiarid savanna rangelands are characterized by
large fluctuations in amount and distribution of annual
rainfall and, as a consequence, exhibit a high degree
of variability in primary production (Friedel 1990).
Traditionally, livestock management has attempted to
cope with this variability by stocking at supposedly
low or conservative fixed rates, in an effort to promote long term productivity of the range (Mace 1991).
The validity of this management paradigm has been
challenged, not least due to a trend towards increasing
woody cover reported from many semiarid rangelands
(Archer et al. 1988; Dean & MacDonald 1994; Hacker
1984; Van Vegten 1982). For this trend, livestock
grazing itself is reckoned a major cause (Buffington
& Herbel 1965; Fleischner 1994; Skarpe 1990b; Van
Vegten 1983). However, if livestock is kept at stocking
rates considered to be low by managers why then is
woody cover increasing? The key may be found in
78
the underlying conceptual model used to understand
the processes driving vegetation dynamics in semi-arid
ecosystems.
According to the Clementsian (1916) successional
paradigm, rangeland community dynamics are composed of a continuous sequence of reversible states
proceeding towards a single persistent climax in the
absence of grazing or other disturbances. Grazing
counteracts the successional tendency, and a given utilization intensity results in a specific equilibrium state
of the vegetation lying somewhere on the continuum
from an ungrazed or lightly grazed climax state to a
heavily grazed early-successional state. Thus, management would ideally select a stocking rate (i.e.,
carrying capacity) that keeps the rangeland community at a desirable successional state, producing a
maximum sustainable yield under grazing. However,
Westoby et al. (1989) suggest that the equilibrium
concept of continuous and reversible compositional
changes does not apply to semiarid rangelands. They
argue that a more appropriate paradigm would be the
state-and-transition concept, which views the system
as being composed of a limited number of states connected by a more complex pattern of transitions than
allowed under the Clementsian notion of succession.
For example, an adverse climatic event under conditions of heavy grazing could trigger a transition from a
range dominated by perennial grasses towards a shrub
dominated state; however, a reverse transition to the
grass dominated state would be extremely unlikely due
to low mortality rates and high drought resistance of
woody vegetation (Westoby et al. 1989). The stateand-transition concept thus offers an explanation for
the observation that degraded rangelands often do not
recover even under drastically reduced utilization intensities (Milton & Dean 1995). As a consequence,
proponents of the state-and-transition concept advocate adaptive stocking as an alternative management
strategy for avoiding the possibly hazardous consequences of fixed stocking based on the range succession approach (Abel & Blaikie 1989; O’Reagain &
Turner 1992; Walker 1993). Thus, the two conceptual models of rangeland dynamics leave us with two
fundamentally different management strategies: fixed
versus adaptive stocking.
In rangeland management, like in any other field
dealing with complex dynamical systems, modeling
approaches are a common means of studying the available strategies. However, most models of rangeland
systems are of limited utility for long-term assessments of the impact of different management strate-
gies. For example, one group of studies lacked any
feedback of herbivory on primary production (Riechers et al. 1989; Hatch et al. 1996) and another limited
feedback effects to short-term changes (Stafford Smith
& Foran 1992). In a more promising study, a decision support system based on the state-and-transition
concept was developed (Bellamy et al. 1996). In this
model, the rules for transitions between different states
were defined for a particular Australian rangeland.
However, the model assumed that the outcome of the
grazing impact was known at the floristic community
level, and analyses of different livestock management scenarios were based on this knowledge. In all
of these cases, it is impossible, to develop a longterm assessment of management strategies based on
an understanding of the ecological processes driving
population dynamics in rangelands. What is needed
are models that integrate a consideration of plant-plant
and plant-animal interactions at the population level
(e.g., Hacker & Richmond 1994), with a consideration
of the major environmental factors driving community
dynamics. To that end, Jeltsch et al. (1997a, 1997b)
have developed a model for semiarid savanna rangelands in the southern Kalahari. They explored the
effects of livestock grazing, fire, and rainfall patterns
on long-term community dynamics with a spatially
explicit, grid-based model of vegetation dynamics.
These models, complemented by their recent investigation of the spatial aspects of grazing (Weber et al.
1998), successfully demonstrated that spatially explicit models hold merit for examining management
questions, as has previously been argued by Dunning
et al. (1995) and Turner et al. (1995).
The need to consider spatial effects in studying
the long-term impact of herbivory arises from the key
role of plant-plant and plant-animal interactions. Interactions among plants are largely confined to small
spatial scales due to plant immobility during most of
their life cycle, and also to their morphological and
reproductive traits. Thus, for plant communities, large
scale vegetation dynamics are influenced by local interactions at small spatial scales. For plant-animal
interactions, such as ungulate herbivory, the role of
space is less obvious. However, a large number of
factors influence grazing distribution (Owens et al.
1991), resulting in heterogeneities over a range of spatial scales from the individual plant to the patch, the
landscape, and the regional scale (Coughenour 1991;
Fuls 1992; Pickup & Chewings 1994; Ring et al.
1985). The impacts of heterogeneous grazing intensity on vegetation dynamics have been documented
79
at the landscape scale for grazing gradients around
water holes (Valentine 1947; Hart et al. 1993) and
for patchy grazing patterns with patch diameters in
the range of several meters (Kellner & Bosch 1992;
Fuls 1992). However, given a nonlinear vegetation
response to a spatially heterogeneous grazing impact,
predictions of the landscape-scale vegetation response
based on mean grazing intensity, might deviate greatly
from predictions incorporating grazing heterogeneity.
Hence, Noy-Meir (1981) considered the incorporation
of spatial heterogeneity as crucial for models of arid
ecosystems. This claim has recently been confirmed in
modeling studies of grazing gradients at the landscape
scale (Pickup & Chewings 1994; Jeltsch et al. 1997b)
and of patch level grazing heterogeneity (Weber et al.
1998).
We have conducted a study of alternative management strategies using a spatially explicit model that
incorporates spatial heterogeneity in grazing patterns,
the results of which are presented in this paper. We
compared the long-term effects of fixed versus adaptive stocking strategies on landscape-scale community
composition, and examined the maximum long-term
mean stocking rates achievable at a given risk of
ecological degradation for specific management planning horizons. To this end a spatially explicit model
of vegetation dynamics of a semiarid savanna rangeland (Jeltsch et al. 1997a; Weber et al. 1998) was
modified to incorporate both a fixed and an adaptive
stocking strategy, the latter allowing stocking rates
to be altered in response to changes in annual primary production. We then analyzed both management
strategies over a range of light to heavy utilization intensities for different scenarios of patch level spatial
grazing heterogeneity, and over management planning
horizons of 20 and 50 years. We compared community response to grazing using shrub cover as the key
state variable, and determined maximum utilization
intensities achievable under a given risk of ecological
degradation.
Methods
Study site
The simulation model represents a summer rainfall region with about 400 mm mean annual precipitation.
This is characteristic of the savannas in the southern
Kalahari of Botswana (Field 1977) and South Africa
(Fourie et al. 1985) that are used for cattle ranching.
Woody vegetation is characterized by Grewia flava
and Acacia mellifera ssp. detinens (Skarpe 1990a)
as the major species accounting for increased cover
of woody plants in areas affected by high grazing
pressure (Skarpe 1990b). Perennial grasses such as
Schmidtia pappophoroides and Stipagrostis uniplumis
dominate the herbaceous layer. Additionally, annual
grasses such as Aristida congesta contribute significant fractions to vegetation cover in favorable years.
Rangeland model
We used a spatially explicit grid-based simulation
model introduced by Jeltsch et al. (1997a, b) for
studies of shrub cover dynamics in semiarid savanna
rangelands in the southern Kalahari. Full documentation of this model has been published elsewhere
(Jeltsch et al. 1997a, b). Hence, we give only a summary of the basic model and then focus on the modifications introduced for grazing and the alternative
stocking strategies.
Basic model
The model depicts a total area of 25 ha subdivided into
a rectangular grid of 10 000 square cells. Each cell is
5 m on a side, a size corresponding to the maximum
observed scale of direct grass-shrub interaction (Belsky 1994). Based on these spatial sub-units, the model
describes in annual time steps: soil water availability, vegetation dynamics, herbage production, grazing,
and fire (Figure 1). Vegetation is modeled as being
composed of different life forms, and individual cells
are either dominated by perennial grasses and herbs,
shrubs, or annuals or a mixture of forms is present.
Daily rainfall data in the model are generated with
a South African rainfall simulator (Zucchini et al.
1992) parameterized for a site with a long-term mean
annual rainfall of 394 mm located at 25◦ 200 S and
23◦ 320 E in the southern Kalahari. Annual soil-water
availability is then calculated as described by Jeltsch
et al. (1997a). Daily rainfall data are aggregated into
longer rain events; individual events are separated by
at least three days without rain. Events of less than
10 mm are considered ecologically ineffective in the
Kalahari (Skarpe 1990b) and are disregarded. Annual
soil-water availability for plants is calculated as the
sum of the rainfall contained in rain events, reduced
by 50% to account for undifferentiated losses through
evaporation, run-off, and deep percolation (Whitmore
1971; Skarpe 1990b). Available moisture is distributed through the top-soil (depth 40 cm) and sub-soil
80
Figure 1. Flowchart of the simulation model; for details see text.
81
(depth > 40 cm) layers with the assumption that only
rain events with more than 40 mm of precipitation
contribute to sub-soil moisture. Water consumption by
plants within individual grid cells reduces the locally
available soil moisture in the top- and sub-soil layers.
The amount of moisture reduction by water uptake
depends on root distributions and evapotranspiration
(Jeltsch et al. 1997a). As a consequence, shrubs reduce soil moisture within their own cells and within
adjacent cells, since their lateral root extension is not
limited to 5 m × 5 m patches (Belsky 1994).
We categorize levels of potential production into
three different classes for perennial life forms: low,
moderate, and high. The potential production of perennial grasses in any given year depends on the previous
year’s production, available soil water, biomass reduction by grazing, and grass fires. For perennial grasses
and herbs, dry matter production is 800, 1400, and
3000 kg ha−1 under low, moderate, and high levels of
potential production (Weber1998B). If sufficient soil
water is available for a perennial life form, its potential production is maintained or increases. Given
insufficient moisture, potential production decreases,
and perennials with low production potential run a
chance of local extinction (Jeltsch et al. 1997a). At
the grid-cell level, probabilities of colonization by any
life form and mortality of perennials depend on the
amounts of available soil water. For example, in the
absence of available sub-soil water, a shrub-cell with a
low production potential runs a 5 to 30% chance of extinction depending on the amount of available top-soil
water (see Jeltsch et al. 1997a, Table 2). Colonization of empty grid-cells also depends on soil water.
For example, shrub colonization does not occur in the
absence of sub-soil water (see Jeltsch et al. 1997a).
Like colonization and extinction, fire is modeled
probabilistically (Jeltsch et al. 1997a). No fire occurs
when the fuel load, which is the residual grass biomass after grazing, is less than a minimum threshold
of 1000 kg ha−1 (Frost & Robertson 1987). Above the
minimum threshold, fire probability increases with the
square root of the fuel load up to 100% at the maximum fuel load of 3000 kg ha−1 . Maximum fuel load
is given by the high level of production potential for
perennial grasses and herbs. Shrubs in or adjacent to
burning grass-cells stand a 5% chance of extinction
(Trollope 1982).
Stocking strategies
We compared two stocking strategies: fixed stocking
and adaptive stocking. Under fixed stocking, stock-
ing rate is kept constant irrespective of community
composition or forage production of the range. This
implies that management provides for additional feed
in case forage need exceeds production. Under fixed
stocking, herbage utilization M during any given year
is given by
M = F /B,
(1)
ha−1 )
with annual forage consumption F (kg
calculated as the daily dry mass intake of 10 kg per large
stock unit (Fouché et al. 1985) multiplied by the stocking rate, and B (kg ha−1 ) representing total annual
herbage biomass production. By keeping stocking rate
constant, management forces F to be a fixed rate of
consumption for the range. Thus, herbage utilization
M results from the management input F and the range
level herbage production B. In contrast, under adaptive
stocking, herbage utilization M is set as a fixed management target and annual forage demand F is altered
to track annual variation in herbage production, i.e.,
F = M · B.
(2)
Although stocking rates in a model can respond instantaneously and perfectly to changing rates of forage
production, in reality changes in stocking rate will be
imperfect and time delayed. Time lags originate since
the current year’s forage production can at best be
quantified subsequent to termination of a rainy season, and adjustment of stock numbers will also take
time. Within the framework of our annual model, it
is justifiable to model lag-free adjustments of stock
numbers, since there is a distinct rainy season within
the study area. (cf., Stafford Smith & Foran 1992;
Riechers et al. 1989). However, adjustment will usually not be perfect, since rates of change in stock
numbers are likely to be limited: purchasing additional
stock can be affected by cost or supply restrictions,
and increasing stock numbers by breeding meets biological restrictions; on the other hand, managers are
hesitant to decrease stock numbers, be it for traditional
reasons, market restrictions, or due to the problems
envisioned with regards to subsequent restocking. To
simulate constraints of adaptive stocking, we therefore introduce restrictions in the rates of change in
stock numbers. During a current year t, within which
herbage production Bt increases, we restrict the adaptive forage demand F t – and thus the adaptive stocking
rate – according to
Ft = min(M · Bt , (1 + up) · Ft −1 )
(3)
with F t −1 denoting the previous year’s forage demand,
and the change restricting parameter up ≥ 0. For ex-
82
ample, with herbage production Bt exceeding Bt −1
by 50%, and up = 0.2, the increase in stocking rate
would remain limited to 20%. If herbage production
decreased, forage demand is restricted by
Ft = max(M · Bt , (1 − down) · Ft −1 )
(4)
with the change restricting parameter down ∈ [0,1].
With up = 0, and down = 0, any adaptation is excluded and a fixed stocking rate results; with down = 1
and up reasonably large, for example up = 4, change
restrictions are removed and the stocking rates resulting from Equations (3) and (4) are identical to the
ones resulting from the fully adaptive strategy given
in Equation (2). Thus, with the two restriction parameters up and down, we define a continuum of stocking
strategies from fixed to fully adaptive.
As a comparative measure for utilization intensity under alternative stocking strategies, we calculate
mean equivalent stocking rates from the total herbage
biomass actually consumed during all of a simulation
period and total forage demand per large stock unit.
This mean stocking rate is a management oriented
measure of the range response to utilization, since
it is related to the economic return from the grazing
system.
Grazing model
At an arbitrarily small scale, a grazing event is comprised of the selection of a site, and its subsequent defoliation. In characterizing grazing events, we follow
the terminology given in Heitschmidt et al. (1990):
defoliation intensity d e is the proportion of biomass
consumed in a single grazing event; defoliation frequency is the total number of grazing events on a
particular site during a period of interest (a whole year
in this study); defoliation severity da denotes annual
consumption relative to annual herbage biomass production, and results from all the grazing events on a
particular site in a given year. Accordingly, we model
the grazing process as a sequence of grazing events. A
grazing event is comprised of the random selection of
a suitable grid-cell and the cell’s subsequent defoliation. The sequence of grazing events in a given year
continues until either the total annual forage requirements are met or no more forage is available. In order
to allow for local variability, defoliation intensity de of
a grazing event is calculated as
de = min(1, M · r),
(5)
with r denoting a random modifier. The amount of biomass consumed in a grazing event is then given by the
product of the defoliation intensity de and the amount
of herbage biomass in the selected grid-cell. Grazing
does not affect potential production level, unless the
residual biomass is down to the production level of a
lower productivity class. In this case, potential production is reduced to the respective class. If grazing
leaves less than 0.25 kg in a grid-cell, the herbaceous
component is no longer considered and a cell hitherto
occupied only by perennial grasses would be open to
colonization by all three life forms in the subsequent
year.
Small scale grazing heterogeneity
Grazed at individual severities, the model’s 25 m2
grid-cells define the spatial scale of grazing heterogeneity. However, an identical value of herbage utilization M can result from an infinite number of different patterns of small scale, or grid-cell level patterns of
grazing heterogeneity. In order to study the effects of
different small scale grazing patterns, we distinguish
between three scenarios of grazing heterogeneity, as
originally introduced in Weber et al. (1998). In the
first scenario, the modifier r in Equation (5) is drawn
randomly from an even distribution in [0, 2]. In a second scenario, we draw r from a normal distribution
with mean 1 and standard deviation 0.1. In a third scenario, r is also normally distributed, but we exclude
repeated site selection until most of the available sites
have been selected once, thus enforcing on most of the
forage sites a minimum defoliation frequency of one
event per year. Grazing heterogeneity in these three
scenarios will be referred to as ‘high’, ‘moderate’ and
‘low’, respectively. For herbage utilization of 50%,
Figure 2 shows the resulting distributions of cell based
annual defoliation severity. In the scenarios of high or
moderate grazing heterogeneity (Figures 2A and 2B),
about one fourth of all cells containing herbage remain almost ungrazed. The two scenarios differ in the
fraction of sites subject to extremely high defoliation
severity. Whereas under high heterogeneity about 15%
of the grass cells are grouped in the highest utilization
class, under moderate heterogeneity no more than 2%
are found in this class. Under both of these scenarios,
very small fractions of the range experience defoliation severities close to the level of herbage utilization
M. Contrarily, under the scenario of low grazing heterogeneity (Figure 2C), most of the range is subject
to defoliation severities close to the level of herbage
utilization M.
In our model, grid-cells are subject to grazing at
individual severities, and thus their size defines the
83
1947; Martens 1971; Hart et al. 1993; Pickup &
Chewings 1994). In order to study management effects
under a large scale pattern of grazing heterogeneity, we introduce a grazing gradient with distance
from water controlling local defoliation intensity of
grazing events as described in Jeltsch et al. (1997b);
defoliation intensity de is given as
de = min(1, M · r · 2 exp(−0.0025l)),
(6)
with l representing distance from water (number of
grid-cells). We assumed a linear water source at one
of the short edges of a rectangular grid of 40 × 640
grid-cells (Jeltsch et al. 1997b). For this gradient scenario the additional trampling mortality factor mt = exp(−0.05l) was introduced (Jeltsch et al.
1997b) to account for increased trampling mortality due to higher livestock density around the water
source.
Simulations
Figure 2. Frequency distribution of local herbage utilization under
three different scenarios of grazing heterogeneity, and an overall
herbage utilization of M = 50%. Heterogeneity levels: (A) high,
(B) moderate, (C) low.
spatial scale of grazing heterogeneity. Drawing parallels between grid-cells and grazing patches, the order
of magnitude of patch size in our model matches
with empirical results by Ring et al. (1985) and Fuls
(1992). Under high and moderate grazing heterogeneity, ‘patches’ of low grazing severity contribute a
significant share to the mosaic of grazing sites on the
range. Under high grazing heterogeneity, the same
holds for patches of extremely high defoliation severity. The distributions depicted in Figure 2, however,
are not static with respect to herbage utilization M.
Hence, under fixed stocking, fluctuations in herbage
utilization M, which are due to inter-annual variability
of herbage production, result in shifts in the distribution of local defoliation severity (Weber et al.
1998).
Large scale grazing heterogeneity
Sheep and cattle need to drink water daily or every
other day. Therefore, grazing intensity decreases with
increasing distance from surface water. The resulting
grazing gradient can extend over several kilometers
and is prevalent in semiarid rangelands (Valentine
In order to investigate management impacts on temporal dynamics under a particular climatic data set,
we present single simulation runs over 50 years under three different utilization intensities for both fixed
and adaptive stocking strategies. Management effects
under a large scale grazing gradient will also be examined with a single simulation run for each of the two
management strategies. The grazing gradient simulations were run at a utilization intensity equivalent to
a mean stocking rate of 8 large stock units per km2
(lsu km−2 ).
To assess the impact of restrictions in the ability
to adjust stock numbers under the adaptive stocking
strategy, we compare a number of restricted adaptive
strategies with the fully adaptive and fixed stocking
strategies. To this end we present, for a particular climatic data set, results of single simulations run under
alternative stocking strategies. Herbage utilization was
set at levels resulting in approximately equal mean
stocking rates of 9 lsu km−2 for all of the strategies.
The factors management, grazing heterogeneity
and utilization intensity were then analyzed in a factorial simulation experiment. We ran each factor combination with a set of four series of daily rainfall data,
and replicated the simulations for each rainfall set
(n = 25). Simulations were initialized with a map
representing rangeland in good condition with relative
grid-cell cover of ca. 2%, 85%, and 13% for annuals,
perennial grasses and herbs, and shrubs, respectively.
We generated this initial map by grazing a randomly
84
Figure 3. Constant stocking strategy: impact of stocking rate on temporal dynamics of perennial grass cover, shrub cover, and herbage
utilization (left column), and on herbage production and consumption (right column). Results depict three single simulations run with identical
random number sequences. Spatial grazing heterogeneity was high.
initialized grid with 10%, 70%, and 15% relative
cover over 20 years at a moderate stocking rate of
5 lsu km−2 . Due to the colonization pattern of shrubs,
i.e. higher colonization probabilities for sites adjacent
to shrub occupied grid-cells, the initial cover showed
a clumped distribution of shrubs. Note that simulated
cover refers to the fraction of grid-cells occupied by a
life form.
45% cover (Table 1). Under 10 lsu km−2 (Figure 3 top
row), grass cover decreased rapidly, and a sequence
of low rainfall years at the end of the second decade
resulted in a breakdown, followed by total removal
of perennial grasses during the third decade. Consequently, in the final shrub dominated phase, herbage
production decreased to less than 250 kg ha−1 maintained mainly due to annuals. Obviously, this scenario
of over-grazing caused the transition of the range from
a savanna to shrubland.
Results and discussion of simulations
Sample simulations – no grazing gradient
Constant stocking
Stocking at a rate of 7 lsu km−2 , no directional
change occurred in shrub and grass cover (Figure 3
lower row). Annual herbage production showed large
fluctuations reflecting high variability of rainfall. At
9 lsu km−2 (Figure 3 middle row), a slow decrease
of grass cover occurred during the first four decades,
accompanied by a doubling in shrub cover. In the fifth
decade, grass cover was diminished to a degree that
a number of low rainfall years with herbage production of less than 1000 kg ha−1 caused a breakdown in
grass cover, followed by an increase of shrubs up to
Adaptive stocking
Stocking at a herbage utilization of 20%, grass and
shrub cover showed no directional change (Figure 4
lower row). With an overall mean consumption of
260 kg ha−1 , this utilization level is equivalent to
a mean stocking rate of 7.2 lsu km−2 (Table 1).
Under 30% utilization (Figure 4 middle row), a
slight decrease of grass and an increase of shrub
cover occurred. With an overall mean consumption of
350 kg ha−1 , this utilization level resulted in a mean
stocking rate of 9.7 lsu km−2 . Under 40% utilization
(Figure 4 top row), a steady decline of grass cover
and a steady increase of shrub cover occurred. Consequently, herbage production showed a negative trend.
However, after 50 years of utilization, grass cover
85
Figure 4. Adaptive stocking strategy: impact of herbage utilization on temporal dynamics of perennial grass cover, shrub cover, and stocking
rate (left column), and on herbage production and consumption (right column). Results depict three single simulations run with identical random
number sequences. Spatial grazing heterogeneity was high.
was not yet completely removed, and mean equivalent
stocking rate was still 9.1 lsu km−2 .
Constant versus adaptive stocking
At low utilization intensities, the two management
strategies did not differ either in terms of final community composition or in rates of herbage production
(Table 1). With a 50% increase in utilization intensity
(M = 30%), adaptive stocking resulted in a mean
equivalent stocking rate of 9.7 lsu km−2 and did not
cause a severe deterioration of range condition. In
contrast, fixed stocking at 9 lsu km2 caused degradation to a level that would exclude further utilization
of the range. Out of the three sample runs, the maximum acceptable utilization intensity under constant
stocking would be 7 lsu km−2 , whereas for adaptive
stocking a herbage utilization of 30% would be acceptable, yielding a mean equivalent stocking rate of
9.7 lsu km−2 . To summarize, the sample runs indicate two major differences in vegetation response to
the stocking strategies. First, under adaptive stocking,
maximum acceptable levels of utilization intensity are
higher than for fixed stocking. Second, the response by
the vegetation to ‘over-grazing’ is qualitatively different for the two strategies. Over-stocking with the fixed
strategy initially produces a slow change in community composition, followed by a period of rapid change
producing a decline in perennial grass cover and an
increase in shrub cover. In contrast, over-stocking under the adaptive strategy results in a gradual change in
community composition over time.
Sample simulations – with grazing gradient
Under a grazing gradient, stocking strategy showed
a large effect on community composition of range
vegetation (Figure 5) as well as on vegetation zonation (Figure 6). Under fixed stocking, overall cover
by perennial grasses decreased and shrub cover increased throughout the simulation (Figure 5), and after
50 years of fixed stocking a large zone of greatly increased shrub cover had developed (Figure 6B). In
contrast, under adaptive stocking, changes in overall cover by either grasses or shrubs remained small,
without showing clear trends (Figure 5). The zone
of increased shrub cover remained limited to areas
close to the water source, and shrub density did not
reach the high levels occurring under fixed stocking
(Figure 6C). Thus, in this example, stocking strategy
determined not only whether grazing had detrimental long-term effects on community composition, but
86
Table 1. Six sample simulation runs under constant and adaptive stocking at three different utilization intensities. S, tocking rate; M, herbage utilization; Seq , mean equivalent stocking rate = total
herbage consumption / total forage demand.
Strategy
Utilization intensity
S
M
Seq
lsu km−2
%
lsu km−2
Mean herbage
Production
Consumption
kg ha−1
kg ha−1
Final cover
Grasses
Shrubs
%
%
Fixed
7
9
10
20
31
46
7.0
9.0
7.0
1289
1043
560
255
327
256
84
35
0
14
45
75
Adaptive
–
–
–
20
30
40
7.2
9.7
9.1
1307
1177
832
261
353
331
83
73
35
15
23
53
Sample simulations – level of adaptivity
Figure 5. Perennial grass (A) and shrub cover (B) under a water
point grazing gradient by time, and stocking stragegy. Management inputs were a stocking rate of 8 lsu km−2 , and a mean
herbage utilization of M = 23% for fixed and adaptive stocking,
respectively. Annual means of herbage consumption and production
were 291 kg ha−1 out of 1135 kg ha−1 , and 288 kg ha−1 out of
1256 kg ha−1 for fixed, and adaptive stocking, respectively. Thus,
mean equivalent stocking rate was 8 lsu km−2 for both stocking
strategies. Grazing heterogeneity was high for both management
strategies. For initial and final shrub cover maps see Figure 6.
also whether the particular grazing conditions lead to
a disadvantageous spatial structuring of the vegetation. Both detrimental changes did occur under fixed
stocking, but were avoided under adaptive stocking.
We examined the impact of altering stocking strategies
along a continuum, from being completely adaptive
to being completely fixed, by restricting the degree
to which stock levels could be altered in any given
year in five separate model runs that had comparable
mean stocking rates of 9 lsu km−2 (Figure 7). Under
fixed stocking, grass cover was reduced by more than
half after 50 years of grazing, whereas shrub cover
more than doubled. Under fully adaptive stocking,
there were few effects of grazing on either grass or
shrub cover. When changes in stocking rate were restricted to ±10% per year, the changes in vegetation
cover affected by grazing were intermediate between
the fixed and fully adaptive strategies. However, final grass cover was 68% which is more than twice as
high as under fixed stocking. When stock reductions
up to 50% per year were allowed, vegetation dynamics were very close to the sequence observed under
fully adaptive stocking. These results demonstrate that
perfect tracking of primary production is not a prerequisite for reducing the ecological risk of grazing by
means of an adaptive stocking strategy. In particular
the ability for drastic destocking – here 50% annually
– holds long-term ecological benefits over fixed stocking. It furthermore indicates that potential increases in
stock numbers of 10% annually provide sufficient flexibility for a beneficial tracking of fluctuating primary
production.
Factorial simulation experiment
We systematically explored the consequences of altering utilization intensity and grazing heterogeneity
under the two extreme strategies, i.e. fully adaptive
87
Table 2. Effect of time horizon, spatial grazing heterogeneity, and management strategy on shrub cover doubling thresholds, and maximum yields. S, stocking rate; M, herbage utilization; Seq , mean equivalent stocking rate = total herbage
consumption / total forage demand; Meq , mean equivalent herbage utilization = total herbage consumption / total herbage
production.
Time
horizon
years
Spatial
grazing
heterogeneity
Management
Thresholds
S
M
lsu km−2
%
Seq
lsu km−2
Meq
%
Maximum yields
S
M
lsu km−2
%
Seq
lsu km−2
Meq
%
20
High
Fixed
Adaptive
Fixed
Adaptive
Fixed
Adaptive
9.6
–
11.6
–
14.6
–
–
40
–
46
–
65
9.6
11.2
11.6
12.8
14.6
17.8
31
40
39
46
50
65
11.0
–
12.4
–
17.2
–
–
40
–
46
–
70
10.6
11.2
12.1
12.8
15.4
18.2
43
40
47
46
66
70
Fixed
Adaptive
Fixed
Adaptive
Fixed
Adaptive
8.2
–
9.2
–
13.0
–
–
31
–
34
–
42
8.2
9.9
9.2
10.8
12.9
13.1
25
31
28
34
42
42
9.2
–
10.6
–
13.8
–
–
33
–
39
–
65
8.9
10.0
10.2
11.2
13.5
14.9
32
33
38
39
49
65
Moderate
Low
50
High
Moderate
Low
versus fixed stocking. Under both strategies, we observed a nonlinear response of shrub cover to changes
in utilization intensity (Figures 8A and 8B). At low
utilization intensities, shrub cover remained close to
the initial value of 13% cover. When utilization intensity exceeded some critical level, shrub cover increased rapidly under increasing utilization intensity.
We assessed the risk of shrub cover increase for each
of the factor combinations. To this end, we estimated
probabilities of shrub cover at least doubling within a
given time horizon with the relative frequency of this
change in the total set of 100 simulations per factor
combination. We assumed that not more than a 10%
chance of shrub cover at least doubling was acceptable. The according maximum acceptable utilization
intensities, will henceforth be referred to as threshold utilization intensities or simply ‘thresholds’. Time
horizon of management, spatial grazing heterogeneity,
as well as stocking strategy influenced this threshold
(Table 2).
Time horizon of management
For both stocking strategies, thresholds decrease
with increasing management time horizon (Table 2).
For example, for 20 years of fixed stocking under moderate grazing heterogeneity, the threshold is
11.6 lsu km−2 . However, at the 50-year time scale this
stocking rate results in a 100% chance of shrub cover
doubling. Moreover, with a mean expected shrub
cover of over 60%, the mean equivalent stocking rate
is reduced to merely 8.6 lsu km−2 . Hence, identifying
acceptable levels of utilization intensities based on a
short-term planning horizon leads to over-utilization at
the ecologically more relevant time scale of 50 years.
This, however, applies not only to the 20 versus 50year thresholds but also to the 50-year threshold in
relation to, for example, a 100-year threshold. Hence,
our thresholds define risks only for the specified time
period, and should not be mistaken for an examination
of long-term sustainable levels of utilization.
Spatial grazing heterogeneity
Under both stocking strategies, thresholds were negatively related to grazing heterogeneity (Table 2, Figure 8). For example, under fixed stocking and low
grazing heterogeneity, the 50-year threshold was about
60% higher than under high grazing heterogeneity.
In our model, grazing ultimately acts as a local disturbance, rendering grass sites available for shrub
colonization, when grass density is reduced below a
minimum. Thus, with respect to shrub establishment,
the frequency of sites with high defoliation severity
is critical. In the three grazing scenarios studied, this
frequency increases with the level of grazing heterogeneity (Figure 2). Hence, under a given utilization
intensity, shrub establishment is favored by increasing
grazing heterogeneity, and consequently, ‘acceptable’
88
Figure 6. Simulated shrub cover maps under water point grazing gradient. Maps represent an area of 200 ∗ 3200 m. (A) initial map; (B) after
50 years of constant stocking at 8 lsu km−2 ; (C) after 50 years of adaptive stocking at a mean herbage utilization of M = 23%. Grazing
heterogeneity was high for both management strategies. For respective time series of vegetation cover see Figure 5.
livestock would increasingly accept sites of lower forage quality. Similarly, the fraction of heavily grazed
sites would increase with utilization intensity, creating
‘patch over-grazing’ (Fuls & Bosch 1991; Fuls 1992;
Kellner & Bosch 1992). Our grazing heterogeneity scenarios might thus be interpreted as representing rangelands of different levels of heterogeneity in
herbage quality. Considering the empirical evidence,
the most homogenous scenario, however, is unlikely
to be of high relevance in semiarid rangelands.
Figure 7. Grass and shrub cover dynamics under different stocking strategies with a mean stocking rate of 9 lsu km−2 . Adaptive:
herbage utilization M = 26%; restricted adaptive A: M = 28%,
up = 10%, down = 10%; B: M = 31%, up = 10%, down = 50%;
C: M = 31%, up = 50%, down = 50%. Results depict single
simulation runs under high grazing heterogeneity.
levels of utilization intensity are negatively related to
grazing heterogeneity.
Although forage quality was not included in our
model, the generated patterns of grazing heterogeneity
can be explained as a result of qualitative selectivity
due to spatial variability in forage quality, as discussed by Weber et al. (1998). Given a spatially
heterogeneous distribution of herbage quality, livestock would mainly select high quality sites, leaving
low quality sites almost ungrazed under moderate utilization intensity. With increasing utilization intensity,
the fraction of ungrazed sites would decrease, since
Stocking strategy
For most combinations of planning time and grazing
heterogeneity, about 20% higher utilization intensities
could be maintained under adaptive stocking than under fixed stocking, without increasing the risk of shrub
cover doubling (Table 2). There is also a qualitative
difference in the response by the vegetation to increasing levels of utilization intensity (Figure 8). Under
adaptive stocking, vegetation response is more gradual
with respect to increasing utilization intensities than
under fixed stocking (Figures 8A and 8B). For example, under fixed stocking and moderate heterogeneity,
a stocking rate of 12 lsu km−2 , which exceeds the 50year threshold by 30%, would bring a 100% risk of
shrub cover at least doubling, and mean equivalent
stocking rate would be reduced by 13%. Moreover,
expected shrub cover would exceed the initial cover
by a factor of five. Under adaptive management, a utilization intensity which exceeds the threshold by 30%
would also bring a 100% risk of shrub cover at least
doubling. However, mean stocking rate would remain
unaffected, and expected shrub cover would less than
triple. This more gradual response to over-utilization
is also demonstrated in the sample simulations; under
fixed stocking at 10 lsu km−2 (Figure 2, top row),
which is a utilization intensity that exceeds the thresh-
89
Figure 8. Constant (left column) versus adaptive (right column) stocking strategy: Mean shrub cover after 50 years (A, B), and mean herbage
consumption over 50 years (C, D) by stocking rate (A, C) or herbage utilization (B, D).
old by about 20%, grass cover breaks down after two
decades of grazing, and at the end of the third decade
shrubs cover three quarters of the range (Table 1). In
contrast, under adaptive stocking at 40% herbage utilization (Figure 4 top row), a level which also exceeds
the threshold by about 20%, grass cover decreases
at a more or less constant rate, and after 50 years
of grazing grass still covers one third of the range
(Table 1).
Our finding of superiority in the adaptive stocking strategy is consistent with our understanding of
the ecological processes driving the system. By determining the frequency of sites available for shrub
establishment, the fraction of heavily defoliated sites
is crucial for the effect of grazing. Under fixed stocking, years of low rainfall form forage bottlenecks
with increased herbage utilization and, consequently,
increased fractions of heavily defoliated sites are produced (Weber et al. 1998). When a subsequent year
of sufficient rainfall occurs shrub establishment is
favored. Thus, under fixed stocking, herbaceous vegetation suffers from a twofold stress in drought years:
first, drought reduces recruitment, and increases the
probability of local grass extinction; second, defoliation severity increases due to the increased herbage
utilization resulting from reduced overall herbage production. This negative feedback of overall herbage
production on herbage utilization initiates a third detri-
mental process: if grazing operates at an intensity that
causes an initially slight decrease in grass cover, this
decrease will also reduce herbage production. Production will be reduced more if woody vegetation replaces
grasses. Due to reduced production, herbage utilization increases and consequently so does the fraction
of heavily defoliated sites. Ultimately, this gradual
process leads to an increase in the frequency of years
acting as forage bottlenecks. Due to reduced herbage
production, the amount of rainfall required for production to remain high enough to avoid large fractions
of the range being heavily defoliated will increase.
Thus, fixed stocking increases the risk of range degradation by imposing a destabilizing feedback of rainfall
and herbage utilization onto the range. To the contrary, under adaptive stocking, the negative impact of
low rainfall periods on grass cover is less pronounced
since, by definition of our adaptive strategy, herbage
utilization is not affected and consequently the fraction
of heavily defoliated sites remains constant. Hence,
the ecological advantage of adaptive stocking is due
to the less damaging effect of forage bottlenecks in
low rainfall years, and the absence of a destabilizing
feedback between rainfall and herbage utilization.
This difference in the interaction of rainfall and
grazing also explains the characteristic way in which
the two stocking strategies affect range vegetation.
Under fixed stocking at a critical utilization intensity,
90
we initially observe a gradual decrease in grass cover,
followed by an accelerated breakdown (Figure 3). Under adaptive stocking, such a breakdown does not
occur (Figure 4). Instead, we observe a constant negative trend in grass cover and herbage production. This
difference is also reflected in the more gradual change
of slopes at the utilization threshold (Figures 8A and
B). The increased elasticity of vegetation response
to increasing utilization intensities may be the most
important difference between the two strategies, considering that due to poor knowledge of spatial grazing
patterns, a manager generally would not know the true
level of the threshold. This uncertainty, and the tendency to increase utilization intensity for short-term
economic reasons can result in supercritical levels of
grazing pressure. Under adaptive stocking, the damage
affected by supercritical utilization intensities would
be limited due to slower rates of vegetation change.
The relative advantage of adaptive stocking was
affected by an interaction between time horizon and
grazing heterogeneity (Table 2). For the longer time
horizon, the ability to maintain a higher mean stocking
rate under adaptive stocking decreased with grazing
heterogeneity, and was negligible under low grazing
heterogeneity. For the shorter time horizon, the advantage of adaptive stocking decreased from high to moderate levels of grazing heterogeneity, but increased
from moderate to low levels. This interaction is due
to the relatively slow response of shrub cover to heavy
grazing, and the relationship between herbage utilization and the fraction of heavily grazed sites, which
differs between the heterogeneity scenarios. Under
heterogeneous grazing with fixed stocking rates, the
fraction of heavily grazed sites is highly sensitive to
herbage utilization, whereas under low heterogeneity, this fraction is rather insensitive within a wide
range of herbage utilization (cf., Weber et al. 1998).
Thus, under highly heterogeneous grazing, even moderate stocking rates would frequently lead to damaging
levels of herbage utilization, due to rainfall driven
fluctuations of herbage production. In contrast, under low heterogeneity, considerably higher stocking
rates would be required to produce the same damage.
Under the adaptive strategy, the fraction of heavily
grazed sites is not affected by fluctuations of herbage
production and is specific for each heterogeneity scenario. Therefore, the relative advantage of adaptive
over fixed stocking decreases with grazing heterogeneity. However, this does not hold for the short time
horizon, for which the advantage in terms of mean
stocking rate was highest for lowest heterogeneity. For
this short time horizon, the slow response time of
shrub cover masked the long-term effects of grazing,
and resulted in an ecologically flawed ranking due to
the extremely high utilization intensities.
A further aspect relevant for the ecological ranking
of management strategies is the different functional
response of mean herbage consumption to utilization intensity (Figures 8C and 8D). At low levels
of utilization intensity, mean herbage consumption
increases linearly with utilization intensity, since community composition and herbage production remain
unaffected. When community composition is increasingly altered towards a higher shrub cover, herbage
production decreases. Under fixed stocking, herbage
consumption increases linearly until periods of forage deficit occur. Any further increase of the stocking rate results in further decreases of herbage production that cannot be offset by herbage utilization,
once 100% utilization have been reached. In this
case, further increases of the stocking rate reduce
mean herbage consumption. Contrarily, under adaptive stocking, herbage utilization is fixed and thus,
decreasing herbage production directly affects herbage
consumption. Thus, we observe a more gradual decrease of mean herbage consumption with increasing
utilization intensity due to the absence of negative
feedback between herbage production and herbage
utilization. Furthermore, the two stocking strategies
differ in the relations between the thresholds and the
utilization intensities maximizing yield measured in
terms of mean herbage consumption (Figures 8C and
D) or mean equivalent stocking rate (Table 2). Under
adaptive stocking, utilization intensity for maximum
yield is identical to, or at least very close to the threshold for most factor combinations (Table 2). Hence,
managing for an ecological target of limited degradation risk comes with the economic benefit of also
maximizing yield. Moreover, if management fails to
fine-tune utilization intensity to the very optimum
level, yield loss would be rather small, due to the
low slopes of the response curve around the optimum
(Figure 8D). In contrast, under fixed stocking, utilization intensity for maximum yield is about 10%
higher than the threshold for most time-heterogeneity
combinations. Hence, under fixed stocking, the ecological target of a limited degradation risk conflicts
with the economic target of maximizing yield in terms
of consumed herbage biomass. A longer time horizon
of management would inevitably resolve this conflict
of ecological and economical targets. However, under
realistic planning horizons, this conflict is real and
91
dangerous, as it is possibly one of the main causes of
over-utilization of semiarid rangelands.
General discussion
Understanding the effects of grazing on range vegetation is a prerequisite for long-term assessments of livestock management strategies (Stafford Smith 1996).
This understanding, however, cannot be achieved
without accounting for the determinants of both range
vegetation dynamics and grazing impacts. Here, we
have reported a first approach towards assessing alternative livestock management strategies for a savanna
rangeland with a spatially explicit model including
life history traits and resource competition aspects of
floristic components, rainfall, fire and grazing. The
identified thresholds of utilization intensity and their
determinants need to be evaluated within the limitations of our approach, the results of which hold
important management implications.
Thresholds and their determinants
Under both stocking strategies, we observed a nonlinear response of shrub cover to increasing grazing
utilization. Shrub cover response and its most characteristic feature – a threshold level of utilization
intensity above which grazing resulted in high shrub
cover – were highly sensitive to a number of factors. Therefore, after discussing the magnitudes of the
identified thresholds, we will focus on the impacts of
grazing heterogeneity, planning horizon and stocking
strategy.
Threshold magnitude
The thresholds we have identified through our model
offer a possibility of testing it against empirical data.
If utilization intensities in our field site, or in other
comparable rangelands, are higher than our threshold, and are so without invoking detrimental changes,
we would have to reevaluate, at the very least, the
parameter values used in our model. Based on a review of field studies, Holechek et al. (1989, p. 192)
recommended utilization intensities of 35–45% for
semiarid ranges in the USA where shrub encroachment was not currently a problem. For ranges in the
southwestern USA, however, use should not exceed
25–35% of grazable forage in order to maintain longterm forage production (Holechek et al. 1989). Except
for our most homogenous grazing scenario, this corresponds quite well to the 50-year thresholds of 25–34%
mean equivalent herbage utilization predicted by our
model (Table 2). For our specific study area, about
7–9 lsu km−2 are recommended (Field 1977; Fourie
et al. 1985), equivalent to a utilization of 22–27%
of mean herbage production, which is approximately
1200 kg ha−1 . Thus, regional recommendations correspond to our 50-year thresholds for fixed stocking with
8.2 and 9.2 lsu km−2 for high and moderate grazing
heterogeneity, respectively (Table 2).
The close match of regional recommendations with
our thresholds seemingly contradicts reports of increasing shrub cover in our study area (Donaldson
1969). Considering that utilization intensities exceeding the threshold by only small amounts will result
in greatly increased shrub cover, this seeming contradiction is resolved by a comparison of recommended
and actual levels of utilization. The latter frequently
exceed the former in the Molopo area of the southern
Kalahari (Thomas & Shaw 1991). Moreover, at the
global scale a meta-analysis including over 200 studies
(Milchunas & Lauenroth 1993) yielded mean utilization intensities of 45% for grasslands and 55% for
ranges with a woody vegetation component. Hence,
we conclude that our results are consistent with both
the detrimental grazing effects in our study area and –
interpreting our model as a representative example of a
savanna rangeland – the detrimental changes observed
in semiarid rangelands globally.
Grazing heterogeneity
We found that grazing heterogeneity operating at
scales comparable to empirically reported patch grazing patterns (Ring et al. 1985; Fuls 1992) determines
the level of the grazing thresholds for a given stocking
strategy and a given time horizon. This result bares
on rangeland dynamics since empirical evidence suggests that grazing is not homogeneously distributed in
semiarid ranges (Owens et al. 1991; Kellner & Bosch
1992). Heterogeneity in soil properties (Schlesinger
et al. 1990), particularly infiltration capacities (MacDonald 1978), and relief-dependent redistribution of
rainfall inputs (Noy-Meir 1981), generates heterogeneity in local water availability and thus in forage
quantity and quality, due to phenological and floristic differentiation (Gammon 1978; MacDonald 1978).
Hence, livestock encounters heterogeneity in forage
quantity and quality, which are two major determinants of site selection for grazing (Senft et al. 1985). In
addition to predefined physical, chemical, and floristic
heterogeneity, physiological requirements and the behavior of domestic ruminants affect the grazing impact
92
(Hobbs 1996) producing a heterogeneous pattern that
can be considered an intrinsic property of semiarid
grazing systems. However, quantitative information
on spatial patterns of grazing utilization is generally
poor (Coughenour 1991) and thus poses a limitation
for the assessment of grazing impacts (Weber et al.
1998), and consequently for management strategies.
Planning horizon
Rangeland management requires balancing the competing demands of livestock and the state of the range,
which essentially represents a trade-off between the
present and the future (Wilson 1996). The resulting
conflict between the short-term goal of increasing livestock production and the long-term conservation of
rangeland productivity reflects the ultimate dilemma
of rangeland management. Our results highlight this
dilemma: even with a horizon of 20 years, which
is not what any manager would agree to be shortterm planning, the levels of ‘acceptable’ utilization
intensity are not sustainable and result in drastically
increased shrub cover when applied over longer time
spans (Table 2, Figure 8). Thus, risk assessments
of management options – here, the choice of stocking strategy and utilization intensity – must be based
on long-term planning horizons in order to be ecologically meaningful. The critical impact of varying
the time horizon of management decisions was also
documented in a simulation study comparing recommended stocking rates along a rainfall gradient in the
Kalahari by Jeltsch et al. (1997a). They found that,
over a 20 year period, recommended stocking rates
affected little change, but over a 100-year period, large
increases in shrub cover occurred. Ultimately, at long
time scales, only ecologically sound levels of utilization intensity allow sustained livestock production,
and thus, at long time scales the conflict of ecological and economical considerations is resolved. As of
yet, these long time scales are rarely considered in
rangeland management.
Stocking strategy
Under adaptive stocking, levels of acceptable utilization intensities were generally higher than under fixed
stocking. Thus, adaptive stocking allows higher utilization intensities without increasing the risk of detrimental grazing impacts, provided that grazing distribution is not homogenous. Although other studies
comparing fixed and adaptive stocking strategies did
not consider long-term effects of grazing on range productivity, a comparison with our results is interesting.
Increased returns of adaptive stocking, as compared
to fixed stocking, have been reported from Riechers
et al. (1989) and Stafford Smith & Foran (1992). To
the contrary, Illius et al. (1998) reported that adaptive
stocking decreased mean stocking rate as compared
to fixed stocking, although annual sales were not affected. Illius et al. (1998) simulated woody browse
and perennial grass dynamics, and based animal demography on a physiological model driven by diet
composition. Contrary to the annual time steps used
by Riechers et al. (1989), Stafford Smith and Foran
(1992) and our model, the model of Illius et al. (1998)
operates on a daily basis. Furthermore, in the models
with the coarser temporal resolution, stock numbers
are simply adjusted to the available amount of forage.
Thus, the latter group represents models in which lagfree adaptation through management is more or less
given, whereas in the study by Illius et al. the degree of
adaptation depends on both management activity and
the outcome of a detailed model of animal demography. Therefore, Illius et al.’s observation that stock
numbers tended to lag behind climatic fluctuations,
and their argument that destocking can be effective
only if the productive potential of the herd can be
re-established more rapidly then is possible from depleted herd resources, might explain the contradictory
results.
In comparing fixed versus adaptive stocking, we
examined two strategies representing the extreme ends
of a continuum of management options (Stafford
Smith 1996). Even if fixed stocking rates cannot be
fully achieved in reality, our model is a viable simplification of the management goal of keeping stock
numbers close to a ‘carrying capacity’. For adaptive stocking, what can be achieved, in reality, is
constrained by time lags in the response to changes
in forage availability and limitations in the rates of
change in stock numbers. However, since the current year’s forage biomass can be assessed after the
termination of a distinct rainy season, lag free adaptation for a model operating at annual time steps is not
an unrealistic assumption, and has also been studied
by other authors (e.g., Riechers et al. 1989; Stafford
Smith & Foran 1992). As for restricted rates of change
in stock numbers, our sample runs showed that unrestricted adjustment to the available forage biomass is
not a prerequisite for improved performance of adaptive stocking. This is corroborated by a model analysis
for Australian rangelands (Stafford Smith & Foran
1992), which compared the economic performance of
two adaptive stocking strategies (20% and 40% de-
93
stocking in drought) with a non-adaptive control. In
this study, the economic performance ranking of different levels of adaptivity was highly dependent on the
weather sequence, but both adaptive strategies were
superior to the non-adaptive control (Stafford Smith
& Foran 1992). Although the relationship between the
level of adaptivity and the ecological and economic
performance of a stocking strategy is highly complex,
we suggest that perfect tracking of primary production
is not necessarily the best strategy. On the one hand, in
particular the ability for substantial destocking seems
a beneficial trait of the adaptive strategy; on the other
hand, for restocking, smaller rates of stock number
increase after drought might promote faster recovery
of primary production, e.g., due to improved replenishment of carbohydrate reserves in perennial grasses,
or improved recruitment resulting from increased seed
production. Therefore, we expect an optimum performance at some restricted level of adaptivity. This
optimum, however, would be specific for particular
sets of climatic, and floristic conditions rather than
universal for as broad a range of ecological conditions
as found in semiarid savannas.
Management implications
A prerequisite for an assessment of management
strategies are clearly defined management objectives.
Under the objectives of maximizing range output at a
given level of degradation risk we studied alternative
stocking strategies. Thus, our approach included both
an ecological as well as a production oriented goal. We
showed that for both strategies, a level of utilization
intensity exists under which both goals are achieved.
This threshold utilization intensity, however, is not a
fixed ecological property of the range but a variable
highly sensitive to the planning horizon of management, the spatial heterogeneity of the grazing pattern,
and the stocking strategy.
Stocking at the thresholds identified for the 20year planning horizon failed to achieve either of the
goals at the 50-year time scale, and resulted in shrub
dominated ranges under both strategies. Thus, our
results show that the management trade-off between
the present and the future can lead to ecologically
harmful levels of utilization intensity, even under a
management horizon as long as 20 years. Because
removal of livestock did not affect recovery of community composition in time spans relevant for management (Jeltsch et al. 1997b) the consequences of
over-utilization are at best difficult and very costly to
reverse. Therefore, we conclude that long-term goals
for range ‘condition’ need be assigned a higher priority in the goal hierarchy determining the planning
process in range management.
With respect to increasing shrub cover, thresholds
of utilization intensity existed under both stocking
strategies. Thus, for both strategies, identification of
appropriate utilization intensities is a central issue.
To that end, the predominating spatial patterns of the
grazing impact need be accounted for, due to the large
effect of grazing heterogeneity on threshold levels
(Weber et al. 1998). Our results confirm the view that
in rangelands prone to shrub cover increase, a spatially
uniform grazing distribution is desirable (Owens et al.
1991). However, for the patch scale, the effectiveness
of management systems designed to increase homogeneity of the grazing pattern has yet to be established
(O’Reagain & Turner 1992), and the patchy pattern
of the grazing impact might not be open to management control to a sufficient degree. Our results also
showed that ignoring the heterogeneous pattern of the
grazing impact increases the risk of range degradation due to the effects of patch-overgrazing (Kellner &
Bosch 1992). Hence, management aiming at sustainable land use has to account for grazing heterogeneity, and research institutions and extension agencies
working towards recommendations on utilization intensities should explicitly consider the effect of spatial
heterogeneity.
Whereas a comparison of the range succession
and state-and-transition models of rangeland dynamics was beyond the scope of this study, our evaluation
of the two stocking strategies derived from these conceptual models unequivocally showed the ecological
advantages of adaptive over fixed stocking. What does
this ecological advantage mean for rangeland management? Two aspects need be considered: first, the
relation between the additional cost and the additional
benefit of an adaptive strategy; second, the altered
dynamics of vegetation change due to the absence
of destabilizing feedback under adaptive stocking. As
for the first aspect, the adaptive strategy is preferable
if adjustment costs are lower than revenues from the
increase in mean stocking rate. As for the second aspect, the altered dynamics of vegetation change under
adaptive stocking could be seen merely as altered ‘rundown dynamics’ under over-stocking. Of course, an
increased run-down time (Mentis et al. 1989) cannot
be a target in sustainable range management. However, under adaptive over-stocking, the danger of an
abrupt increase in the rate of vegetation change was
94
avoided, whereas it characterized change under fixed
over-stocking. Thus, under the adaptive strategy, there
is more time to respond once a degrading change
in range vegetation has been detected. Moreover,
since annual adjustment of stocking rates characterizes the adaptive strategy, an appropriate management
response would be much more likely, than under
fixed stocking which is guided by the idea of keeping
stocking rates constant. Hence, under spatially heterogeneous grazing, adaptive stocking reduces the risk
of range degradation and allows increased utilization
intensities compared to fixed stocking.
Conclusions
We presented the first spatially explicit model that integrates small scale spatial components of both vegetation dynamics and grazing utilization for a long-term
assessment of alternative stocking strategies in livestock management. Our results showed that next to utilization intensity, grazing heterogeneity and stocking
strategy determine the response of rangeland vegetation to grazing. Given a heterogeneous grazing pattern,
adaptive stocking allowed a higher utilization intensity
than fixed stocking without inflating the risk of shrub
cover increase. In addition, adaptive stocking lacked
the ecologically most critical trait of fixed stocking,
which is the negative feedback between the amount
of annual rainfall and herbage utilization by livestock.
We conclude that due to this negative feedback and
the high variability of annual rainfall, range vegetation under fixed stocking is more sensitive to grazing
impacts in general, and in particular to grazing at utilization intensities exceeding the maximum acceptable
levels, that is to ‘over-grazing’. Thus, adaptive stocking allows not only a risk neutral increase of utilization
intensity, but also, degradation risks at utilization intensities exceeding the threshold levels are smaller
than under fixed stocking. This ecologically beneficial trait of adaptive stocking is particularly important,
since in reality, thresholds of utilization intensity are
poorly known, and short-term economic management
goals often favor high utilization intensities.
The ecological advantage of adaptive stocking in
semiarid rangelands is unequivocal and arises from
the combination of (1) high rainfall variability driving
annual primary production, (2) spatially heterogeneous grazing patterns, and (3) the different nature
of feedback under the two stocking strategies. How
this ecological advantage translates into specific rec-
ommendations for range management requires further
studies considering the diversity of goals in rangeland
management (cf., (Stafford Smith et al. 1994) as well
as a more detailed analysis of restrictions in the adjustment of stock numbers, possibly including herd
dynamics (cf. Stafford Smith 1996; Illius et al. 1998).
However, an all inclusive approach catering to spatial
dynamics of vegetation and herbivore impacts, as well
as feedbacks on herbivore population dynamics, might
easily result in a model of high complexity incompatible with limited observational data and limited scientific knowledge. Therefore, reductionism has been the
predominant approach to coping with complex realities. Nevertheless, reductionist models ignoring e.g.
feedbacks of herbivory and primary productivity in
general, or the spatially heterogeneous structure of
rangelands and herbivore impacts in particular, have
restricted our understanding of managed grazing systems, and our ability to assess management strategies.
Spatially explicit models are promising and timely
tools for increasing our understanding of long-term
dynamics in managed rangelands.
Acknowledgement
We thank Thomas Stephan and Sue Milton for helpful
comments on the manuscript.
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