


































African Journal of Agricultural Marketing Vol. 2 (3), pp. 120-126, March, 2014. Available online at 
www.internationalscholarsjournals.org © International Scholars Journals 
 
 
 

 

Full Length Research Paper 
 

Strategies for managing risk of cattle farmers in 

Namibia: Case study from Omaheke and Otjozondjupa 

regions 
 

T.J Danjuma and Dencia Alidon 
 

Department of Agriculture, Polytechnic of Namibia, Namibia. 
 

Accepted January  29, 2014 
 

A sample survey of 269 questionnaires was administered to commercial and communal cattle farmers 
in the two regions of Namibia, (Omaheke and Otjozondjupa) to identify factors which affect the adoption 
of livestock insurance. About 205 questionnaires were collected and the 64 questionnaires were 
regarded as irregular responses or not returned. A computer software programme was used to generate 
a logit model. This model was used to test the alternative risk management strategies used by farmers 
in the two regions. In addition, this research took into account the off-farm investment and farm 
enterprise diversification. The logit model produced results that are statistically significant and 
negative estimated coefficient of the household characteristics. This implies that the Namibian 
livestock industry growth can be achieved with improved education, experience and support from other 
income as way of diversifying risk strategy. However, the positive relationship of variables FTHEFT and 
PROD implies the sector is suffering from continuous risk of theft and requires quality production to 
get market access. This necessitates the need for policy makers and insurers to design programme to 
educate farmers so that they can adopt proper risk management tools and thereby increase their 
participation in insurance. The low level of education of many farmers in the study area may have 
negatively influenced the decision to purchase livestock insurance in addition to other factors. 

 
Key words: Risk management strategies, logit model, Namibia livestock industry 

 
 
INTRODUCTION 
 
Natural disasters such as floods and bushfires can have 
a major impact on the management and financial viability 
of rural properties, as well as major implications for 
animal welfare. Landholders have a responsibility to 
ensure that management and property development 
plans recognise the risks and incorporate the strategies 
that are necessary to ensure the safety of all persons, 
livestock and any residential dwellings, and the security 
of the plants and equipments in the event of such a 
disaster. The cyclical nature of production is a 
characteristic of livestock farming which is caused 
primarily by the climatic conditions such as flooding and 
drought. Moreover, human-made disaster such as theft 
and starting of fires are other risks for losing livestock. To 
 
 
 
*Corresponding author. E-mail: ty_danjuma@gmail.com. Tel: 

+264 61 207 2304. Fax: +264 61 207 2143. 

 
 
 

 
avoid such kind of loss of livestock productivity, it is 
important to increase insurance awareness campaigns 
among the farmers. Insurance awareness enable farmers 
to realise the need for livestock insurance, therefore 
insure their livestock, which can then be considered as a 
way of risk management and management skills. Risk 
assessment and risk reduction strategies need to be con-
sidered by Namibian livestock farmers regardless of the 
size of the property. These are the basic elements that 
assist in developing an awareness of the range of risks 
and issues (Bowler, 2007). In Namibia, cattle farming are 
the main agricultural production sector with annual 
estimated value of N$900 million, and of which about 
N$400 million is being contributed by exports of weaner. 
The total number of cattle in 2006 was estimated to be 
2.3 million (Meat Board of Namibia, 2007). In this context, 
the objective of this study is to assess the 
appropriateness of insurance as a risk management tool 
for farmers in communal and commercial areas. The 



Danjuma & Alidon             120 
 
 

 
research includes a literature study of the principles, 
opportunities and problems of risk- sharing strategies in 
Namibia. Secondly, an empirical study of farmers’ 
perceptions of risk and risk management were carried out 
by administering questionnaires among a large sample of 
livestock farmers. Since livestock production is the 
backbone of the economy of Namibia, it is vital for 
farmers to gain awareness about the importance of 
insurance. Therefore, this study makes a contribution for 
improvement of the know-how of farmers regarding risk 
assessment in their farming activities. Therefore, the 
main objectives of this paper are to examine factors that 
influence livestock insurance adoption by livestock 
farmers in Namibia’s Omaheke and Otjozondjupa regions 
and draw some policy implications from the results. 
 
 
METHODOLOGY AND DATA 
 
General 
 
A structured questionnaire was used to identify factors that affect 
the use of insurance. Semi-structured interviews were conducted 
with farmers at their respective farms. Information on livestock 
production characteristics in the smallholder farming systems was 
obtained from communal and commercial farms in Omaheke and 
Otjozondjupa. The Omaheke and Otjozondjupa regions are the two 
major producers of livestock in Namibia. Hanagara (2009) on his 

study shows that communal and commercial farmer in Omaheke 
registered with Meat board of Namibia estimated to be 5727 and 
390 respectively. Whereas number of farmers at Otjozondjupa were 
unable to registered, when an inquiry made to the farmer from that 
area estimation from his knowledge indicated it should be less than 
Omaheke. A total of 269 questionnaires were distributed to the 
farmers in these regions. From this 42 of questionnaires were 
collected from commercial farmers and about 163 were collected 

from communal farmers. The remaining 64 questionnaires were 
regarded as irregular response or not returned. With the 
assumption of Otjozondjupa the same population as Omakeke, the 
surveyed questionnaire is fairly representative; this accounts for 5 
and 1% communal and commercial farmers respectively. 

 
Household characteristics 
 
The following household characteristics were included: sex; age; 

level of training/education attained; farming experience; and size of 

the household. 
 
 
Study area 
 
The study was conducted in Omaheke and Otjozondjupa regions 
and focussed on both communal and commercial farming systems 
using 205 respondents, which is fairly representative of the study 
area. The respondents were chosen for particular purposes on the 
basis that they were involved in livestock farming in these two 
regions, and on basis that they were “typical” of a group or 
represented diverse perspectives of livestock farming (Leedy and 
Armrod, 2000). District interviews were conducted using students 
residing in these regions. This proved to be cost effective way of 

colleting data. The estimated population of farmers was supplied by 
Agricultural Extension Officers or village heads as the sampling 
frames. 

 
 
 

 
Review of current literature 
 
Jarvie and Nieuwoudt (1988) define insurance as “… the elimination 
of the uncertain risk of loss for the individual through the 
combination of a large number of similarly exposed individuals who 

each contribute …premium payments sufficient to make good the 
loss caused to any one individual.” Thus, the idea behind insurance 
is that of risk pooling, which involves combining the risks faced by a 
large number of individuals who contribute through premium pay-
ments to a common fund that is used to cover the losses incurred 
by any individual in the pool (Hardaker et al., 1997). Insurance, in 
general, can provide protection against adverse economic losses 
experienced by individuals and firms, and caused by natural 
phenomena such as fire, hail and floods. The decision to buy 
insurance against risk in agriculture should be an economic one. In 
making that decision, requires the consideration of the following two 
critical factors: 
 
1) How much loss can the manager (farmer) withstand without 
insurance?  
2) What are the trade-offs between insurance costs and potential 

losses (Casavant and Infanger, 1984)?  
 
Therefore, insurance is more attractive to risk-averse farmers and in 
situations where risks warrant paying a premium significantly higher 
than the expected loss without insurance (Hardaker et al., 1997). 
However, in some countries government subsidises premiums, 
making the purchase of insurance more attractive (Eidman, 1990). 
There are two basic issues that affect both the insurer and insured. 
These are asymmetric information and systemic risk. Asymmetric 
information relates to the problem that the insurer and the insured 

may not have the same information regarding the probability of 
losses occurring. The problem could arise due to either adverse 
selection or moral hazard. Adverse selection occurs if those more at 
risk purchase more insurance than others, without the insurer being 
aware. “As a result, the insurer’s expected indemnity outlays 
exceed total premium income, and, in the long run, the insurance 
operation loses money” (Nieuwoudt, 2000). 
 
 
RESULTS AND DISCUSSION 
 
Descriptive result analyses 
 
Table 1 shows the diversification of risk, non- farm 
income and insurance awareness. The research revealed 
that 95.2% commercial and 98.2% communal farmers in 
Omaheke and Otjozondjupa regions have no insurance 
cover for their livestock. They use non- farm income and 
diversify their farm activities as means of risk 
management strategy. Furthermore, the study reveals 
that 71.4% of commercial farmers and 92% of the 
communal farmers have off farm income to support their 
farming. From the survey conducted, the study finding 
implies that farmers in Namibia might not insure their 
livestock. 26.2% of the commercial farmers revealed that 
they cannot afford to pay insurance premium, and 27% 
revealed that they do not see the importance of insuring 
livestock. For similar research question, results from 
communal farmers show 17.2 and 82.8%, respectively 
(Table 2). This indicates that the livestock farmers’ 
awareness to insure is very poor in Omaheke and 
Otjozondjupa regions of Namibia. 

   



121      Afr. J. Agric. Mark. 
 
 

Table 1. Percentage of response to different questions. 
 

   Commercial farmers  Communal farmers 
 Description Yes No No response Yes No No response 
 Do you have other non- farm income? 71.4 23.8 4.8 92 6.7 1.2 
 Do you think insurance is important? 57.1 42.9  93.2 6.8  

 Do you have insurance?  95.2 4.8  98.2 1.8 
 Do you diversify your risk? 33.3 54.8 11.9    

 

 
Table 2. Summary statistics of reasons for not insuring livestock. 
 
   Commercial farmers Communal farmers  

   Frequency Percent Valid percent Frequency Percent Valid percent  
 Valid I could not afford 11 26.2 28.9 28 17.2 17.2  

  I do not see the importance 27 64.3 71.1 135 82.8 82.8  

  Total 38 90.5 100.0 163 100.0 100.0  

 Missing System 4 9.5      

 Total  42 100.0      

 

 
Table 3. Summary description of the study areas and sample sizes used for the diagnostic study.  

 
   Commercial farmers Communal farmers 
   Frequency Percent Valid percent Frequency Percent Valid percent 
 Valid Male 39 92.9 92.9 127 77.9 77.9 
  Female 3 7.1 7.1 36 22.1 22.1 
  Total 42 100.0 100.0 163 100.0 100.0 

 

 
Table 4. Summary description of type of farming. 

 
  Commercial farmers Communal farmers  

  Frequency Percent Valid percent Frequency Percent Valid percent  

Valid Livestock 29 69.1 76.2 82 50.3 50.3  

 Mixed farming 13 30.9 23.8 81 49.7 49.7  

 Total 42 100.0 100.0 163 100.0 100.0  

 

 
Table 3 summarises the gender distribution data in the 

survey, in both farming sectors, the largest portion of the 
respondents were male and this shows that there is 
gender imbalance in this sector. This could imply that 
government and other non- governmental stakeholders in 
gender equality need to encourage female farmers’ 
participation.  

Table 4 shows that commercial farmers who practise 
livestock farming, account for about 69% of the 
respondents. This shows that commercial farmers focus 
mainly on increasing productivity by specialising on the 
specific enterprise. On the other hand, communal farmers 
responded that they are engaged in both livestock and 
mixed enterprise on equal basis (50:50%). This implies 
that the communal farmers try to diversify their farming 

 

 
activities. This could be due to poor resource ownership 
preferred to diversify risk and getting advantages of 
multiple income generation.  

The responses on diversifying enterprise indicates that 
about 16.7% commercial farmers regard profit as the 
main motive for farming, 9.5% of farmers indicated that 
they diversify in order to avoid risk and 4.8% indicated 
that they diversify to create jobs for their families. On the 
other hand, communal farmers reveal that about 14% 
diversify with the expectation of creating jobs, 12.3% to 
avoid risk and 11.7% for profit reasons (Table 5). 
 

Tables 6 and 7 illustrate the perception of insurance by 

communal and commercial farmers. The research found 
that the livestock farmers’ perception to insurance is good 



Danjuma & Alidon             122 
 
 
 

Table 5. Summary of reasons for diversifying farming enterprises. 
 

      Commercial farmers  Communal farmers  

      Frequency Percent Valid Percent Frequency Percent  Valid Percent  
 Valid To get better profit 7 16.7 53.8  19 11.7 23.5  

  To avoid risk  4 9.5 30.8  20 12.3 24.7  

  To create a job  2 4.8 15.4  23 14.1 28.4  

  Total  13 31.0 100.0  19 11.7 23.5  

 Missing System  29 69.0   81 49.7 100.0  

 Total     42 100.0   82 50.3    

          163 100.0    

   Table 6. Summary result of importance of livestock insurance.      
            

      Commercial farmers  Communal farmers    

      Frequency Percent Frequency Percent    

    Valid Yes 24  57.1  150 92.0    

     No 18  42.9  11 6.7    

     Total 42  100.0  161 98.8    

    Missing System    2 1.2    

    Total      163 100.0    

 

 
Table 7. Summary result of degree livestock insurance importance. 

 
  Commercial farmers Communal farmers  

  Frequency Percent Frequency Percent  

Valid Very important 16 38.1 139 85.3  

 Important 7 16.7 11 6.7  

 Not important 1 2.4 3 1.8  

 Total 24 57.1 153 93.9  

Missing System 18 42.9 10 6.1  

Total  42 100.0 163 100.0  

 

 
good. These accounts for 92 and 57% of communal and 
commercial farmers, respectively (Table 6). When the 
respondents were asked how important insurance is to 
their livestock, 85% of communal farmers and 38% 
commercial farmers said it was very important (Table 6). 
This implies that the insurance companies in Namibia did 
not give much weight to advocate livestock insurance; 
this might be due to the nature of the farms or complicity 
of the enterprise to handle the sector in the insurance 
system. 
 
 
Model 
 
In explaining a model the dependent variable (Yi), (where 

one represents the farm enterprise diversified as risk 

minimisation and zero represents not diversified), 

different regression methods can be used. Some of the 

methods would make use of discriminant analysis, linear 

 

 
probability model, logit and probit. In this study the 
following independent variables were considered: 

Gender of the head of household; age of the head of 
household; year of farming experience; education level; 
family size; off farm income; awareness to insurance; 
frequency of theft; safety; productivity; frequency of sales; 
value of sale; credit, character of a person to risk 
(captured by asking his/her prescription in starting new 
technique).  

This study follows the general modelling of Mohammed 
and Ortmann (2005) to test the relationship between 
dependent and exploratory variables. The model is as 
follows: 
 
ln (pi/(1-pi) = o + 1Gender + 2 Age+ 3 EXPLVi+ 4 EDUi + 5 

FSIZE+ 6 OFFi + 7INFOi + 8FTHIEFT + 9SAFETY+ 

10PROD+ 11FSale+ 12Vsales+ 13CREDIT  
+ 14Ntech+ 15Dummy 
Where: 

   



123      Afr. J. Agric. Mark. 
 
 
 
Table 8. Variables that influence the adoption of risk management and expected sign. 
 

Variables Description Expected sign 

Gender Gender of the household, +/- 
Age Age of the household +/- 
EXPLV Year of farming experience +/- 
EDU Education level head of the household +/- 
FSIZE Number of people in the house +/- 
OINCOME Off farm income outside the farm - 
INFO Awareness to insurance + 
FTHIEFT Frequency of theft + 
SAFETY A need to increase safety of production - 
PROD A need to increase productivity - 
FSALE Frequency of sales - 
Vsales Value of sales last season - 
CREDIT Amount of loan outstanding - 
Ntech Character of a person to risk - 
Dummy Dummy variable for type of farming, 1 for commercial farmer, otherwise zero +/- 

 

 
(pi/1-pi) is the probability of insurance awareness, gender 
of the household, age of the household, EXPi is the 
farming experience, EDU is the educational level, Fsize is 
the number of family members in the house, OFFi is the 
off farm income, INFOi is the awareness of insurance, 
Ftheft is the frequency of theft, SAFETY is a need for 
safe production, PROD is a need to increase productivity, 
Fsale is frequency of sales, Vsale is a value of sales last 
season, CREDIT is the amount of an outstanding loan. 
 
 
 
Consideration of the model variables 
 
As indicated earlier, livestock insurance adoption in 
Omaheke and Otjozondjupa regions of Namibia is not 
well established. This may be due to a number of factors, 
such as lack of information on the livestock insurance, 
lack of awareness of the insurance scheme, low level of 
educational attainment among farmers, poor rural 
infrastructure (making communication difficult and limiting 
access to insurance), affordability, degree of farmers’ risk 
aversion and diversification of farm enterprise. These 
could be among the factors that affect the insurance of 
livestock. The definitions of the most important variables 
expected to influence the adoption of livestock insurance 
are presented in Table 8.  

The expected sign for risk management between male 
and female farmers could be positive or negative; male 
farmers are most likely to be risk takers and expected to 
invest in one enterprise for higher return. On the other 
hand, since men have relative higher power and time to 
spend at the farm, they could do mixed farming to keep 
themselves busy at the farm. Age and experience holds 
similar impact to the adoption of risk management.  

The expected sign for educational qualification could be 

 

 
positive or negative. Education may promote an 

understanding of the effects of risk and hence may 
increase the demand for diversification of the enterprise 
as risk management. On the other hand, increasing 
education levels are associated with an increase in 
transferable human capital, facilitating greater risk taking 
by individuals with lower risk aversion (Szipiro and 
Outreville, 2003 cited in Mohammed and Ortmann, 2005). 
Family size was expected to have a positive effect as the 
numbers of family members were dependent on the farm. 
As farmers participate in off-farm investments this will be 
take as a risk management strategy, the probability of 
diversifying enterprises decrease, either due to other 
secured income or they do not have time to involve in 
additional farm enterprises. Therefore, the expected sign 
for off-farm income will be negative. As farmers’ attitude 
towards insurance increases, it is most likely that farmers 
will see need to subscribe to insurance. Further, as the 
frequency of theft increases, it is necessary for farmers to 
diversify farming to avoid potential losses.  

Although diversification is an alternative risk 
management strategy, it does not necessarily mean that 
diversification and insurance always can closely 
substitute each other (Blank and McDonald, 1996). 
 
 
LOGIT MODEL RESULTS 
 
A chi-square test was used to test the equality of the 
standard deviation of a population to a specified value. 
This test can be either a two-sided test or a one-sided 
test, the two- sided version tests against the alternative 
that the true standard deviation is either less than or 
greater than the specified value. The one-sided version 
only tests in one direction. The choice of a two-sided or 
one-sided test is determined by the problem (Snedecor 



Danjuma & Alidon             124 
 
 
 

Table 9. Logit model results for livestock risk management adoption in Namibia (n = 200). 
 

Variables Coefficient estimate Standard error Wald t-stat Significance 

Gender 7.667 3.808 4.073 0.162** 0.0436 
Age -0.0654 0.0291 5.074 -0.197** 0.0243 
EXPLV -0.0212 0.0536 0.157 0.692*** 0.0001 

EDU -1.003 0.598 2.809 -0.101* 0.093 
FSIZE 0.594 0.268 4.931 0.192** 0.0264 
OINCOME 3.77 1.511 6.22 0.231** 0.0126 
INFO 0.883 1.58 0.312 0.001* 0.5763 

FTHIEFT 1.338 0.838 2.553 0.083* 0.1001 
SAFETY 0.0314 0.893 0.0012 0.0001 0.971 
PROD 4.015 1.929 4.33 0.172** 0.0374 
FSALE -3.792 1.485 6.519 -0.239* 0.0107 

Vsales 0.0005 0.0003 3.469 0.1358* 0.0629 
CREDIT -4.256 1.74 5.989 -0.224*** 0.0629 
Ntech -1.725 1.409 1.498 -0.001 0.2209 
Dummy -12.833 6.72 3.65 -0.1442* 0.0562  

 Model chi-square 36.49*** on 20 degree of freedom 

 Correct prediction (%)  
 Overall 93 
 Diversification 60 
 Non diversified 98 

Note: *, **, and *** indicates statistical significance at 10, 5 and 1% respectively. 
 

 
and Cochran, 1983). 

The chi-square, which tests the joint significant of the 
explanatory variables in this study, is statistically 
significant at the level 5% (Table 9). The estimated model 
correctly classified 93% of the respondents. The success 
rates for predication of adoption of diversification and non 
diversified enterprises are 60 and 98%, respectively. 

On the basis of results obtained and shown in Table 9, 
the techniques described in the methodology section 
were applied and factors affecting diversification as a 
means of risk management are reported in this section. 
Variables NSTOCK, EXPLV, SAFETY, HOWIMP and 
NTECH are not significant (Table 9), all other variables 
were found to be statistically significant at the specified 
level of significance.  

The positive estimated coefficient sign to the variables 
(Dummy, GENDER, PROD, OINCOME, VSALE, 
FTHEFT, and WSALE) indicate that the greater the 
values of these variables, the higher the tendency for far-
mers to diversify their enterprises. The negative sign for 
the remaining variables indicate the opposite implication 
of the previous explanation, that is, the greater the value 
of these variables, the lower the probability for diversified 
enterprise.  

The positive sign of the FSIZE was as hypothesised. It 

indicates that family members are dependents on the 

 

 
farm. Responsibility and creativity increases as the far-
mer wants either to avoid risk or to obtain better income 
for the family. This makes it necessary for the family to 
diversify its enterprise, especially communal farmers.  

The formal education level (EDU) has a negative 
coefficient estimate indicating that, ceteris paribus, the 
probability of diversifying risk deceases as the level of 
formal education of the farmers’ increases; this implies 
that farmers preferred to specialise on the specific 
enterprise to maximise output. Bullock et al. (1994) and 
Vandeveer (2001) found education was negatively related 
to a farmer’s willingness to take risk. However, 
Mohammed and Ortmann (2005) found that education 
was positively related to farmers’ willingness to take risk.  

The gender of the farmer was found to be positive and 
significant at 5% level, whereas, age was found to be 
negative and significant. This implies that female farmers 
are risk averse. Age indicates that specialisation as the 
farmer matures is most likely to be more commercialised. 
The negative estimate coefficient for age implied the 
decision of diversification. It appears, therefore, that older 
and more experienced farmers are less willing to diversify 
their enterprise. Farmers with such characteristics might 
have acquired enough knowledge through time to deal 
with income and risk without diversification. Results of the 
studies by Jarvie and Nieuwoundt (1988) and Vandeveer 



125      Afr. J. Agric. Mark. 
 
 

 
(2001), however, indicate that younger farmers, or those 
with less experience, were less likely to diversify their 
enterprise.  

Off-farm income (OINCOME) has a negative coefficient 
estimate implying that the more farmers engage in off-
farm activities the less probability to diversify the 
enterprise. This may be due to the fact that time 
constraints to allow stretching to many enterprises. The 
off- farm income is also a good cash injection to the 
livestock farming especially for the emerging commercial 
farmers. They support their farms through their other 
income outside farm. The off- farm income helps many 
farm households as a means of diversification of risk. 
This negative estimate coefficient for this variable implies 
that farmers engage in off farm activities. 

FSALES shows significant and negative estimated 
coefficient. This implies that there is a high frequency of 
sales in the livestock. This is a good proxy for good 
market opportunity for the livestock farmers. On the other 
hand, frequency of theft (FTHEFT) shows positive 
estimated coefficient. This implies that as the risk of theft 
gets high, farmers prefer to diversify. CREDIT indicates 
that negative and significant values show that farmers 
focus on one enterprise to get more return in order to 
increase the payment/debt commitment to the debtors.  

Lastly, the puzzling result of the estimated coefficients 
on PROD and VSALE, shows significant levels, and are 
positive to influence diversification. Perhaps insured and 
non- insured category data were not found for this study. 
This might jeopardize the result of this variable, or this 
might suggest that a specific study is needed on the 
impact of productivity and value of livestock relationship 
to risk management strategy to obtain a more reasonable 
estimate. 
 
 
CONCLUSIONS AND POLICY IMPLICATIONS 
 
It was very surprising that none of the surveyed livestock 
farmers insured their livestock. As indicated in Table 1 
farmers were aware of the importance of insurance. 
However, when they were asked why didn’t they insure 
their livestock, 64.3% of commercial farmers and 82.8% 
communal farmers said, they did not see the importance 
of insuring. About 26.27% of commercial farmers and 
17.2% communal farmers said that the insurance 
premium was unaffordable. This implies that the insurers 
need to increase campaign on increasing awareness on 
the role of livestock insurance to farmers. It requires the 
combined efforts of all stakeholders namely, 
governments, civic society organisations and the private 
sector. Furthermore, it is important to ask question; how 
can livestock farming in Namibia make a significant con-
tribution to the viability of farming systems in the country 
to achieve the Millennium Development Goals without 
considering how to minimise risk in the farming? Is it 
impossible to support the farming industry? Therefore, it 
is important for good risk assessment and risk reduction 

 

  
 
 
strategies to be considered by Namibian livestock 
farmers in Omaheke and Otjozondjupa regions, 
regardless of the size of the property.  

The statistically significant and negative estimated 
coefficient of the household characteristics at the spe-
cified significance level (such as family size, educational 
qualification, age, and off farm-income) implies that 
Namibian livestock industry growth was achieved with 
improved education, experience and support from other 
income as a strategy for diversifying risk. However, the 
positive relationship of FTHEFT and PROD implies that 
the sector is suffering from continuous risk of theft and 
requires quality production to get market access. There-
fore, farmers need to follow effective risk management 
systems to achieve the required profit rate. 

The negative statistical estimation of education 
attainment plays a role in creating insurance awareness 
and implies that farmers prefer to diversify their enterprise 
as a risk management tool, rather than buying insurance. 
On the other hand, the positive correlation of information 
toward insurance (which is captured by asking “How is 
insurance important to you?”) among the livestock 
farmers in Omaheke and Otjozondjupa regions. The 
answer to this question reveals that there is a need for 
policy makers and insurers to design a programme to 
better educate farmers so that they can assess risk 
management tools and thereby increase their 
participation in insurance. The low level of education of 
many farmers in the study area may have negatively 
influenced the decision to purchase livestock insurance in 
addition to other factors such as not knowing about the 
provision of insurances, the premium being too expensive 
and so forth. The insurance companies in Namibia should 
intensify their advertising efforts and inform farmers of the 
importance of insurance in the industry. Currently, 
livestock farmers seem to follow diversification activities 
as alternative risk management strategies.  

While the results of this study are encouraging, there 

remains considerable scope for refining and deepening 
the research. 
 
 
REFERENCES 
 
Blank SC, Mcdonald J (1996). Preferences for crop insurance when 

farmers are diversified. Agribusiness. 12(6): 583-592.  
Bowler J (2007). Risk management for stock owners in times of fire and 

flood. http://www.dpi.nsw.gov.au/__data/assets/pdf_file/0004/104278 

/risk-management -for-stock-owners-in-times-of-fire-and-flood.pdf 
date accessed on May 20, 2009.  

Casavant KL, Infanger CL (1984). Economics and Agricultural 
Management. An Introduction. A Prentice-Hall Company, Reston, 
Virginia, USA. pp. 144-147. 

Eidman VT (1990). Quantifying and managing risk in agriculture. 
Agrekon. 29(1): 11-23.  

Hardaker JB, Huirne RBM, Anderson JR (1997). Coping with risk in 
Agriculture. CAB International, Wallingford, Oxon, UK. pp. 106-111.  

Jarvie EM, Nieuwoudt WL, (1988). Factors influencing crop insurance 
participation in maize farming. Agrekon. 28(2): 11-16. 

Leedy P, Armrod JE (2000). Research Planning and Design. Merrill 
Prentiss Hall, USA. Pp. 49-50.  
Meat Board of Namibia (2007). Cattle review: production and marketing 



Danjuma & Alidon             126 
 
 
 
trends. http://www.nammic.com.na date accessed April 10, 2009. 
Mohammed MA, Ortmann GF (2005). Factors influencing adoption of 

livestock Insurance by commercial dairy farmers in Three Zobatat of 
Eritrea. Agrekon. 44(2): 172-186.  

Nieuwoudt WL (2000). An economic evaluation of a crop insurance 

programme for small-scale commercial farmers in South Africa. 
Agrekon. 39(3):269-291. 

 
 
 

 
Snedecor S, George W, William G (1989). Statistical Methods, Eighth 

Edition, Iowa State University Press. pp. 89-93. 
Vandeveer ML (2001). Demand for area crop insurance among litchi 

producers in northern Vietnam. Agric. Econs., 26(2):173-184. 


