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Characterizing commercial cattle farms in Namibia: Risk, management, and sustainability

Vol. 11(41), pp. 4109-4120, 13 October, 2016
DOI: 10.5897/AJAR2016.10981
Article Number: 9D440FC61131
ISSN 1991-637X
Copyright ©2016
Author(s) retain the copyright of this article
African Journal of Agricultural
Full Length Research Paper
Characterizing commercial cattle farms in Namibia:
Risk, management, and sustainability
Roland Olbrich1, Martin F. Quaas2 and Stefan Baumgärtner1,2*
1Department of Sustainability Science and Economics, Leuphana University of Lüneburg, Germany.
2Department of Economics, University of Kiel, Germany.
3Department of Environment and Natural Resources, University of Freiburg, Germany.
Received 10 March, 2016; Accepted 15 September, 2016
Commercial cattle farming in semi-arid regions is subject to high rainfall risk. At the same time, it is
prone to rangeland degradation. Theoretical works suggest that rainfall risk management by means of
financial instruments may stabilize farming-derived income over the short-term, but provides little
incentives for conservative rangeland management. Thus, the use of financial strategies of income
stabilization may accelerate rangeland degradation over the long term, as opposed to production or
organization strategies which may alternatively be used to stabilize farming incomes. In this paper, we
provide an empirical characterization of Namibian commercial cattle farming and explore the link
between risk, management, and sustainability by examining structural farm patterns with a cluster
analysis. Our data comes from a large-scale survey across the Namibian commercial cattle farming
area, to which 398 farmers responded. Our results show that the most distinct of the three identified
clusters is characterized by high sustainability and low financial risk management, and that it does not
differ from the remaining two clusters with respect to income. This suggests an inverse relationship
between financial risk management and sustainability, and thus supports theoretical insights.
Key words: Cattle farming, semi-arid rangelands, Namibia, empirical survey, rainfall risk, risk management,
A defining characteristic of semi-arid areas is low and
highly variable rainfall. Roughly 50% of the land in these
areas is used as rangeland for extensive livestock
farming (MEA, 2005), as this type of land use offers
sufficient flexibility to adapt to the challenging rainfall
conditions. However, even though livestock farming is
intended to deal with the variable rainfall conditions, it is
frequently unsustainable with 1020% of semi-arid areas
being degraded (MEA, 2005: 637, 640). One reason is
that livestock farming is often practiced as communal,
common-property farming systems where it may be
rational for farmers to “produce outcomes that are not in
*Corresponding author. E-mail: Tel: +49 761 203 3753. Fax: +49 761 203 3690.
Author(s) agree that this article remain permanently open access under the terms of the Creative Commons Attribution
License 4.0 International License
JEL-Classification: Q12, Q15, Q57
4110 Afr. J. Agric. Res.
anyone’s long-term interest” (Ostrom, 1999: 279).
However, degradation is also observed in commercial
farming systems where property-owning farmers
exclusively manage rangeland and may do so for
decades (de Klerk, 2004; Smit et al., 2015). One reason
for degradation in commercial systems is the use of
inadequate management strategies (Fynn and O'Connor,
2000; de Klerk, 2004; Wiegand, 2010; Kgosikoma et al.,
2012). More specifically, theoretical analyses suggest
that financial management strategies may stabilize
farming-derived income, but that farmers who apply
these strategies refrain from periodically resting their
rangeland and thereby ultimately overstock their land
(Quaas et al., 2007; Quaas and Baumgärtner, 2008,
2012; Baumgärtner and Quaas, 2009a; Müller et al.,
2011). Essentially, financial strategies thus trade-off the
short-term reduction of income risk against the system’s
long-term sustainability. The precise link between risk,
management and sustainability in commercial farming
systems is, however, poorly understood, not least of all
because comprehensive empirical data is lacking.
In this paper, we empirically characterize risk,
management and sustainability for commercial livestock
farming in semi-arid rangelands. Our case study is
commercial cattle farming in Namibia which constitutes
an economically important sector that contributes directly
1–2% to Namibia’s GDP (MAWF, 2009) and provides
employment for approximately 40,000 farmers and farm
workers (NTA, 2013: 15).
Like other semi-arid areas,
Namibian rangelands are subject to high rainfall risk
(Sweet, 1998) as well as to degradation in the form of
bush encroachment (de Klerk, 2004; Smit et al., 2015).
In August 2008, we conducted a large-scale survey
among 2,119 commercial cattle farmers through a mail-in
questionnaire (Olbrich et al., 2012).
We collected
information on 1) perceived rainfall risk, 2) risk
management strategies, 3) the farm’s sustainability, 4)
individual risk and time preferences and normative views
of sustainability, and 5) personal, farm and environmental
features. 398 farmers responded to the survey,
corresponding to a response rate of 19%. Here, we
analyse these data by providing descriptive statistics and
also by exploring structural farm patterns in a cluster
analysis for a subset of 108 farmers. We hypothesize
that risk, management and sustainability are intricately
linked for our case study along the lines of earlier
theoretical results described above.
The paper is organized as follows: First is a brief
description of commercial cattle farming in Namibia,
followed by a description of the data collection and the
analytical procedures. Thereafter the descriptive
statistics and results for the cluster analysis is presented,
discussed and the research concluded.
This amounts to 5.7% of total employment in Namibia (NSA, 2015: 6).
Although dating from 2008, our survey is (to our knowledge) still the most
recent and comprehensive of its kind in Namibia.
One prominent notion of sustainability is strong
sustainability: critical natural and economic components
of a system such as rangeland condition or farm
income have to be conserved at or above specified
thresholds, and have to be conserved independently of
each other (Pearce et al., 1989; Ekins et al., 2003).
Specifying sustainability thresholds is a normative
decision and may, like all normative decisions, occur at
the level of the individual or at the level of the society
(Schwartz, 1977; Stern, 2000; Baumgärtner and Quaas,
2009b; Young and Burke, 2010; Olbrich et al. 2014).
Once thresholds are specified, achieving sustainability
depends on choosing and applying adequate
management strategies (or, more generally, measures)
in order to comply with the thresholds (Baumgärtner and
Quaas, 2009b).
The management strategies that are ultimately
adequate may not be obvious at first, as different
strategies may all have beneficial effects over the short-
term. However, some strategies may ultimately be
detrimental for the system and lead to an unsustainable
development, which may become only obvious over the
long term.
In a series of theoretical studies, Quaas and
Baumgärtner (2008), Baumgärtner and Quaas (2009a)
and Müller et al. (2011) showed for Namibian rangelands
that conservative rangeland management that employ
resting strategies particularly adapted to variable rainfall
may provide a form of natural insurance, as it buffers the
negative effects of low rainfall events. Such strategies
that are aimed at the production process / farm
organisational levels and financial strategies are
substitute for reducing income risk and thus for
stabilizing income over the short term. Over the long-
term, however, financial strategies can be unsustainable:
farmers no longer have an incentive to use natural
insurance by means of conservative rangeland
management. Instead, they overstock their farms. This
slowly degrades their rangeland and eventually also
leads to considerably lower farm income.
System description of commercial cattle farming in
Commercial cattle farming is an extensive farming
system and economically the dominant livestock system
in Namibia: it contributes by far the largest share of total
agricultural output and approximately 12% directly to
GPD (MAWF, 2009: 7, 9).
An estimated 2,250
commercial cattle farmers (Olbrich et al., 2012) keep an
All subsequent figures from MAWF (2009) are calculated as averages over
the period 20002007.
Figure 1. Commercial cattle farms in Namibia. The dashed line
delimits what is considered the commercial cattle farming area
(Mendelsohn, 2006). Crosses denote the position of all 299
farms which were identified in our mail-in questionnaire.
average of 840,000 cattle (MAWF, 2009: 13). Of the
298,961 cattle that are on average marketed each year,
roughly half (49%) are sold as live cattle (almost
exclusively as weaners) whereas the other half (51%,
almost exclusively oxen) are sold as beef (MAWF, 2009:
14). Almost all weaners are exported as live cattle to
feed lots in South Africa (Schutz, 2010). Beef is primarily
sold to South Africa (45%), overseas (37%) and other
markets (3%) with only a small fraction consumed
domestically (15%) (MAWF, 2009: 14, 15). The
commercial cattle farming area in Namibia covers
approximately 14.5 million hectares (ha) (Mendelsohn,
2006: 42) of rangeland in the northern half of Namibia
(Figure 1). It is confined at its southern and western
fringes by areas too dry for farming and at its northern
and eastern fringes by communal lands. On average, the
commercial cattle farming area receives an annual
rainfall of only 374 mm, with 95% (352 mm) of rainfall
falling during the rainy season from November to April
(NMS, unpublished).
Rainfall is low on average and varies considerably,
both across rainy seasons (NMS, unpublished)
We refer here to the meteorological year, which is commonly defined from
July to June in southern Africa (e.g. Unganai, 1996; Burke, 1997). We define
the rainy season as the period 01st of November until 30th of April.
For example, the coefficient of variation for total annual rainfall shows a
value of 0.35 (NMS, unpublished). For comparison: the corresponding
Olbrich et al. 4111
across individual farms (Ward et al., 2004). The
rangeland’s production is rainfall-limited and strongly co-
varies with rainfall (Ward and Ngairorue, 2000; du
Plessis, 2001; Atlas of Namibia Project, 2002: Figure 2).
As such, precipitation risk directly transforms into
rangeland production risk. Since commercial cattle
farming in Namibia is extensive farming, the rangeland
production risk in turn translates into cattle production
and ultimately income risk.
Beyond being subject to low and highly variable
rainfall, rangelands of the commercial cattle farming area
are in an unsustainable state. They suffer from
degradation due to bush encroachment, i.e. they have
come to be dominated by woody vegetation (de Klerk,
2004; Joubert et al., 2008; Kgosikoma et al., 2012; Smit
et al., 2015). Bush encroachment entails a reduction in
the rangeland’s overall production which is equivalent to
a reduction in grazing capacity. A reduction in grazing
capacity, in turn, diminishes farm income (de Klerk,
2004; Lukomska et al., 2014) since grazing capacity
directly relates to the amount of cattle that may be
supported by the rangeland. Bush encroachment across
the commercial cattle farming region is illustrated by
grazing capacity being nowadays much worse than the
historic value of above 0.1 Large Stock Unit per hectare
(LSU/ha) that was encountered on average across
Namibia until the mid 1960s (de Klerk, 2004: 21).
Risk management strategies in cattle farming
As previously mentioned, farmers’ income is highly risky
since it is related to the high rainfall risk via rangeland
production and cattle production. Income may thus
change dramatically from year to year if no risk
management is conducted.
A farmer may manage the risk through a number of
risk management strategies. These strategies either
adjust the organization or production processes of the
farm (“on-farm strategies”) or makes use of financial
products or off-farm assets (“financial strategies”). The
main on-farm strategies are:
i) Increasing the rangeland size (“rangeland size
ii) Resting part of the rangeland to provide feed
throughout the year (“resting rangeland”),
iii) Providing cattle with purchased hay and licks
(“additional feed”),
iv) Choosing cattle breeds adapted to local
environmental conditions (“breed adaptation”),
v) Choosing a production system, such as weaner or ox
production, that is adapted to local environmental
conditions (“production system adaptation”);
and the main financial strategies are:
coefficient of variation is between 0.1 and 0.2 for countries in central and
northern Europe (Chapman, 2010: Map 2).
12 16 20 24
4112 Afr. J. Agric. Res.
i) agreeing on advances on livestock sales (“advances
on livestock sales”),
ii) keeping a checking account as a financial buffer
(“checking account as buffer”),
iii) taking up loans for covering operating losses (“loans
for covering operating losses”),
iv) obtaining income from off-farm sources (“off-farm
income”), and
v) investing into agricultural derivatives (“investment into
agricultural derivatives”).
Data sources
Here, we briefly describe the process and the questionnaire that we
used for the data collection. A detailed description of the data
collection can be found in Olbrich (2012) which also includes a
copy of the questionnaire.
Description of data collection
In August 2008, we sent out mail-in questionnaires to commercial
cattle farmers in Namibia in order to elicit 1) perceived rainfall risks,
2) risk management strategies, 3) the farm’s sustainability, 4)
individual risk and time preferences and normative views of
sustainability, and 5) personal, farm and environmental features.
Questionnaires were sent to a group of 2,119 farmers which
consisted of members of the Namibia Agricultural Union (NAU) and
of farmers that deliver cattle to MeatCo, the largest slaughterhouse
in Namibia. This group essentially is the whole population of
commercial cattle farmers in Namibia (Olbrich et al., 2012). We
mailed out questionnaires for the first time in the period 19th 21st
of August 2008, and a second time as a follow up on the 15th of
September 2008.
398 questionnaires were returned, equaling a return rate of
An optional question for identification of the farm was
answered by 299 (75.1%) of questionnaire participants.
Elicitation of rainfall risk, management and sustainability
We elicited rainfall risk by asking farmer to rate this risk on a six-
item Likert-scale ranging from “no risk at all” to “very high risk”.We
elicited risk management by asking farmers for each on-farm risk
management strategy (that is, rangeland size increase, resting
rangeland, additional feed, breed adaptation and production
system adaptation) and financial risk management strategy (that is,
advances on livestock sales, checking account as buffer, loans for
covering operating losses, off-farm income and investment into
agricultural derivatives) to self-report the importance they ascribe to
each strategy. Importance was recorded on a six-item Likert-scale
ranging from “not at all important” to “very important”.
We measured sustainability by the grazing capacity in the unit
LSU/ha, in line with our depiction of the rangeland system. Note
that we explicitly asked farmers to report the grazing capacity of
their rangeland (and that we did not simply calculate the stocking
To our knowledge, there exists no other comprehensive survey of
commercial cattle farmers in Namibia. We thus cannot validate the
representativeness of our sample by comparison with independently collected
data sets. However, we statistically compared independent subpopulations
within the sample and found only negligible differences at the 5% significance
level (Olbrich et al., 2009: Table 3).
Elicitation of risk and time preferences and normative views of
We elicited risk and time preferences by hypothetical choice
experiments in the questionnaire using a multiple-price-list format
(Olbrich et al., 2012). Based on this elicitation, we construct indices
for risk and time preference out of the raw responses in the
experiments. The risk preference index hereby is a discrete
variable with values in {1, 2, …, 7} where low values denote high
risk aversion and high values denote risk attraction. For the time
preference index we construct a discrete variable with values in {1,
2, …, 6} where low values denotes patience and high values
denote impatience.
In accordance with the definition of strong sustainability in
Section 2.1, we pre-selected ecosystem condition of the rangeland,
measured as grazing capacity in the unit LSU/ha, as one critical
component for conservation; and we selected income, measured
as net annual income in the unit N$, as an additional critical
economic component. We then elicited the threshold level at or
above which ecosystem condition (income) should be conserved.
In addition, we elicited two further normative views of sustainability
which are tied closely to the notion of strong sustainability.
the acceptable risk that the conservation of ecosystem condition
(income) fails in a given year, measured as a probability, and
secondly the time horizon for conservation of ecosystem condition
and income, measured in the generations.
Elicitation of personal, farm and environmental features
In addition to the aforementioned variables, we elicited a variety of
personal, farm and environmental features. A list of all elicited
variables along with their summary statistics is given in Table 1.
Statistics analysis
In a first step, we analyze data through the use of descriptive
statistics. Results are presented in the form of a summary table.
We can easily calculate the stocking rate from the questionnaire. Grazing
capacity and stocking rate are only moderately correlated (Pearson correlation:
r=0.49, p-Value<0.01, N=340), indicating that farmers indeed reported two
separate concepts.
As Olbrich et al. (2011b) detail, we encountered irregularities for some
farmers in the risk experiments, which we treated as artifacts and excluded in
our further analyses. Similar irregularities were encountered in the time
experiments and the respective observations were likewise excluded.
As these aspects are not central to this paper, we have for simplicity not
detailed the underlying conceptual framework in Section 2.1. This is explained
in detail in Baumgärtner and Quaas (2009b). To briefly summarize the main
point of that publication, we here point out that the notion of strong
sustainability as introduced in Section 2.1 assumes a deterministic system. In
such a system the effect of human measures (such as management strategies)
on the system’s development is fully known. However, many systems such as
cattle farming in Namibia are stochastic where unpredictable events (such as
stochastic rainfall) may occur and negatively impact on the system. This may
preclude the achievement of sustainability despite the best intentions for
setting sustainability thresholds and taking measures. Thus, in order to
consider sustainability in such systems, one also has to define 1) which risk
that conservation fails due to unpredictable events is still acceptable, and 2) the
time horizon over which the system should be conserved.
Altogether, more demanding views of sustainability are thus reflected in
higher values for the threshold level of ecosystem condition (income) and for
the time horizon, and in lower values for the acceptable risk that conservation
of ecosystem condition (income) fails.
Olbrich et al. 4113
Table 1. Summary statistics for 1) rainfall risk, 2) risks management strategies, 3) the farm’s sustainability, 4) risk and time preferences and
normative views of sustainability, and 5) personal, farm and environmental features.
Std. dev.
1) Rainfall risk
Rainfall [1=no risk, 6=very high risk]
2) Risk management strategies [1=not at all important, 6=very important]
On-farm management strategies
Additional feed
Production system adaptation
Breed adaptation
Resting rangeland
Rangeland size increase
Financial management strategies
Advances on livestock sales
Checking account as buffer
Loans for covering operating losses
Off-farm income
Investment into agricultural derivatives
3) Sustainability indicators
Grazing capacity [LSU/ha]
4) Preferences and normative views
Risk preference index [1=very risk averse, 5=risk neutrality, 7=very risk attracted]
Time preference index [1=very patient, 6=very impatient]
Sustainable annual net income [N$]
Sustainable ecosystem condition [LSU/ha]
Acceptable income risk [probability]
Acceptable ecosystem condition risk [probability]
Time horizon [generations]
5) Personal, farm and environmental features
Personal features
Household size [number of members]
Age [years]
Afrikaans [%]
Education level [1=no high school graduation, 6=Doctorate]
Farm features
Rangeland [hectare]
Land net rented a [hectare]
Single owners [%]
Oxen production [%]
Annual net income [1= <N$50,000, 5= >N$350,000]
Weekend farmer [%]
Environmental features
Average rainy season assessment (20042008) [1=very poor, 6=very good]
Land quality [1=very poor quality, 6=very good quality]
Actual bush cover [1=0%, 6=81 to 100%]
Optimal bush cover [%]
Displayed are mean, median, standard deviation, minimum and maximum for all continuous and Likert-scale measured.
4114 Afr. J. Agric. Res.
We then analyze characteristics jointly through a cluster analysis to
explore whether we may classify farms into similar groups.
Specifically, we conduct a hierarchical cluster analysis. We use
Ward’s method for agglomeration over an N x N dissimilarity matrix,
where N is the number of observations (Ward, 1963). The matrix
contains as elements the Gower dissimilarity measure between
observations which is designed to accommodate both continuous
and binary characteristics (Gower, 1971). It is defined as
ij w
where Dij is the dissimilarity between observation i and j as the sum
of the dissimilarities dijk between observation i and j with respect to
each characteristic k = {1, …, K} (StataCorp, 2007; Everitt et al.,
2011). wijk is a binary indicator that takes on the value 1 if
observations i and j have non-missing entries for characteristic k
and is 0 otherwise. We only include observations that have non-
missing entries for all K characteristics since all Dij are then
calculated over the same set of characteristics. Thus, wijk always
takes on the value 1, and the denominator equals K. Only including
observations with non-missing entries has the side effect of
reducing the sampling set to 108 observations, since not all farmers
responded to all questions.
The specification of dijk differs between binary and continuous
characteristics. For binary characteristics,
ijk 1
where xik and xjk are the values that characteristic k takes on for
observations i and j, respectively. For continuous characteristics,
)min()max( kk
ijk xx
which standardizes the absolute distance between xik and xjk by the
range of values that characteristic k takes on over all observations.
When calculating the Gower dissimilarity measure, highly
correlated characteristics may bias results as the impact of these
characteristics on the measure is overemphasized with respect to
the remaining characteristics (Backhaus et al., 2006: 550).
However, none of the 528 unique characteristics pairs (based on 33
characteristics over which we conduct the cluster analysis) display
a correlation coefficient above 0.6 and only 11 a correlation
coefficient above 0.4.
We chose the number of clusters by calculating the pseudo F
index (Calinski and Harabasz, 1974), where large values indicate a
good number of clusters, and the pseudo T squared Je(2)/Je(1)
index (“pseudo T squared index”) (Duda and Hart, 1973), where low
values indicated a good number of clusters, and by subsequently
identifying local maxima and minima, respectively. As a robustness
check we require that both indices display local optima at the same
number of clusters.
Subsequent to the cluster analysis, we examine in regards to
which characteristics the clusters differ significantly overall and
exactly which clusters are responsible for the significant difference.
For continuous characteristics, we thereto conduct one-way
analyses of variance (ANOVA) followed by pair-wise, Bonferroni-
corrected t-tests. For binary characteristics, we conduct Chi-square
tests followed by pair-wise, Bonferroni-corrected Chi-square tests.
All analyses are performed using the Stata/SE 10.1 statistical
software package.
Descriptive statistics
A comprehensive overview of descriptive statistics is
given in Table 1. Here we briefly discuss these statistics,
beginning with rainfall risk, risk management and
sustainability, and closing with farmers’ preferences,
normative views and personal, farm and environmental
Rainfall risk
The rainfall risk is rated above average with a value of
4.6 (out of 6.0) on the Likert scale. The risk is
heterogeneous across farmers as indicated by a standard
deviation of 1.2.
Risk management strategies
In terms of risk management, farmers predominantly
consider on-farm management strategies to be important
in the management of risky rangeland production.
Especially those on-farm strategies where the decision
process is in the hand of farmers are rated high, that is,
resting rangeland (4.7 on a six-item Likert-scale),
additional feed (4.7), breed adaptation (4.5) and
production system adaptation (4.4). In contrast, the
remaining on-farm strategy, rangeland size increase is
rated considerably lower (3.3), potentially because the
application of this strategy depends on third parties
offering land for sale or renting.
Financial risk management strategies are of less
importance. Checking accounts as buffer (4.7) and off-
farm income (4.0) are rated relatively high. In contrast,
farmers seem to be skeptical towards the remaining
financial management strategies: advances on livestock
sales (3.1), loans for covering operating losses (3.0) and
investment into agricultural derivatives (2.4) are among
the lowest rated strategies.
Heterogeneity in ratings across farmers is considerable
for most risk management strategies (standard deviations
of 1.6 to 1.8).
This finding is in accordance with our aforementioned
statement that strategies may (in part) be substitutes with
respect to risk management, which allows for
considerable leeway in whether individuals farmers apply
a specific strategy or not.
Average grazing capacity is 0.080 LSU/ha and is lower
than the historic 0.1 LSU/ha that was found on average
prior to the mid 1960s (de Klerk, 2004: 21). The rangeland
thus (on average) has not been managed sustainably.
However, grazing capacity is, with a standard deviation of
0.040 LSU/ha, highly variable across farms, suggesting
large differences in sustainability of individual farms.
Risk and time preferences
Farmers are on average risk averse, as indicated by a
value of 4.8 (out of 7.0) for the risk preference index. In
another study on the same database, the authors
calculate for the average farmer a point estimate for the
coefficient of relative risk aversion (CRRA) of 0.78 which
likewise indicates risk aversion (Olbrich 2012).
estimate is slightly higher than the value of 0.54 reported
for a field study of semi-subsistence farmers in Ethiopia,
India and Uganda by Harrison et al. (2010), but in range
with the value of 0.79 provided for the Danish population
by Andersen et al. (2006).
Farmers are of intermediate impatience as exemplified
by a value of 3.2 (out of 6.0) for the time preference
index. Calculating point estimates of discount rates to
mirror the aforementioned point estimates of risk aversion
has not yet been done by the authors in a separate
publication, and is beyond the scope of this publication.
Normative views of sustainability
Farmers believe that ecosystem condition should be
sustained at or above a threshold of 0.082 LSU/ha and
annual net income at or above a threshold of N$
Heterogeneity for both the normative view on
ecosystem condition and income is high with standard
deviations of 0.045 LSU/ha and N$ 206, 97, respectively.
In regards to the time horizon for sustaining ecosystem
condition and income we find that 8.7% of farmers do not
care about the future beyond their own generation,
whereas 16.1% of farmers have a very long outlook, i.e.
ten generations or more. On average, farmers indicated
that ecosystem condition and income ought to be
sustained for the 3.3 generations following their own
generation, that is, for the generations of their children,
grandchildren, and great-grandchildren. This is the
timeframe that most farmers are expected to experience
in their lifetime.
In Olbrich (2012), as well as in the subsequently cited papers, a positive
value of the CRRA indicates risk aversion, a negative value risk attraction and
a value of zero risk neutrality.
For comparison: median annual income for the Namibian population was N$
29,361 in 20032004. See also Footnote 12.
Namibian farmers typically have their children at a young age (personal
observation), and life expectancy is high (see for comparison our findings on
farmers’ age in below in this section).
Olbrich et al. 4115
Acceptable ecosystem condition risk and acceptable
income risk are both centred at an intermediate probability
value of 0.6, that is, a probability of 60% that grazing
capacity (income) falls below the specified threshold is
still acceptable. Distributions of both probability
thresholds are, however, spread out over the whole
range of possible values, as exemplified a standard
deviation of 0.2 for both characteristics, revealing large
heterogeneity across the farmers’ population.
Personal, farm and environmental features
Farmers are very heterogeneous in age and the
distribution is centred within the advanced age: average
age of farmers is 55.4 years with a standard deviation of
11.9 years. The majority of farmers (50.4%) are of
Afrikaans decent, with the remaining farmers being
predominantly of German descent. Education is of high
importance among farmers with a median of 4.0,
indicating that half of the farmers have a university
degree (bachelor, master or doctorate). Household size is
on average 3.3 members.
Farms are typically large with an average area of
rangeland of 7,949 ha, but individual farms are very
heterogeneous in size as indicated by a standard
variation of 6,765 ha. On average, farmers rent 1,149 ha
of farmland in addition to the land they own. Farms are
typically operated by a single owner (70.7%) as opposed
to forms of joint ownership (e.g. corporations, partnerships
or cooperatives). The most common production system is
oxen production (pursued by 47.7% of farmers), while
other production systems such as weaner production or
speculation production are of less importance. Farmers
earn a considerable higher income than other Namibians:
median income among farmers is N$ 150,001 to N$
250,000 which is much greater than the 2009/2010
Namibian median household income of N$ 40,744.
all farmers earn this income primarily from their farm:
20% of farmers are weekend farmers that operate the
farm only on the weekend (as a hobby or secondary
occupation) while earning their livelihood primarily in a
different occupation.
Farmers assess the previous five rainy seasons as
above average as indicated by a value of 4.0 (out of 6.0).
Land quality (e.g. soil conditions) is likewise assessed to
be above average with a value of 4.3 (out of 6.0). Almost
half of the farms (48.2%) have actual bush cover that is
intermediate or higher (that is, 41% or more of the farm
covered by bushes). Finally, the bush cover that farmers
The latest available national income data was elicited in 2009/10 by Namibia
Statistics Agency (NSA, 2012, 2010: 15). In order to properly compare our
2008 farm data to the 2009/10 national data, we have to adjust for inflation
which amounted to 9.1% in Namibia in 2008 (Statista, 2016). Adjusted median
farmer income can then be interpolated to N$ 164,651 to N$ 272,750 in 2009
prices, and is thus still considerably higher than the N$ 40,744 median
household income.
4116 Afr. J. Agric. Res.
Figure 2. Dendrogram for three cluster solution. Cluster labels and observations per cluster are
indicated above the respective branch. Clusters are MULTOWN (multiple owners), SUFIMA (high
sustainability / low financial risk management) and AFRIKAANS (Afrikaans farmers). N=108.
consider optimal on their farms is a low to medium cover
(an average of 25% of the farm being covered by bush)
and thus lower than actual bush cover.
Cluster analysis
In reporting results for the cluster analysis, we make
three terminological simplifications for convenience:
firstly, we say “characteristics of clusters” when we, of
course, actually refer to characteristics of the farmers or
farms included in the respective clusters; secondly, the
values we report are cluster-averaged values, but we do
not explicitly refer to them as “averaged”; thirdly, when
we state that a cluster is “different” we always mean,
unless otherwise noted, that the discussed clusters differ
significantly from all other clusters.
As previously mentioned, we excluded in the cluster
analysis all observations which had a missing entry in
any of the analyze characteristics. As a consequence,
only 108 observations (out of 398) were used in the
cluster analysis.
Choice of cluster number
Both the pseudo F- and the pseudo T-index have optima
jointly at a number of three and nine clusters (Figure 2,
Table 2). At three clusters the pseudo T-index has a
global minimum while the pseudo F-index has only a
local maximum. Conversely, at nine clusters the pseudo
Table 2. Results for pseudo F- and pseudo T square-indices
for different numbers of clusters. Good number of clusters are
indicated by high values for pseudo F-index and by low values
for pseudo T-square index.
Number of clusters
pseudo F
pseudo T square
so large that individual clusters are distinct in only very
few characteristics; secondly, under this solution we
encounter clusters with fewer than 7 observations,
making the validity of the analysis doubtful due to the low
F-index has a global maximum and the pseudo T-index’
only a local minimum. Examining both indices thus does
0.5 1 1.5 2
Gower dissimilarity measure
not give a unique solution to the optimal number of
clusters. Nonetheless, we report the three cluster solution
as the nine cluster solutions has two disadvantages:
firstly, it is not very insightful as the number of clusters is
number of observations.
Cluster SUFIMA
This cluster is best described by high sustainability and
low financial risk management (SUFIMA”). It is the
smallest in that it contains 26 out of the 108 analyzed
farms (Table 3). It is also the most distinct cluster,
differing significantly from each of the two other clusters
in 10 out of the 33 analyzed characteristics.
The cluster has the highest grazing capacity (0.089
LSU/ha; p<0.05) and thus the highest sustainability. In
regards to risk management, it has the lowest ratings of
the three clusters for all financial risk management
strategies, albeit the differences are significant only for
the strategies advances on livestock sales (1.4 on a six-
item Likert-scale; p<0.05) and loans for covering
operating losses (1.5; p<0.01). In contrast, it does not
have the lowest ratings for all on-farm strategies, but only
for three of these strategies: for rangeland size increase
(2.7; p<0.1), albeit at only the 10% significance level; and
for production system adaptation and breed adaptation
(both 3.9; p<0.05), albeit differing in both strategies only
from one other cluster. Finally, it also has the lowest
rating of rainfall risk (4.4 on a six-item Likert-scale;
p<0.1), but differs in the latter only at the significance
level of 10% and only from one other cluster. Thus, of the
aforementioned characteristics it is sustainability (via the
proxy grazing capacity) and financial risk management
that make this cluster distinct.
Cluster SUFIMA also has the most demanding normative
views pertaining to acceptable grazing capacity risk
(probability threshold of 0.7; p<0.05), possibly because
farmers in this cluster experience low risk and can thus
“afford” this more demanding normative view. Other
normative views are not significantly different.
Finally, the cluster is distinct in two characteristics
which are not obviously related to sustainability and
management: it has the lowest number of household
members (2.7 members; p<0.1) and it is the most patient
(2.6 out of 6.0, p<0.1), albeit it is again significantly
distinct in the latter characteristics from only one other
The cluster does not differ significantly in any other
personal, farm and environmental features or in risk
preferences. We especially note that it does not differ in
income, and that it also does not differ in weekend
farming (a criterion which, in Namibian everyday use, is
typically employed to characterize farmers).
Based on the distinct characteristics of cluster SUFIMA,
Olbrich et al. 4117
the remaining two clusters are accordingly characterized
by low sustainability and high financial risk management.
Beyond this distinction, however, they also have their
own distinct characteristics.
The next larger cluster with 36 farms is significantly
distinct in five such characteristics and best characterized
by multiple ownership (“MULTOWN”) as it has the highest
proportion of multiple ownership (41.7% of single owners,
corresponding to 58.3% multiple owners; p<0.01). It also
has the highest area of (net) rented land (2,587 ha,
p<0.05) and the highest area of rangeland, although the
difference in the latter variable is not significant. We may
interpret this as a tenuous indication that multiple owners
have the means to operate altogether larger farms.
This cluster also differs from the other clusters in
characteristics that are less obviously associated with
multiple ownership: it has the highest rating of the
strategy advances on livestock sales (3.6; p<0.05), the
lowest rating of the strategy resting rangeland (4.1;
p<0.05) and the youngest farmers (46.9 years; p<0.01),
albeit it significantly differs in latter two characteristics
from only one other cluster.
Cluster Afrikaans
The largest cluster with 46 farms is distinct in four
characteristics. It is difficult to describe this cluster as we
see no obvious connection between these characteristics;
we opt to describe it as Afrikaans farmers (“AFRIKAANS”)
as it exclusively consists of farmers of this ethnicity
(p<0.01). Beyond this distinction, it has an intermediate
rating of the strategy advances on livestock sales (2.6;
p<0.05) and, differing significantly from one other cluster,
has the lowest proportion of oxen production (42.3%;
p<0.01) and the lowest education level (3.4 index points;
Summary of cluster analysis results
Altogether, we thus also observe heterogeneity of cattle
farms when classifying them, albeit only one cluster of
farms (namely SUFIMA) is very distinct. In accordance
with the key distinct characteristics of this cluster,
classification is predominantly driven by sustainability and
financial risk management. To a lesser extent,
classification is driven by organizational structure or
ethnicity, the defining characteristics of the remaining two
clusters. Rainfall risk, risk and time preferences as well
as normative views play only a marginal role for
classification. Finally, we especially note again that
income does not drive classification at all.
In this paper we characterize commercial cattle farms in
4118 Afr. J. Agric. Res.
Table 3. Cluster-averaged values of characteristics for clusters MULTOWN (multiple owners), SUFIMA (favorable environment / low
financial risk management) and AFRIKAANS (Afrikaans farmers).
1) Rainfall risk [1=no risk, 6=very high risk]
2) Risk management strategies [1=not at all important, 6=very important]
On-farm management strategies
Additional feed
Production system adaptation
Breed adaptation
Resting rangeland
Rangeland size increase
Financial management strategies
Advances on livestock sales
Checking account as buffer
Loans for covering operating losses
Off-farm income
Investment into agricultural derivatives
3) Sustainability indicators
Grazing capacity [LSU/ha]
4) Preferences and normative views
Risk preference index [1=very risk averse, 5=risk neutrality, 7=very risk attracted]
Time preference index [1=very patient, 6=very impatient]
Sustainable annual net income [N$]
Sustainable ecosystem condition [LSU/ha]
Acceptable income risk [probability]
Acceptable ecosystem condition risk [probability]
Time horizon [generations]
5) Personal, farm and environmental features
Personal features
Household size [number of members]
Age [years]
Afrikaans [%]
Education level [1=no high school graduation, 6=Doctorate]
Farm features
Rangeland [hectare]
Land net rented [hectare]
Single owners [%]
Oxen production [%]
Annual net income [1= <N$50,000, 5= >N$350,000]
Weekend farmer [%]
Environmental features
Average rainy season assessment (20042008) [1=very poor, 6=very good]
Land quality[1=very poor quality, 6=very good quality]
Actual bush cover [1=0%, 6=81 to 100%]
Optimal bush cover [%]
p-values for cluster differences calculated for each characteristic by one-way ANOVA for continuous and Chi-square test for binary characteristics.
Shading indicates cluster responsible for differences as calculated by Bonferroni-corrected t-tests for continuous and pair-wise Chi-square test for
binary characteristics, with the significance levels: *** p<0.01, ** p<0.05, * p<0.1. Dark shading denotes that cluster differs from both other
clusters, light grey shading that it differs from only one other cluster (the one most different in averaged values). N=108.
Namibia, a prime case of livestock farming in semi-arid
rangelands, according to 1) perceived rainfall risk, 2) risk
management, 3) the farm’s sustainability, 4) risk and time
preferences and normative views of sustainability, and 5)
personal, farm and environmental features. We find that
cattle farms are highly heterogeneous in a wide range of
When classifying farms in a cluster analysis, we also
find heterogeneity as exemplified by the identification of
three separate clusters (“SUFIMA”, “MULTOWN” and
“AFRIKAANS”). More specifically, results from the cluster
analysis show that cattle farms are classified mainly by
their sustainability and the farmer’s financial risk
management, but not by the farmer’s income: the most
distinct of the three identified clusters is characterized by
high grazing capacity (a proxy for high sustainability) and
low financial risk management, but not by high or low
income. In other words, our results suggest that
sustainability and financial risk management are inversely
related while income levels appear to be unaffected by
the choice of risk management technique. One possible
explanation, is that financial risk management may
provide income risk reduction to a similar extent than on-
farm management over the short term; however, it may
simultaneously reduce long-term sustainability since
farmers neglect the adequate on-farm management of
their rangeland. Such a link has already been proposed
in theoretical work (Quaas et al., 2007; Quaas and
Baumgärtner, 2008, 2012; Baumgärtner and Quaas,
2009a; Müller et al., 2011), and our empirical results thus
nicely conform to these theoretical findings.
It is also interesting to note which other characteristics
(apart from income) are not driving farm classification.
Firstly, risk and time preferences as well as normative
views of sustainability are only marginally important for
classification. Based on the observed differences in
management, one might hypothesize that preferences
and normative views, which are key behavioural
determinants, are not related to management behaviour
in Namibian cattle farming. Regarding preferences, this is
controversial and we do not expect that such a
hypothesis will be upheld under more in-depth scrutiny
than can be achieved through a cluster analysis.
Regarding normative views, however, we indeed find no
evidence that they impact on farm management in an in-
depth analysis (Olbrich et al., 2014). Secondly, weekend
farming, a characteristic typically employed by Namibian
farmers and decision makers for characterization of
farms, also does not drive our classification. It thus
seems that it is only of minor importance for
characterization in comparison to other characteristics.
Having provided these observations, we note the
limitations of the cluster analysis: it cannot be used to
make definite statements concerning the causal
relationship between single characteristics and thus
cannot be a substitute for an in-depth analysis. Most
importantly, we cannot clarify the exact relationship
Olbrich et al. 4119
between sustainability, financial risk management and
income without further analysis, as we have, for example,
done in respect to normative views (Olbrich et al., 2014).
Altogether, this study is the first to provide a
characterization of Namibian commercial cattle farms in
respect to risk, management and sustainability. It furthers
the understanding of the system and provides the basis
for more in-depth analyses. Finally, it highlights issues
that may warrant close attention and may ultimately
contribute to the development of policies that promote
sustainability of commercial cattle farming in Namibia.
Conflict of Interests
The authors have not declared any conflict of interests.
This work was funded by the German Federal Ministry of
Education and Research (BMBF) under grant no.
01UN0607. The Ministry was not involved in the study
design, in the collection, analysis and interpretation of
data, in the writing of the report or in the decision to
submit the article for publication.
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Bush encroachment is an environmental problem in savanna ecosystems, but it is not yet clear whether it is more prevalent in communal or ranching grazing lands. This study investigates bush encroachment dynamics in relation to two rangeland management systems under different environmental conditions in Botswana. Woody vegetation cover (WC) was measured in 10 × 10 m quadrants at 100, 200 and 300 m along 23 transect located in both communal and ranching lands. Woody plant cover and diversity were not strongly influenced by rangeland management systems (P>0.05), but were highly dependent on site effects. The encroached rangelands (WC>40%) particularly, at Matlolakgang site and also had high woody species diversity. Woody cover and diversity increased with decreasing soil clay content, but not in a linear way. The lack of variability of bush encroachment between management systems has implications for rangeland management policy in Botswana and other sandveld regions of sub-Saharan Africa.
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Near-term and current regional climate patterns for Zimbabwe are summarized based on twentieth-century observation data and model simulations. These records suggest air temperature warming of up to 0.8 degrees C and a decline in annual precipitation during the past 60 yr. Future climate scenarios, based on 2 General Circulation Model (GCM) equilibrium models, General Fluid Dynamics Laboratory (GFDL3) and Canadian Climate Centre (CCCM), were developed for Zimbabwe and southern Africa. Both GCMs suggest that with doubling of atmospheric CO2 mean air temperature will increase by 2 to 4 degrees C. In contrast, these 2 GCMs project different current and future precipitation scenarios for Zimbabwe. When calibrated with observed climate data from Zimbabwe, the CCCM GCM closely simulated current ambient temperature and precipitation patterns. The El Nino Southern Oscillation (ENSO) phenomenon is a major influence on interannual variability of climate in southern Africa. Future African regional climatology projections, based on GCM scenarios, will need to better consider ENSO and other factors if greater precision is to be achieved.
We study optimal adaptive grazing management under uncertain rainfall in a discrete-time model. As in each year actual rainfall can be observed during the short rainy season, and grazing management can be adapted accordingly for the growing season, the closed-loop solution of the stochastic optimal control problem does not only depend on the state variable, but also on the realization of the random rainfall. This distinguishes optimal grazing management from the optimal use of most other natural resources under uncertainty, where the closed-loop solution of the stochastic optimal control problem depends only on the state variables. Solving this unusual stochastic optimization problem allows us to critically contribute to a long-standing controversy over how to optimally manage semi-arid rangelands by simple rules of thumb.
Many savannahs are considered to be threatened. But the massive areas, covering a fifth of the earth, have a dynamic of their own: The apparently unstoppable encroachment of scrub appears to be just a natural transitional stage