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Current Research in Environmental Sustainability 3 (2021) 100103
Available online 17 November 2021
2666-0490/© 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Comparative analysis of time and monetary opportunity costs of
human-wildlife conict in Amboseli and Mt. Kenya Ecosystems, Kenya
D.O. Manoa
a
,
b
,
*
, F. Mwaura
b
, T. Thenya
b
, S. Mukhovi
b
a
Born Free Kenya, P.O. Box 1519-00502, Nairobi, Kenya
b
Department of Earth and Climate Science, University of Nairobi, P. O. Box 30197-00100, Nairobi, Kenya.
ARTICLE INFO
Keywords:
Human-wildlife conict
Opportunity costs
Time & Monetary loss
Amboseli
Mt. Kenya
ABSTRACT
Traditionally, the cost of Human-wildlife conict (HWC) has largely focused on visible costs, ignoring the hidden
costs (HC). The HC of HWC are losses that are uncompensated, temporarily delayed, or psychosocial in nature.
HC, such as opportunity costs (OC) are scantly documented to inform policy changes for addressing HWC. This
study demonstrates the importance of considering HC using Amboseli Ecosystem (AE) and Mt. Kenya Ecosystem
(MKE) in Kenya. The objectives of this study were to: a). quantify the economic magnitude of the OC of HWC and
its impacts on human wellbeing; b) compare the time and monetary OC; c) make recommendations for HWC
related policy reform. Data was collected from 408 households using a multi-stage sampling technique. Op-
portunity costs were conceptualised as the mean time and money lost due to wildlife presence and attacks.
Analysis indicates that the hours spent guarding livestock (t =3.820, d.f =110, p =0.000) and crops (t =3.571,
d.f =130, p =0.00) in AE and MKE at night were signicantly different. Conversely, daytime hours spent
guarding livestock and crops in AE and MKE were similar (P >0.05). On average, AE households spent KES 208,
540 (US$ 1913) compared to MKE who incurred KES 131,309.75 (US$1205) guarding livestock and crops.
School children in AE lost more time in the morning (1.28 ±0.053 h; n =98) and in the evening (1.22 ±0.044 h;
n =93) than in MKE. Overall, OC were more in AE than MKE, suggesting that HC varies with ecosystems. A
review of the wildlife compensation policy and law to include HC can help deter resentments resulting from
uncompensated HWC costs.
1. Introduction
Human-wildlife conict (HWC) is a historical problem that begun
when human beings started sharing space with wildlife as well as
domesticating plants and animals for livelihood support. This dates back
to the last Pleistocene era (about 15,000 years ago) and Neolithic period
(Squires, 2011). The International Union for Conservation of Nature-
World Conservation Congress (IUCN-WCC) resolution 101 of 2020 rec-
ognises that HWC has an impact on crop and livestock yields, prots and
human safety (IUCN-WCC, 2020). IUCN-WCC further acknowledges that
HWC compromises food security, economic growth and possibilities of
attaining sustainable development goals (IUCN-WCC, 2020). As such,
HWC remains a global challenge to both society and their means of
livelihood. Traditionally, the cost of HWC has been documented in terms
of direct costs, such as crop raiding, livestock loss and human death and
injuries (see for example Madhusudan, 2003; Zakayo, 2014; Mashalla
and Ringo, 2015 & Dai et al., 2019) while ignoring the hidden costs. Yet,
Hoare (2001) asserts that HWC has a wide range of intangible negative
social and psychological impacts including fear, loss of sleep and devi-
ated focus.
1.1. Direct costs of HWC
The direct impacts of HWC includes crop damage, livestock preda-
tion, human deaths and injuries, property damage and diseases trans-
mission. Crop raiding is a common problem to many farmers across the
globe. For example, an estimate of crop loss to various wildlife species
(e.g., white-tailed deer, wild pigs, bears and sandhill cranes) between
2015 and 2019 in the eastern and southern parts of the USA, revealed a
soybeans loss worthy US$323.9 million and corn valued at US$194.0
million (McKee et al., 2021). In Brazil, the Military Highway Police of
S˜
ao Paulo documents an average of 2611 animal-vehicle crashes per
year, with 8.5% of cases resulting to human injuries or fatalities (Abra
et al., 2019). In addition, Abra et al., 2019 estimated the annual loss of
* Corresponding author at: Born Free Kenya, P.O. Box 1519-00502, Nairobi, Kenya.
E-mail address: manoadavid4@gmail.com (D.O. Manoa).
Contents lists available at ScienceDirect
Current Research in Environmental Sustainability
journal homepage: www.sciencedirect.com/journal/current-research-in-environmental-sustainability
https://doi.org/10.1016/j.crsust.2021.100103
Received 29 June 2021; Received in revised form 4 November 2021; Accepted 9 November 2021
Current Research in Environmental Sustainability 3 (2021) 100103
2
US$ 25,144,794 to the society due to vehicle collision with wildlife
species such as lowland tapir (Tapirus terrestris), and capybara (Hydro-
choerus hydrochoeris). Similarly, in China, nine people were killed and
ve injured by brown bear in Qinghai Province between 2014 and 2017.
In the same period, bear house break in losses in China were estimated to
be U$ 4.03 million (Dai et al., 2019). Wildlife species can also transmit
diseases to livestock. For example, it is estimated that badger-to-cattle
transmission causes between 1% and 25% of new outbreaks of Tuber-
culosis (TB) in cattle in the United Kingdom (Donnelly and Nouvellet,
2013).
In Africa, where people and wildlife still share space, direct impacts
of HWC are diverse. For instance, in the six coastal districts of Tanzania,
spotted hyena killed 14 people and injured 24 others between 2016 and
2018 (Tanzania Wildlife Management Authority, 2019). Similarly, an
analysis of HWC statistics for Laikipia and Kajiado Counties, showed
that a total of 64.09 ha of crops were damaged by diverse wildlife be-
tween 2010 and 2018 (Manoa et al., 2020b). In the same period, Manoa
et al. . (2020b) reported that Kajiado County lost livestock worth KES
1,785,000 (US$ 16,780.53) while Laikipia County lost KES 407,000 (US
$ 3826.15).
1.2. Hidden costs of HWC
The hidden costs of HWC are losses that are uncompensated,
temporarily delayed, or psychosocial in nature (Ogra, 2008; Barua et al.,
2013). They include transaction costs, health costs and opportunity
costs.
1.2.1. Transaction costs
Transaction costs are incurred due to bureaucratic inadequacies and
delays associated with compensation of victims of HWC (Barua et al.,
2013). The essence of the compensation schemes is to refund people the
nancial losses incurred through human injuries, death, crop and live-
stock loss, damage to property and so on. This is necessary in order to
enhance the coexistence between people and wildlife (Treves et al.,
2009). Yet, in reality those affected by HWC, particularly in developing
regions experience difcult in accessing compensation as expected.
Consequently, scholars such as Ogra and Badola (2008), DeMotts and
Hoon (2012), and Barua et al. (2013), have pointed out corruption, lack
of education and awareness, and inability of wildlife authorities to
attend to claims in a timely way are hindrances to compensation
schemes. The processing of compensation claims usually requires vic-
tims to provide a wide range of supporting documents such as death
certicates and title deeds, proof of compensation claim travel related
expenses, all which greatly magnify the time and money transaction
costs (Madhusudan, 2003). Jadhav and Barua (2012), therefore claims
that pursuing compensation can expose people to new spaces of insti-
tutional inequality.
Delays in the payment of HWC compensation claims by governments
is not a new phenomenon in the world. Madhusudan (2003), for
example, reported that villagers around the Bandra Tiger Reserve in
India received only 14% and 5% of crop and livestock related
compensation, respectively after extended delays. Another study con-
ducted in the Boromo region in Burkina Faso, established that 98% of
the people who incurred losses due to human-elephant conicts opted
not to report such incidents because the government had not paid the
previous damages (Marchand, 2002). In Kenya, a performance audit for
the Kenya Wildlife Service (KWS) revealed that HWC cases worthy KES
2,235,388,000 (US$ 21,029,049) had not been paid between 2013 and
2018 (GoK, 2018). From the economic perspective, the delayed payment
of HWC compensation claims results to transaction costs over time.
1.2.2. Health costs
Human health can greatly be shaped by the stress and anxiety of
living within wildlife ranges. People have been found to be sensitive to
nancial costs and impaired freedom of movement, which can be
compromised by wildlife (Bowie, 2009). FAO (2009) argues that crop
damage results to reduced cash income and has indirect repercussions
on human health, nutrition, education and eventually on development.
When crop damage occurs, people divert the nances reserved for
healthcare towards the purchase of food items. In Indian Sundarban, for
example, Chowdhury et al. (2008) observed that about half of the
women who lost their husbands to tiger and crocodile attacks had psy-
chological problems due to the inability of recovering the bodies of their
loved ones for decent burials. Many had high rates of suicidal tendencies
and depression. Another study by Jadhav and Barua (2012) established
that injuries, fatality or physical threats from elephants worsened
pre-existing medical conditions such as alcoholism and contributed to
new ones such as post-traumatic stress disorder.
Similarly, a study conducted in Sagalla area of Taita-Taveta County
(southern Kenya), 92% participants (n =26) afrmed that elephant crop
raiding caused them emotional and mental distress (Weinmann, 2018).
This has been reported in other studies in Kenya. Farmers in Mirera area
in Naivasha (Kenya), for example, were reported to have spent sleepless
nights while trying to secure their farms aganist wildlife from the Lon-
gonot National Park which destroyed their crops. The farmers opted to
guard their farms at night in fear of wildlife invasion (Kimani, 2016).
And in Mwingi West (Kitui County), residents were reported to live in
fear after a stray lion from Kora National Park killed two cows in their
village, and efforts by KWS to capture and restrain the lion was taking
long. The resident feared that school-going children could be attacked
by the lion (Musangi, 2020). In another incident in Kajiado County, a
group of 30 primary school children from Lenkisem village, were
attacked by an elephant leading to the death of one child (Koech, 2021)
while the rest were living in fear of attending school.
1.2.3. Opportunity costs
Opportunity cost is dened as the loss or sacrice incurred by taking
a particular action against HWC instead of other more preferred and
benecial alternatives (Fauna & Flora International (FFI, 2014). Op-
portunity costs are part of the social challenges experienced by com-
munities living close to wildlife conservation areas (Manoa et al.,
2020a). For example, Mariki (2016) observed that destruction of water
pipes by elephants in West Kilimanjaro (Tanzania) resulted to people
walking longer distances to fetch water at the expense of other social and
economic chores. Elsewhere, Manoa and Mwaura (2016) documented
that pastoralists in the Amboseli region of Kenya who had not adopted
predator-proof kraals spent most nights in the wet season guarding their
livestock against predators.
A review of the hidden costs of HWC in Kenya by Manoa et al.
(2020a) revealed gaps in the characterisation, quantication and com-
parision of opportunity and other hidden costs in Kenyan rangelands (e.
g. Kajiado) and forest ecosystems (e.g. Mt. Kenya), which are associated
with high wildife densities. As such, it has for a long time been difcult
to acertain the effect of hidden costs on people's livelihoods for wildlife
policy reform. This study lls up the gap by comparing time and mon-
etary opportunity cost of HWC for Amboseli Ecosystem (AE) and Mt.
Kenya Ecosystem (MKE). The two study ecosystems are important
wildlife conservation areas, which host several state, community and
private wildlife areas which include national parks, wildlife conser-
vancies, biosphere reserves and world heritage sites (Manoa et al.,
2020a). The specic objectives of this study were to: a). quantify the
economic magnitude of the opportunity costs of HWC and its impacts on
human wellbeing; b) compare the time and monetary opportunity costs,
and c) make recommendations for HWC related policy reform.
2. Materials and methods
2.1. Study areas
Amboseli Ecosystem (AE) is located in Southern part of Kajiado
County and lies along the boundary of Kenya and Tanzania boarder
D.O. Manoa et al.
Current Research in Environmental Sustainability 3 (2021) 100103
3
(Fig. 1). Kajiado County (36◦5
̕, 37◦55
̕ E; 1◦10
̕, 3◦10
̕̕ S) (County Gov-
ernment of Kajiado, 2018). The ecosystem is hosting the world
renowned Amboseli National Park and UNESCO Man and Biosphere
(MAB) World Biosphere Reserve, which is linked to six community
group ranches: Ol gulului/Olorashi, Imbirikani, Kuku, Rombo, Ese-
lenkei, Kimana/Tikondo and a number of wildlife conservancies that
form a buffer zone, totalling to 5700 km
2
(KWS, 2020). It is bordered to
the south by the Mount Kilimanjaro National Park, which is a World
Heritage Site. On the other hand, the MKE (0◦25
̕ S, 0◦10
̕ N; 37◦00
̕ E,
37◦45
̕ E0 as shown in Fig. 2 (County Government of Meru, 2018), is
located in Meru County and Laikipia County within the Central part of
Kenya and consists of the Mt. Kenya National Park, Mt. Kenya National
Forest Reserve both of which have also been designated as a World
Biosphere Reserve and World Heritage Site. The ecosystem is linked to
the north by Ngare Ndare Forest and the Lewa Wildlife Conservancy, all
estimated to be 958 km
2
. This part of the ecosystem was the focus of the
study.
The two ecosystems have diverse wildlife species ranging from large
herbivores such as elephants, rhinos, buffaloes, giraffes, wildebeest,
hippos, zebras, impalas and Thompson gazelles to carnivores such as
lions, leopards, cheetahs and hyenas. AE has about 1800 elephants
(KWS, 2020), while MKE is estimated to have 2000–3000 elephants
(KWS, 2010). The elephants, hyenas and lion migrate widely within and
outside the ecosystems, and are known to destroy crops, attack livestock
and people (KWS, 2010; KWS, 2020; Manoa and Mwaura, 2016).
The two ecosystems experiences two-rain seasons in March-May
(long rains) and October–December (short rains), Rainfall in AE
ranges from 500 mm to 600 mm, whereas MKE receives between 300
mm (on Laikipia side) and 2500 mm (on Meru side). While AE has a
temperature range of 10
0
C to 34
o
C, MKE registers slightly lower tem-
peratures of as low as 8
0
C and as high as 32
o
C ((County Government of
Kajiado, 2018, County Government of Meru, 2018).
Most parts of the AE are sparsely populated, with population den-
sities of 51 person per km
2
with about 75% of the residents depending on
pastoralism for their income ((Table 1, KNBS, 2019a). However, there is
growing inux of agrarian communities into the ecosystem from the
more humid high population density areas. On the other hand, MKE
population varies, with Meru County having population density of 318
people per km
2
, while Laikipia County, which is a semi-arid area has 52
people per km
2
(County Government of Meru, 2018; KNBS, 2019b). The
main economic activity in MKE is crop faming in Meru County, while
many parts of Laikipia County are associated with pastoralism,
large-scale ranches and small-scale agriculture.
2.2. Data collection and analysis
Data collection took place between March and October 2019.
Extensive literature review and key informant consultations with 20 key
informants from conservation organizations and local administration
was conducted to help locate the sites with the highest incidences of
HWC in the two ecosystems. A multi-stage sampling was used to cluster
the population in each ecosystem according to the existing administra-
tive units (sub-locations) from which samples was drawn. Within the
sub-location, sample sizes corresponded to the population sizes of the
local villages. The researchers adopted the simplied Yamane (1967)
formula to calculate the sample size as follows:
n=N
1+N(e)2
Where n =Sample size; N=Population size; e =Margin of error.
Based on the 2017 population census data of households in Meru
County (400,407), Laikipia County (119,768) and Kajiado County
Fig. 1. Amboseli ecosystem (Manoa, 2021).
D.O. Manoa et al.
Current Research in Environmental Sustainability 3 (2021) 100103
4
(199,964), the sample size of 204 households per ecosystem was
derived.
MKE sample size =520,175
1+520,175(0.07)2=204
AE sample size =199,964
1+199,964(0.07)2=204
To determine the sampling interval per village, the researchers
divided the estimated number of households per village with the 2017
population projection of 4 persons per household in MKE and 5 persons
in AE. Target households were identied using the systematic sampling
technique based on the common landmarks at sub-location level, such as
schools, health centres, churches, local markets, water points, dips. In
each household, the researcher sought permission to interview an adult
with a focus mostly on the household heads (males). Where these were
absent, their spouses or any other adult (above 18 years) who had lived
in the household for at least one year was interviewed. For participants
to qualify as respondents in the research, they had to have resided within
the study area and had recently (no more than 12 months ago) experi-
enced one kind of HWC or another. To elicit the opportunity costs,
household respondents were asked to state the time and money spent on
guarding livestock and crops against wildlife attacks.
Opportunity costs was calculated as the mean time and money spent
on guarding livestock and crops in order to prevent livestock predation
and crop raids. In addition, school time lost and delayed reporting to
livelihood activities by adults as well as repair of damaged properties
were considered as opportunity cost. Where households employed
people to guard their property against wildlife, the wages paid per day
or month was considered as opportunity cost. However, where indi-
vidual household members were engaged in guarding, the number of
hours expended was used to calculate the monetary loss. This was done
based on average daily wages of KES 400 (US$ 3.71)
1
in AE and KES 600
(US$ 5.57) in MKE. It was based on the assumption that people worked
for an average 8 h daily, translating to KES 50 (US$ 0.46) and KES 75
(US$ 0.70) per hour, respectively. An independent student-test statisti-
cal analysis was used to test signicant differences between opportunity
cost in AE and MKE.
2.3. Willingness to pay (WTP) and willingness to accept (WTA)
compensation
Contingent valuation method (CVM) and Time Value for Money
(TVM) concept was used in the estimation of opportunity costs by
determining the and Willingness to Pay (WTP) in order to prevent loss
and the Willingness to Accept (WTA) compensation for inevitable losses
by the respondents. Where respondents gave their WTP in terms of crops
and livestock, the quantities were converted to money using the market
price obtained from Kajiado, Meru and Laikipia Counties. The mean
WTP/WTA gures were obtained from the open-ended questions.
Fig. 2. Mt. Kenya ecosystem (Manoa, 2021).
1
1US$ =KES 107.72
D.O. Manoa et al.
Current Research in Environmental Sustainability 3 (2021) 100103
5
3. Results
3.1. Time opportunity costs
3.1.1. Time spent guarding against wildlife
Guarding livestock and crops was a common practice in both AE and
MKE. Households in AE spent more time guarding livestock during the
day (4.16 ±0.185 h) and during the night (3.63 ±0.126 h) compared to
their counterparts in MKE who spent 3.46 ±0.466 h in the daytime and
2.48 ±0.338 h during the night. In addition, individuals in AE guarded
their crops more during the day (4.57 ±0.249 h) and night (3.88 ±
0.180 h) than those in MKE who used 4.39 ±0.178 h e daytime and 2.86
±0.1957 h during the night (Table 2). The combined household time
spent on both livestock and crops in AE and MKE during the day (16.58
h) was more than the total time spent during the night (12.85 h).
An independent student t-test indicated that night-time hours spent
guarding livestock (t =3.820, d.f =110, p =0.000) and crops (t =
3.571, d.f =130, p =0.00) were signicantly different in the AE and
MKE. The AE respondents spent 1.151 h more for night livestock
guarding with 1.026 additional hours for crop guarding than the MKE
respondents. However, the daytime hours spent guarding livestock and
crops in AE and MKE were similar (P >0.05).
3.1.2. School time lost and delay in income generating activities
The presence of wildlife in village areas resulted to late reporting to
school in the morning and leaving school earlier in the evening, which
led to loss of school time for the affected students. The mean school time
lost in the morning (1.28 ±0.053 h; n =98) and in the evening (1.22 ±
0.044 h; n =93) in AE was more than the time lost in MKE in the
morning (0.79 ±0.026 h, n =115) and evening (0.93 ±0.037 h, n =
125) as shown in Table 3. Majority of respondents in AE (51.5%, n =
105) and MKE (43.6%, n =89) had their children reporting to school at
10:00 am instead of the scheduled reporting time of 6:00 am. In addi-
tion, 19.1% (n =39) of the respondents in AE and 35.8% (n =73) in
MKE had their children reporting to school at 8:00 am instead of ofcial
time of 7:00 am.
In the evening, most of respondents in both AE (53.9%, n =110) and
MKE (38.7%, n =79) had their children leaving school at 3:00 pm
instead of 3:30 pm. Another 19.6% (n =40) in AE had their children
leaving school at 4:00 pm instead of 5:00 pm, while in MKE 23.5% (n =
48) of the respondents had their children leaving school at 3.30 pm
instead of 4.30 pm. The variation in schools closing time was based on
the lower primary, upper primary and secondary schools operational
timings.
In areas where parents feared that their children could be attacked by
wildlife, they were forced to escort them to and from school. The time
used to escort children in morning in AE (0.55 ±0.02 h; n =107) was
higher than in MKE (0.38 ±0.04 h; n =179). This meant that the adults
usually reported late to their respective livelihood activities such as
ploughing, milking, and casual work stations because of wildlife pres-
ence in their localities (Fig. 3).
In the MKE, 32.4% (n =66) respondents and 5.9% (n =12) indicated
delayed reporting to their income related activities in the morning. In
AE, seven out of the 12 people reported to work at 9:00 am instead of the
planned 8:00 am. The remaining ve people reported to work at 8:00 am
instead of the scheduled 6:00 am to 7:00 am. In MKE, most respondents
said they were required to report to livelihood activities at 7:00 am
(9.3%, n =19) and 6:00 am (6.9%, n =14). However, most people
delayed, and reported at 8:00 am (17.6%, n =36) and 7:30 am (4.9%, n
=10).
The school time lost by children in the morning (t =8.669, d.f =211,
p =0.000) and in evening (t =5.101, d.f =216, p =0.000) was
signicantly different in AE and MKE, with the former losing 0.495 h
and 0.298 h more, correspondingly. Similarly, the time adults used to
escort children to school (t =8.166, d.f =284, p =0.000) and the time
delayed fetching water and re wood (t =3.424, d.f =52, p =0.001)
were signicantly different for the two ecosystems.
3.1.3. Time spent on property repairs and crop replanting
Eleven (11) water tanks in AE and 21 in MKE were damaged by el-
ephants within a period of one year. In addition, eight property fences in
AE and 25 in MKE were damaged within the same period. On average,
the time used to repair the damaged properties per year in AE (24.08 ±
5.33-h, n =12) was higher than in MKE (4.35 ±1.868 h, n =43). After
crop raiding by wildlife, households in AE spent an average of 124 ±
47.88 h replanting the crops, while those in MKE used 60.03 ±8.13 h to
crop replanting.
Table 1
Summary statistics of the study areas.
Variables AE MKE Sources
Average human
population density/
Km
2
52 170 CGK (2018);
CGM (2018)
Population growth
rate/annum
5.5% 2.1% KNBS (2019b)
Sex ratio (Male:
Famale)
50.2% 49.8% KNBS (2019b)
Road network 2419.2 Km 5968 km CGK (2018);
CGM (2018)
Education
a) Retention rate
b) Completion rate
c) Transition rate
67%
83%
89%
90%
78%
80%
CGK (2018);
CGM (2018)
Main occupation (%) 75%
pastoralism
Crop farming KNBS (2019b)
Monthly income less
than KES 10,000 (US
$ 92.83)
64.2% 66.7% Manoa (2021)
Types of houses 67.1%
corrugated iron
sheet roofs
97.5%
corrugated iron
sheet roofs.
CGK (2018);
CGM (2018)
Main crops Maize & beans Maize, beans,
wheat &
potatoes
Manoa (2021)
No. of health facilities 253 498 CGK (2018);
CGM (2018) Poverty rate (%) 36.9 15.5%
Common problematic
species population:
a) African elephants
b) African lion
c) Spotted hyena
1800–2000
141
346
2000–3000
55
138
KWS (2020);
KWS (2010);
Kimiti et al.
(2019)
Table 2
Time lost during guarding.
Ecosystem N Mean-hrs S.E
Livestock day guarding hours AE 88 4.16 0.185
MKE 24 3.46 0.466
Livestock night guarding hours AE 89 3.63 0.126
MKE 23 2.48 0.338
Crop day guarding hours AE 51 4.57 0.249
MKE 98 4.39 0.178
Crop night guarding hours AE 50 3.88 0.180
MKE 82 2.86 0.1957
Table 3
School time lost.
Session Ecosystem N Mean S.E
Time lost in the morning AE 98 1.28 0.053
MKE 115 0.79 0.026
Time lost in the evening AE 93 1.22 0.044
MKE 125 0.93 0.037
Escort children to school AE 107 0.55 0.015
MKE 179 0.38 0.013
Time lost for delayed water and rewood
fetching
AE 46 1.50 0.060
MKE 8 2.25 0.412
D.O. Manoa et al.
Current Research in Environmental Sustainability 3 (2021) 100103
6
3.2. Monetary opportunity cost
3.2.1. Amount spent guarding against wildlife
Individual households in AE spent a KES 137,570.22 (US$1262) on
livestock guarding compared to MKE who spent KES 84,011.36 (US$
770.75) per year (Table 4). In addition, the amount AE households spent
on crop guarding, KES 70,970 (US$ 651) was higher than in MKE, KES
47, 298.39 (US$ 434). Some households hired guards to keep off wildlife
from their crops and livestock. The average amount spent per year on
hired livestock guards by households in AE (KES 46,835.82 ±2115.35
(US$ 430), n =67) was higher than in MKE (KES 34,166.75 ±5976.98
(US$ 313.50), n =12). Similarly, the amount used to hire guards to scare
off wildlife from farms in AE (KES 31,888.89 ±6221.48 (US$ 293), n =
9) was higher than in MKE (KES 18,497.75 ±1545.25 (US$ 170), n =
89).
The t-test for the amount spent by respondents in AE and MKE on
crop and livestock guarding both for household members and hired la-
bour was signicantly different (Table 5), with the expenditure in AE
being higher than in MKE.
3.2.2. Money spent on property repairs and crop replanting
The average amount spent on material and labour for repairing
damaged water tanks and property fences in AE, KES 12,686.67 ±
4351.51(US$117.77 ±40.40), n =15) was almost equal to that spent in
MKE, KES 12,118.61 ±1186.39 (US$ 112.50 ±11.01), with a slight
difference of KES 568.06 (US$ 5.27) per year per household. Other than
property repairs, respondents in both AE and MKE, indicated that they
spent an average of KES 30,185 ±9989 (US$280.21 ±92.73) and KES
21,005.59 ±3166.86 (US$ 194.99 ±29.40) respectively replanting
their crops after wildlife raids.
3.2.3. Money spent on other HWC mitigation measures
The other common mitigation measures used to protect crops from
wildlife are shown in Table 6. They include scarecrows (Fig. 4), fencing,
dogs, light and noise emitting devices such as old magnetic tapes. Farm
fencing using barbed wire and rolls of twisted chain-links was the most
expensive method used in AE (KES 34,423.08 (US$316), n =13) and
MKE (KES 23,833.33 (US$ 218.70), n =6). Unlike in MKE, dogs and
noise mitigation measures were not used to protect crops in the AE.
Just like in crop mitigation measures, the use of livestock enclosure
sheds (boma) using chain-link fence (Fig. 5) was the most expensive
method used in AE, KES, 45,718.92 (US$ 419.44) and MKE, KES 23,250
(US$ 213.3) as shown in Table 7. The most common method used for
livestock protection was a hedge fence, with 158 households or 65.8% of
the sampled households using it. On average, the cost of the hedge fence
was higher in AE (KES 11,289.29 (US$104), n =140) compared to MKE
Fig. 3. An elephant blocking the way for community travelling to Kimana market in the AE in 2019.
Table 4
Amount spent in KES and US$ on crop and livestock guarding.
Expenditure Ecosystem N Mean (KES) S.E
Amount household spent on
crop guarding
AE 50 70,970.00 (US$
650)
6209.20
MKE 93 47,298.39 (US
$434)
3040.75
Amount spent on hired
labourer to guard crops
AE 9 31,888.89 (US$
293)
6221.48
MKE 89 18,497.75 (US$
170)
1545.25
Amount household spent on
guarding livestock
AE 89 137,570.22 (US$
1262)
11,794.88
MKE 22 84,011.36 (US$
770)
9610.17
Amount spent on hired
labourer to guard livestock
AE 67 46,835.82 (US$
430)
2115.35
MKE 12 34,166.75 (US$
314)
5976.98
Table 5
T-test on money in KES spent on livestock and crop guarding.
Expenditure t-test
values
d.f Sig. (2-
tailed)
Mean
Difference
Remarks
Amount spent on
crop guarding
3.847 141 P =
0.000
23,671.613 Signicant
Amount spent on
hired labourer to
guard crops
2.559 96 P =
0.012
13,391.136 Signicant
Amount household
spent on guarding
livestock
2.207 109 P =
0.029
53,558.861 Signicant
Amount spent on
hired labourer to
guard livestock
2.266 77 P =
0.026
12,669.071 Signicant
Table 6
Costs for crop protection methods used in AE and MKE.
Crop mitigation
measures
Ecosystem N Mean (KES) S.E
Scarecrows AE 7 685.71 (US$6.29) 120.37
MKE 55 1068.18 (US$ 9.80) 74.92
Fencing AE 13 34,423.08 (US$ 316) 11,720.41
MKE 6 23,833.33 (US$
218.70)
11,402.97
Dogs guarding AE – -. -.
MKE 55 2005.45 (US$ 18.40) 116.10
Lighting devices AE 3 4033.33 (US$ 37) 260.34
MKE 19 4063.16 (US$ 37.30) 407.69
Noise devices AE – -. -.
MKE 26 1234.62 (US$ 11.33) 206.21
D.O. Manoa et al.
Current Research in Environmental Sustainability 3 (2021) 100103
7
(KES 7150.00 (US$ 65.60), n =18). Similarly, the average cost of night
lighting devices used in AE, KES 17,017.44 (US$ 156) was twice the cost
in MKE, KES 8375.00 (US$ 76.83).
3.2.4. WTP and WTA for Hidden costs
Respondents in MKE were willing to pay and accept higher rates for
various hidden costs than their counterparts in AE (Table 8). The highest
mean WTA by respondents per day for time loss in income generating
activities was KES 255.64 ±15.93 (approx.US$ 2.37) in AE and
KES412.76 ±12.54 (approx. US$3.83) in MKE. Similarly, time loss for
income-generating activities elicited the highest WTP in AE, KES102.44
±7.99 (approx. US$ 0.94) and in MKE, KES 118.45 ±9.34 (approx.US$
1.10). The lowest WTP and WTA was recorded in AE for restricted night
travel of KES 43.13 ±3.19 (approx.US$ 0.40) and KES 84.22 ±5.78
(approx.US$ 0.78) respectively. Generally, the WTA for the various
hidden costs was double the respective WTP values.
Fig. 4. A scarecrow in a beans eld at Imuruto village in AE.
Fig. 5. Cattle entering predator-proof shed (boma) at Inkorienito village in
Amboseli Ecosystem.
Table 7
Cost in KES and US$ for livestock protection measures used in AE and MKE.
Livestock mitigation
measures
Ecosystem N Mean S.E
KES (US$)
Hedge AE 140 11,289.29(US$104) 822.80
MKE 18 7150.00 (US$ 65.60) 819.38
Chain-link fence AE 37 45,718.92 (US$
419.44)
3798.49
MKE 44 23,250.00 (US$
(213.3)
1735.75
Scarecrow AE 12 808.33 (US$ 7.42) 83.90
MKE 4 975.00 (US$ 8.95) 184.28
Dogs AE 41 1951.22 (US$ 17.90) 584.12
MKE 23 2206.52(US$ 20.24) 261.20
Lighting devices AE 39 17,017.44 (US$
156.12)
2134.50
MKE 4 8375.00 (US$ 7.68) 1434.33
Table 8
WTP and WTA per day in KES and US$ for different hidden HWC.
WTP/WTA Ecosystem N Mean KES
(US$)
S.E
WTP to mitigate diseases AE 156 61.06 (US$
0.57)
4.46
MKE 80 67.50 (US$
0.63)
6.59
WTA compensation for diseases AE 156 126.67(US$
1.18)
9.82
MKE 80 155.81(US$
1.45)
19.51
WTP for fear of attack AE 164 65.88 (US$
0.61)
12.75
MKE 128 68.56 (US$
0.64)
3.43
WTA compensation for fear of
attack
AE 163 112.91(US$
1.05)
8.14
MKE 129 143.02 (US$
1.32)
8.62
WTP for restricted night time travel AE 83 43.13 (US$
0.40)
3.19
MKE 122 69.06 (US$
0.641)
3.28
WTA compensation for restricted
night time travel
AE 83 84.22 (US$
0.78)
5.78
MKE 121 129.96 (US$
1.21)
6.79
WTP for missing social gathering AE 106 52.50 (US$
0.48)
4.80
MKE 95 63.90 (US$
0.59)
3.47
WTA compensation for missing
social gathering
AE 106 118.11(US$
1.10)
14.61
MKE 97 124.02 (US$
1.151)
6.77
WTP for school absenteeism AE 84 66.25 (US$
0.62)
5.55
MKE 121 97.85 (US$
0.91)
4.71
WTA compensation for school
absenteeism
AE 84 128.57 (US$
1.19)
10.96
MKE 119 215.50 (US$
2.00)
19.52
WTP for loss of sleep AE 139 60.29 (US$
0.56)
3.50
MKE 105 81.38 (US
$0.76)
3.97
WTA compensation for loss of sleep AE 139 114.33 (US$
1.06)
6.98
MKE 105 177.33 (US$
1.65)
10.37
WTP for missing income generating
activity
AE 101 102.44 (US$
0.95)
7.99
MKE 116 118.45 (US$
1.10)
9.34
WTA compensation for missing
income generating activity
AE 101 255.64 (US$
2.37
15.93
MKE 116 412.76 (US$
3.83)
12.54
D.O. Manoa et al.
Current Research in Environmental Sustainability 3 (2021) 100103
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4. Discussion
4.1. Time opportunity costs
4.1.1. Time spent guarding against wildlife
The AE households spent more time guarding their livestock and
crops both during the day and night than those in MKE. The time used to
guard property against wildlife at night in AE and MKE were signi-
cantly different (P <0.05), but the equivalent time spent on the same
during the day was similar in the two study areas. The difference in
guarding time can be attributed to wildlife movements and imple-
mentation of deterrent measures. In AE, the Amboseli National Park is
not fenced, thereby granting free movement of wildlife between the park
and the community group ranches, where human settlements and pri-
vate property are located. The park represent about 8% of the entire
ecosystem, which is a small area for a huge wildlife population including
some problematic species such as elephants, lions, and hyena, whose
home ranges are estimated at 5200–7790 km
2
((Ngene et al., 2017),
28–37 km
2
(Tuqa et al., 2014) and 24–1000 km
2
(Hofer, 2002),
respectively.
In MKE, there is regular wildlife movement between Mt. Kenya Na-
tional Park and Forest Reserve and the adjacent conservancies and for-
ests. However, the MKE is characterized by several electric fences
around neighbouring conservation areas, which minimises wildlife
entry into human settlements. For example, the movement of elephants
from Mt. Kenya Forest Reserve into Lewa Wildlife conservancy is
controlled by an electric fence along the designated corridor that links
the two conservation areas, with an underpass along the Nanyuki-Meru/
Isiolo highway. Since 2016, the Big Life Foundation has been erecting
several short electric fences around AE (Big Life Foundation, 2020).
However, this was done for selected high crop farming areas on the
southern part of the Amboseli, Kimana and Namelok irrigation farms,
leaving out other areas such as Kuku, Rombo, Imbirikani, Eselenkei and
Olgulului. Consequently, households in other AE had to spend more time
guarding their livestock and crops because of the widespread presence of
wildlife in human settlement areas.
Guarding against wildlife property damages is a common practice in
areas where people live in close proximity to wildlife habitats around
the world. The ndings of this study are similar to the observation by
Howard (1995) in Nyabyeya forest reserves in Uganda, where the
highest cost of crop guarding against destruction was $96–$519 per
household. In another study conducted in Tanzania around Mpanga/
Kipengere Game Reserve, 53.4% (n =90) respondents indicated that
they guarded their crops against wild animals both during the day and
night time (Mashalla and Ringo, 2015).
The ndings in this study are similar to the study by Musyoki (2014)
who established that farmers in Mahiga “B” village in Nyeri County,
spent substantial time guarding their crops against wildlife raids. The
difference in time scheduling for guarding in Mahiga “B” and the timings
recorded in this study can rst be attributed differences on the time
when the two studies were conducted including the sample size, inter-
view duration and study locations. Musyoki's study only covered 5
months (August–December) and was based on 9 farmers, while this
study was based on a 12-month period with a sample size of 408 re-
spondents. In addition, in Musyoki's study area, a 1000 km of electric
fence has been erected around the Aberdare Mountains and Mt. Kenya to
reduce contact between people and wildlife (Pearce, 2015).
Spending time guarding livestock and crops has several social-
economic implications to people. Firstly, night guarding denies people
opportunities to engage in other income generating activities during the
day due to lack of sleep. Secondly, as outlined by Barua et al., (2013)
guarding against dangerous and feared wildlife species such elephants is
associated with fatigue and alcohol abuse for anxiety relieve and fear
mitigation among adults. Based on the average casual wages paid in AE
(KES 50) and MKE (KES 75) per hour as observed from the two study
areas, then the average combined time lost guarding livestock per
household per day in AE was KES 389.0, compared to KES 445.50 in
MKE. Equally, for crop guarding, a household in AE lost up to KES
422.50 per day while those in MKE lost KES 543.75 per day. This is a
considerable amount of money to lose per day for people who are
majorly rural, with 40% living in poverty (Kenya Institute for Public
Policy Research and Analysis-KIPPRA, 2020).
4.1.2. School time lost and delays in reporting to income generating
activities
The schooling hours for children in both ecosystems was affected
because of wildlife presence but those in AE were affected more than the
ones in MKE. Household activities by parents was also affected due to
the need to escort children to school for safety reasons. It was observed
that livestock in the two areas are released from the kraals to start
grazing between 8:30 am and 9:30 am. More time was lost by children
and adults in AE due to the location of schools within wildlife dispersal
and migratory routes compared to MKE. According to Croze and Moss
(2011) wildlife species such as elephants, zebra and buffaloes spent
about 80% of their time outside the Amboseli National Park. The Park is
not fenced and there is free movement of wildlife compared to MKE,
where wildlife movement is restricted by the wide spread electric fences
around conservation areas. As such, children have to wait for wildlife to
either retreat back into the park or in the bush within their home lo-
cations. In the evening, children have to leave school early before the
wildlife start moving into the human settlement areas. During the
eldwork, it was observed that villages such as Ol moti, Olgulului, Risa,
Injakta, Lenkisem were all close to community boreholes which were an
attraction to wildlife as sources of water.
The ndings in AE were similar to a study conducted on communities
bordering protected areas in Tanzania, which showed that 41.3% of the
children usually encountered wildlife on their way to school, mostly in
the morning and evening. The study showed that all the 46 students
interviewed, had encountered an elephant, mostly when the animals
were drinking water at the boreholes (Sayuni and Sengelela, 2019). In
addition, Sayuni and Sengelela further notes:
“When pupils encounter elephants, some go back home, some wait for the
elephants to pass by or use another road or look for someone to assist.
Sometimes they fail to attend classes or arrive very late, sometimes at 10
am instead of 7:30 am, so they miss some subjects/lessons. The villages
are very distant, and the houses are distant too”
Therefore, wildlife presence in communities can seriously interfere
with children education. Those who report late in morning and leave
early in the evening usually miss some lessons, which can negatively
affect their performance in national exams and long-term performance
in life. This problem has been reported in other parts of Kenya. For
example, a study by Sitati et al. (2012) on schools in Transmara District
in Kenya, established that pupils living within the elephant ranges who
had missed school for 20–60 days had lower mean scores (216–282
marks) in the nal national exam compared to those outside elephants
ranges (246–323 marks). This is likely to affect the long-term profes-
sional lives for people in wildlife areas who can lag behind other soci-
eties in a country.
Wildlife did not only interfere with the children school time, but also
their parents. The presence of wildlife prevented people from attending
to their different social and economic activities on time. More people
(32.4%) in MKE were affected than in AE (5.9%). This is because most
households in MKE are crop farmers who need to wake up early in the
morning to attend to their crops as well as assessing the damage caused
by wildlife overnight. The people in AE are, typically pastoralists for
whom livestock grazing usually starts when the morning dew has
cleared, and predators retreated into the thicket and parks. Wildlife
restriction on people's movement is not a new phenomenon. In 2003,
residents of Taita-Taveta County were blocked from attending to their
socio-economic activities because of uncontrolled movement of wildlife
D.O. Manoa et al.
Current Research in Environmental Sustainability 3 (2021) 100103
9
in villages and farms around Tsavo National Park (Kimega, 2003).
Kimega noted that during the dry seasons, women in the Taita –Taveta
County were restricted from fetching water as result of roaming ele-
phants around the water supply points.
4.1.2.1. Time spent on property repairs and crop replanting. The repair of
damaged water tanks, fences, and other HWC related trouble-shooting
facilities were found to consume considerable time at household level
in the two ecosystems. Although MKE households had more water tanks
and fences damaged by wildlife, the time used for repairs was higher in
AE than in MKE. The difference can be associated with the extent of the
damage, technical knowhow, and the availability of repair tools. Most
MKE households, who largely depend on agriculture have tools such as
hoes, machetes, and hammers that are required for repairs. In addition,
this study found out that more people in MKE had formal education
compared to those in AE and were hence relatively more exposed to the
required technical skills.
4.2. Monetary opportunity costs
4.2.1. Monetary cost of guarding against wildlife
The study established that a lot of money was spent on guarding
crops and livestock against wildlife in the two ecosystems. However, the
amount spent in AE was signicantly higher than in MKE. Households in
AE had to forego a total of KES 255,376.04 (US$ 2343) per annum in
safeguarding livestock and crops compared to KES 165,476.50 (US$
1518). Overall, these gures are higher than the total income earned
from all sources by a household in AE (KES 120,000.70 (US$ 1100.92)
and MKE, KES 107,968.02 (US$ 990.53), implying that the return on
investment was negative/loss. An analysis of crop loss in South Luangwa
in Zambia and Tarangire in Tanzania also revealed that the mean loss
due to single crop raiding by wildlife exceeded the monthly rural per
capita income of a farmer (Gross et al., 2019).
Spending money on livestock and crop guarding against wildlife is
widespread practice. For example, in South Africa the need to protect
livestock from carnivores has forced some farms to invest up to 300
livestock guarding dogs (Stannard and Cilliers, 2018). However, ac-
cording to Rust et al. (2013) who investigated 94 farms that had invested
in 97 dogs to guard against wildlife, the maintenance cost of a single dog
per year was approximately US$ 2780 which was quite expensive for
small-scale farmers to afford. In Uganda, a study conducted in Hoima
District by Kate (2012) established that a farmer spent between $10–35
per month to hire extra labour to guard their farms against baboons.
Similarly, in Narok County (Kenya), Korir (2015) reported that soya
beans farmers were forced to employ at least three workers to guard
their farms against zebras and gazelles raids. This forced each farmer to
spend an average of KES 18,000 (US$165.14) per month on wages.
Spending money on property guarding against wildlife denies the farmer
the expected full prot from their livestock and crops. It also reduces the
famer's investment in agricultural produce and livestock because some
money has to be allocated for the guarding against wildlife.
4.2.2. Money spent on property repairs and crop replanting
This study did not nd any signicant difference in the money spent
on repairing damaged properties and replanting crops in AE and MKE.
Overall, the amount spent on repairs was less compared to money spent
on guarding crops and livestock. This nding is quite similar to the
national analysis of human-wildlife conict data between 2005 and
2016 in Kenya, which indicated that property damage constituted only
4% of the 29,647 reported HWC cases (Long et al., 2020). The existing
records show that destruction of water tanks and farm fences by wildlife
usually occurs mostly in dry seasons when wildlife move into human
settlement areas in search of water and pasture, and that could be reason
why the cases and related expenditure were lower for property damages.
In addition, some of the affected water tanks are communally owned,
which means that the damages are shared by many households thereby
lowering individual household expenditure per property damage.
Replanting crops in AE was found to be more expensive than in MKE.
This nding can be attributed to the difference in the farm sizes in the
two areas, with households in AE having twice the size of farms
compared to MKE. Other factors, such as physical and geographical
parameters, which were not investigated in this study, could also have
contributed to the difference. For instance, in a study conducted in farms
within Trans Mara County (Kenya), it was established that large farms
bordered by hedges were more likely to be raided (Sitati et al., 2005)
because hedges provided shelter and hiding to various wildlife species.
In addition, the study by Sitati et al. (2005) revealed that greater farm
guarding efforts and the use of early warning systems also determined
the level of crop raiding, and hence the amount used for replanting.
4.2.3. Money spent on mitigation measures
The hidden costs incurred through the money spent on the various
protection measures for crops and livestock in both AE and MKE were
similar, except for the installation of chain-link fences, scarecrows, dogs
and noise producing devices. This is because these methods are rela-
tively cheap to implement, compared to fencing and night-time light
producing devices including solar units. Most scarecrows were made of
sticks and old clothes while noise-producing devises were made using
materials such as old magnetic tapes and tin cans. These two methods
were implemented with the intention of frightening wildlife, especially
birds and small mammals. The ndings on the use scarecrows and
magnetic tapes in this study resembled those of a study conducted in
Machakos County, where 60% of the farmers preferred the use scare-
crows and magnetic tape to scare away birds based on their cost effec-
tiveness (Mutune, 2017). Similarly, a study undertaken in Moi's Bridge,
where farmers encountered a 20% and 80% crop loss to birds and other
animals respectively, showed that they spent between KES 70–150 (US$
0.64–1.38) to install scarecrow (FarmbizAfrica, 2016). Another study
conducted in the same area by Nemtzov and Galili (2006), revealed that
each scarecrow cost about US$ 10. In this study, scarecrows were
minimally used to frighten carnivores in both ecosystems. The low use of
scarecrows for livestock protection could be attributed to its ineffec-
tiveness as demonstrated by Woodroffe et al. (2006) in African
rangelands.
Dogs were used to protect crops and livestock in both AE and MKE
mainly for alerting households of wildlife invasion, as well as scaring
away small mammals and birds. Unlike the trained dogs such as
Anatolian Shepherd used in Southern African countries, the people in AE
and MKE depended on ordinary untrained dogs whose cost ranged from
KES 1900–2200 (US$ 17.43–20.18) per dog compared to the trained
Anatolian Shepherd that cost between US$ 1000 in Tanzania (Ruaha
Carnivore Project, 2020) and US$ 2780 in South Africa and Namibia
(Rust et al., 2013). Although dogs have been documented to be effective
in guarding sheep against cheetah and other small carnivores, studies
indicate that they are associated with some hidden ecological costs. For
example, an analysis of the 183 scats from six livestock guarding dogs in
South Africa revealed that the dogs preyed on 10 different wild mam-
mals (Drouilly et al., 2020). In Kisii (Kenya), an attempt by farmers to
protect their crops from monkeys using dogs was unsuccessful because
their barking whenever the monkey invaded the farms did not stop the
monkeys from crop raiding (Okoyo, 2016).
Light emitting devices such as solar units and ashlights were used
for the night-time guarding of livestock and crops in AE and MKE.
Overall, the lighting devices for crop protection cost about KES 4, 000
(US$36.70) for crop protection and KES 17,017.44 (US$156.12) and
KES 8375 (US$ 76.84) to implement in AE and MKE for livestock pro-
tection, respectively. The ashlight usually gives an illusion to the
invading wildlife that humans are in the farm or around the livestock
kraal. The difference in the hidden cost for the two areas can be linked to
the type of lighting device used. Some farmers simply used a recharge-
able solar panel with 3 bulbs, while others had a fully set solar icking
D.O. Manoa et al.
Current Research in Environmental Sustainability 3 (2021) 100103
10
lights connected to a car battery and solar panels. In AE, the relatively
high price for implementing night-time livestock protection light de-
vices can be attributed to the introduction and high demand for the
modied expensive unit by Coexistech Ltd. Elsewhere, a study under-
taken in the southern section of Nairobi National Park established that a
solar ashlight system introduced by Friends of Nairobi National Park
consisting of 4–6 bulbs at a cost of KES 25,000 (US$ 229.36) per unit
reduced livestock attacks by 96% (Lesilau, et al., 2018). Another study
carried out in Amboseli showed that ashlights were 90% effective in
keeping off predators from kraals (Okemwa, 2015).
The use of chain-link fences, also known as predator-proof boma, to
keep off predators from livestock enclosures was common the two eco-
systems. This involves fencing of livestock enclosures with rolls of
chains-links that are supported with strong posts and a metal door as
opposed to the popular hedge fence that consist of the acacia twigs. The
cost of chain-link fences was higher in AE than in MKE because, the
predator-proof boma design used in AE comprised of 1.8 m high-
recycled plastics poles with chain-links and attened iron drums. The
project was implemented by wildlife charity-Born Free Foundation. The
beneciaries paid 25% of the total cost (estimated to be KES 240,000
equivalent to US$2202) which correlates to the size and number of
livestock (Manoa and Mwaura, 2016). The lower cost of chain-link in
MKE is attributed to the fewer number of livestock per household (38)
compared to AE (98). In addition, the fence designs were different, with
people in AE having been improved their fences through better
communication, education and awareness training (Manoa and Kasaine,
2019). Elsewhere, a cost-benet analysis of predator-proof bomas in
Tanzania revealed that investing in boma fortication is cost effective
compared to the traditional fence as it yielded positive net present
values after two to three years (Kissui et al., 2019). The traditional hedge
fences are less effective because of their low height and the ability of the
predators to jump in and attack livestock (Manoa and Mwaura, 2016).
In addition to the above strategies, communities in AE and MKE also
used barbed wire fencing, but this method was only used by 4.66% of the
total respondents, which can be explained with the relatively high cost
required to install the fence, which ranges between KES 23,000 (US$
211) and 34, 500 (US$ 316.51). In addition to the rolls of barbed wires,
the fence also requires the purchase of installation poles, at the cost of
KES 200-1200(US$ 1.83–11) each, nails KES 150–250 (US$1.38–2.29)
per kg and labour. It is projected that fencing an acre of farm would
comprise 102 posts, 2 rolls of barbered wires, 3.5kgs of nails and labour
are required, all totalling to about KES 40,000 (US$ 366.97) (EcoPost,
2020).
4.2.4. WTP and WTA for hidden costs
Respondents expressed their willingness to accept compensation and
willingness to pay for the various hidden costs associated with HWC. The
daily WTA and WTP values for households was higher in MKE than in
AE. The WTA values for different opportunity costs were higher than the
WTP by about 50%. The differences in the two values have been docu-
mented in other previous environmental economics studies as reviewed
by Gregory & Brown (1999), KNBS. (2019a). Enhanced Food Balance
Sheet for Kenya, 2014-2018 Results. Government of Kenya with a WTA:
WTP ratio ranging of 1.4–61.0. The disparity in the WTA and WTP has
been attributed to the fact that losses matter more to people compared to
commensurate gains and reductions in losses are worth more than
foregone gains. Most CVM studies in the world have reported exagger-
ated WTAs compared to the WTP. For example, duck hunters were
willing to pay US$ 247 above the real cost to waterfowl for one year but
demanded a minimum of US$ 1044 to forego the opportunity to hunt the
same birds (Hammack & Brown, 1974).
5. Conclusion and recommendations
This study has demonstrated that time and monetary opportunity
costs can be characterized, quantied and compared across ecosystems.
Although, the two study areas experienced hidden costs, time and
monetary opportunity costs incurred by households in AE were higher
than MKE. AE households spent an average of 7.79 h during the day
guarding livestock and crops compared to MKE households who spent
5.94 h. This suggest that the magnitude of hidden costs is largely
dependent on the types of wildlife species, their ease of movement and
land use practices. MKE has several electric fences that reduced wildlife
from accessing human settlements, and hence less time and money op-
portunity costs. In addition, physical barriers such as electric fences also
inuenced the time and monetary opportunity cost of HWC. Although,
physical barriers are not a hundred percent effective in barring wildlife,
it is likely that investment in such structures by the government and
conservation stakeholders can help people living in wildlife areas to
reduce hidden costs of HWC. This study reveals that HWC results to
sleepless nights, reduced school time and lower crop yields. The reduced
school attendance can result to poor performance in national exams,
poor progression in student careers, while sleepless nights results to
health problems and drugs abuse.
It is therefore imperative for the government to incorporate the op-
portunity costs of HWC and measures of addressing them. Hidden costs
such as opportunity cost are likely to promote community resentment
towards wildlife conservation because of the substantial amount of time
and money spent on HWC compensation with marginal success. Instead
of the government policy focusing on compensation for visible cost,
effort should go to minimising hidden costs through investment in
preventive measures and improving the already existing measures that
the community living in wildlife areas have adopted. This will go a long
way in reducing interruption of education goals and people's career,
proper health and psychological well-being. Since wildlife conservation
does not mean the same thing to different stakeholders, the HWC policy
should be revised together with other policies such as land, agriculture,
mining, water and forestry, for conformity and addressing contradicting
areas. As demonstrated by the AE and MKE study sites, the HWC policy
must recognize the need for tailor -made solutions that are site specic,
rather than generalizing. In addition, for the HWC policy to be effective
and practical, the government together with wildlife stakeholders must
have an implementation plan that is strongly supported by the necessary
human and nancial resources needed to deal with the HWC. As such,
the government can embrace geo-spatial technology maps to establish
household base in wildlife conict zones capturing resources like land
titles to speed-up process of HWC claims and agony of proong. MKE has
high fencing, than AE, thus working with private sectors to strategically
useelectric fences can help to deter wildlife movements thus reducing
people's livelihood disruption. It is also imperative for the government to
adopt and promote modern technology like mobile phones to minimise
cost of proof of damage on livelihood and in the process accumulate
database of hotspots where priority mitigation like surveillance and
electric fencing would be implemented. Providing subsidies for HWC
deterrent devices such as predator-proof bomas, the same way the
government subsidises fertilizer and seeds to farming communities can
also help to reduce the burden of HC. There is also a need to consider
insurance cover like in livestock sector, where technology has been
employed to improve data report accuracy.
The ever-growing backlog of unpaid compensation claims for losses
incurred through both visible and hidden costs of HWC in Kenya might
require a comprehensive review of the compensation policy and legal
framework. This should focus on the identication of alternative
compensation options and strategies including tax rebates and other
goodies for the HWC loss victims in order to sustain coexistence between
society and wildlife. These options can include tax reliefs and conces-
sions including waivers on county land rates or at least special dis-
counted land rates. Other alternative offers could include income tax
rebates on employment, investment and business income including
business licenses for the victims and their families. In addition, they
could also benet from educational grants and bursaries as well as free
social security and government national health insurance.
D.O. Manoa et al.
Current Research in Environmental Sustainability 3 (2021) 100103
11
Declaration of Competing Interest
The authors declare that they have no known competing nancial
interests or personal relationships that could have appeared to inuence
the work reported in this paper.
Acknowledgement
This work was supported by the Born Free Foundation grant and
authorised by National Commission for Science, Technology & Innova-
tion (Permit No. NACOSTI/P/18/38627/23786) and Kenya Wildlife
Service (ref: KWS/BRPM/5001).
Appendix A. Supplementray data
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