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Comparative analysis of time and monetary opportunity costs of human-wildlife conflict in Amboseli and Mt. Kenya Ecosystems, Kenya



Traditionally, the cost of Human-wildlife conflict (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. Opportunity 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 significantly 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.
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
Comparative analysis of time and monetary opportunity costs of
human-wildlife conict in Amboseli and Mt. Kenya Ecosystems, Kenya
D.O. Manoa
, F. Mwaura
, T. Thenya
, S. Mukhovi
Born Free Kenya, P.O. Box 1519-00502, Nairobi, Kenya
Department of Earth and Climate Science, University of Nairobi, P. O. Box 30197-00100, Nairobi, Kenya.
Human-wildlife conict
Opportunity costs
Time & Monetary loss
Mt. Kenya
Traditionally, the cost of Human-wildlife conict (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 signicantly 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 conict (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, prots 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$194.0
million (McKee et al., 2021). In Brazil, the Military Highway Police of
ao Paulo documents an average of 2611 animal-vehicle crashes per
year, with 8.5% of cases resulting to human injuries or fatalities (Abra
et al., 2019). In addition, Abra et al., 2019 estimated the annual loss of
* Corresponding author at: Born Free Kenya, P.O. Box 1519-00502, Nairobi, Kenya.
E-mail address: (D.O. Manoa).
Contents lists available at ScienceDirect
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Received 29 June 2021; Received in revised form 4 November 2021; Accepted 9 November 2021
Current Research in Environmental Sustainability 3 (2021) 100103
US$ 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,
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
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 difcult 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
certicates 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 conicts 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) afrmed 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 dened as the loss or sacrice incurred by taking
a particular action against HWC instead of other more preferred and
benecial 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, quantication 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 difcult
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 specic 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
(Fig. 1). Kajiado County (365
̕, 3755
̕ E; 110
̕, 310
̕̕ 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
(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 (025
̕ S, 010
̕ N; 3700
̕ E,
̕ 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
. This part of the ecosystem was the focus of the
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 20003000 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 OctoberDecember (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
C to 34
C, MKE registers slightly lower tem-
peratures of as low as 8
C and as high as 32
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
with about 75% of the residents depending on
pastoralism for their income ((Table 1, KNBS, 2019a). However, there is
growing inux 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
, while Laikipia County, which is a semi-arid area has 52
people per km
(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 simplied Yamane (1967)
formula to calculate the sample size as follows:
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
(199,964), the sample size of 204 households per ecosystem was
MKE sample size =520,175
AE sample size =199,964
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 identied 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)
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 signicant differences between opportunity
cost in AE and MKE.
2.3. Willingness to pay (WTP) and willingness to accept (WTA)
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).
1US$ =KES 107.72
D.O. Manoa et al.
Current Research in Environmental Sustainability 3 (2021) 100103
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 signicantly 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 ofcial
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
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
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
signicantly 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 signicantly 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/
52 170 CGK (2018);
CGM (2018)
Population growth
5.5% 2.1% KNBS (2019b)
Sex ratio (Male:
50.2% 49.8% KNBS (2019b)
Road network 2419.2 Km 5968 km CGK (2018);
CGM (2018)
a) Retention rate
b) Completion rate
c) Transition rate
CGK (2018);
CGM (2018)
Main occupation (%) 75%
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
corrugated iron
sheet roofs.
CGK (2018);
CGM (2018)
Main crops Maize & beans Maize, beans,
wheat &
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
KWS (2020);
KWS (2010);
Kimiti et al.
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
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
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 =
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 signicantly 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$
MKE 93 47,298.39 (US
Amount spent on hired
labourer to guard crops
AE 9 31,888.89 (US$
MKE 89 18,497.75 (US$
Amount household spent on
guarding livestock
AE 89 137,570.22 (US$
MKE 22 84,011.36 (US$
Amount spent on hired
labourer to guard livestock
AE 67 46,835.82 (US$
MKE 12 34,166.75 (US$
Table 5
T-test on money in KES spent on livestock and crop guarding.
Expenditure t-test
d.f Sig. (2-
Amount spent on
crop guarding
3.847 141 P =
23,671.613 Signicant
Amount spent on
hired labourer to
guard crops
2.559 96 P =
13,391.136 Signicant
Amount household
spent on guarding
2.207 109 P =
53,558.861 Signicant
Amount spent on
hired labourer to
guard livestock
2.266 77 P =
12,669.071 Signicant
Table 6
Costs for crop protection methods used in AE and MKE.
Crop mitigation
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$
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
(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
Ecosystem N Mean S.E
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$
MKE 44 23,250.00 (US$
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$
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
WTP to mitigate diseases AE 156 61.06 (US$
MKE 80 67.50 (US$
WTA compensation for diseases AE 156 126.67(US$
MKE 80 155.81(US$
WTP for fear of attack AE 164 65.88 (US$
MKE 128 68.56 (US$
WTA compensation for fear of
AE 163 112.91(US$
MKE 129 143.02 (US$
WTP for restricted night time travel AE 83 43.13 (US$
MKE 122 69.06 (US$
WTA compensation for restricted
night time travel
AE 83 84.22 (US$
MKE 121 129.96 (US$
WTP for missing social gathering AE 106 52.50 (US$
MKE 95 63.90 (US$
WTA compensation for missing
social gathering
AE 106 118.11(US$
MKE 97 124.02 (US$
WTP for school absenteeism AE 84 66.25 (US$
MKE 121 97.85 (US$
WTA compensation for school
AE 84 128.57 (US$
MKE 119 215.50 (US$
WTP for loss of sleep AE 139 60.29 (US$
MKE 105 81.38 (US
WTA compensation for loss of sleep AE 139 114.33 (US$
MKE 105 177.33 (US$
WTP for missing income generating
AE 101 102.44 (US$
MKE 116 118.45 (US$
WTA compensation for missing
income generating activity
AE 101 255.64 (US$
MKE 116 412.76 (US$
D.O. Manoa et al.
Current Research in Environmental Sustainability 3 (2021) 100103
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 52007790 km
((Ngene et al., 2017),
2837 km
(Tuqa et al., 2014) and 241000 km
(Hofer, 2002),
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 Bvillage in Nyeri County,
spent substantial time guarding their crops against wildlife raids. The
difference in time scheduling for guarding in Mahiga Band 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 (AugustDecember) 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
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 2060 days had lower mean scores (216282
marks) in the nal national exam compared to those outside elephants
ranges (246323 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
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. 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 signicantly 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 $1035
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 prot 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 signicant 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 conict 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 70150 (US$
0.641.38) 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
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 19002200 (US$ 17.4320.18) 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
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
modied 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 46 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
beneciaries paid 25% of the total cost (estimated to be KES 240,000
equivalent to US$2202) which correlates to the size and number of
livestock (Manoa and Mwaura, 2016). The lower cost of chain-link in
MKE is attributed to the fewer number of livestock per household (38)
compared to AE (98). In addition, the fence designs were different, with
people in AE having been improved their fences through better
communication, education and awareness training (Manoa and Kasaine,
2019). Elsewhere, a cost-benet analysis of predator-proof bomas in
Tanzania revealed that investing in boma fortication is cost effective
compared to the traditional fence as it yielded positive net present
values after two to three years (Kissui et al., 2019). The traditional hedge
fences are less effective because of their low height and the ability of the
predators to jump in and attack livestock (Manoa and Mwaura, 2016).
In addition to the above strategies, communities in AE and MKE also
used barbed wire fencing, but this method was only used by 4.66% of the
total respondents, which can be explained with the relatively high cost
required to install the fence, which ranges between KES 23,000 (US$
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$ 1.8311) each, nails KES 150250 (US$1.382.29)
per kg and labour. It is projected that fencing an acre of farm would
comprise 102 posts, 2 rolls of barbered wires, 3.5kgs of nails and labour
are required, all totalling to about KES 40,000 (US$ 366.97) (EcoPost,
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 1.461.0. 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, quantied 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
inuenced 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 specic,
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 conict zones capturing resources like land
titles to speed-up process of HWC claims and agony of proong. 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 identication 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 benet 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
Declaration of Competing Interest
The authors declare that they have no known competing nancial
interests or personal relationships that could have appeared to inuence
the work reported in this paper.
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).
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... Indirect costs are the financial investment incurred in repairing of damage properties (e.g., fence or house) and opportunity cost is the loss of income due to fore gone activities (e.g., crop guarding). This is consistent with other studies in which HWC forced farmers to take one action instead of other more preferred and beneficial activity [69,70] thereby losing income. For instance, in some parts of Tanzania farmers must walk longer distance to collect water as water pipes were damaged by elephants [71], an activity often at the expenses of other more beneficial work [9]. ...
... Kenya in Kenya spent USD 1913.00 and USD 1205.00, respectively on average for guarding livestock and crops, an amount much higher than their annual income [69]. ...
... In nearby Nepal, maximum human injuries and death caused by wildlife are found to occurred during the night [78]. Similarly in the Amboseli region of Kenya, pastoralists spent most nights in the wet season guarding their livestock against predators [69]. Our study found that the physical and mental hardships associated with night crop guarding can have health implications possibly affecting human capital, which is consistent with the findings of [9,79] who pointed out the possible health implications of night crop guarding. ...
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Indirect impacts of Human Wildlife Conflict (HWC) are largely ignored, poorly understood, and scantly reported in the literature on HWC. Subsistence farmers in the Himalayan kingdom of Bhutan experience an increasing intensification of HWC impacts. Working across four districts representing different geographic regions of the country, we explored the perceived indirect impacts of HWC and how they affect the well-being and happiness of subsistence farmers using qualitative interviews (n = 48) and focus group discussions (n = 8). We conducted a qualitative thematic analysis. Based on respondent’s explanations, we coded the data according to effect of indirect impacts on human, social, financial, physical, natural, and psychological capitals. Mental distress, constant worries about food insecurity, fears for physical safety, frustration of movement restriction due to fear of being attack by wildlife, feelings of economic insecurity and anger over loss of crop and livestock due to wild predators affect the psychological health and well-being of research participants. Vulnerabilities related to gender and wealth status further deepen the effect of indirect impacts. Policies designed to address HWC should incorporate an understanding of the effects of indirect impacts of HWC and should focus on female-headed and poor households to reduce the negative effects of wildlife impacts.
... The result showed the Elephants have negatively affected local communities attacking and killing humans and livestock, destroying irrigation materials, food stores, and crops. This finding is similar to (Munyao et al., 2020;Manoa et al., 2021) which have shown that the level of destruction of the elephants ranges from severe crop-raiding to killing of people, in which the species become the most dangerous and damaging. Moreover, from observation during the present study, various crops (such as vegetables, fruits, oilseeds, and cereal crops) were damaged by elephants during the day and night periods in all four study sites(Eree ebada, Bilisuma and Alola)"kebeles'' due to trampling (Figure 4). ...
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The focus of this paper is to study the Human-Elephant Conflict (HEC), Prevention, Mitigation methods in Babile Elephant Sanctuary, Eastern Ethiopia. Purposive and random sampling methods were used for primary data collection. Accordingly, a total of 138 Households were selected from four Peasant Associations that were close to the sanctuary for interview. Moreover, Focus group discussion, site observations, and secondary data of the last five years (2016-2020) related to HEC. The result showed, 87.7% of respondents thought that crop-raiding by elephants and 85% of them viewed, death of 16 elephants by humans were the cause for conflict. Above 54.3% and 37.7% of them also thought as 22 human and 24 livestock died respectively. Besides, 27% of them indicated, 647.32 quintal of Zea mays (31.27 %) and Sorghum bicolor (46.22%) costing, 67,107.2 ETB-Ethiopian Birr (out of 87,657.27 ETB or 2,369.62 USD) were lost. However, all (100%) of them thought that there was no compensation given for the lost crops. Visual signals (setting fire and lighting torches) and hitting metal objects (acoustic methods) were used as a major mitigation measure. While beehives fences and digging trenches were identified as minor preventive measures. Based on the study results, the following inference is drawn: identifying and documenting the existence of HEC information to build the knowledge gaps on areas where these challenges prevails and implementing various measures of technical(biological and physical methods: farming of cash crops which is less attractive to elephants, fencing), socioeconomic (building community owner ship and educational programs to school, benefit community by employee in development works),and financial strategy set up (compensation losses and revenue sharing) are crucial methods to reducing conflict and co-exiting human and elephant.
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Human-wildlife conflicts (HWC) affect the social-economic aspects of millions of people across the world and is one of the most important challenges facing wildlife conservation. Long-term data collection provides an opportunity to critically understand HWC trends and enable wildlife stakeholders to create evidence-based solutions for coexistence of people and wildlife. We used Kenya Wildlife Service (KWS) data for the 2010-2018 period to analyse trends in typology, seasonality and economic costs of HWC in Kajiado and Laikipia Counties in Kenya. A total of 953 HWC reported cases in the two counties were analysed. Wildlife threats to human life, crop damage and livestock predation were the common form of HWC, contributing 65.7% (n=626), 21.7% (n=207), and 7.7% (n=73) respectively. Apart from livestock predation (t=2.431; P=0.028) all other types of HWC did not show any significant differences in the two counties over the nine-year period. Elephants were responsible for the highest conflict cases (79%, n=753) followed by baboons (6.9%, n=66). Elephants contributed to the highest human fatality and injuries (43%, n=10); while snakes and buffalo were second, each contributing to 17% (n=4) of the total cases. Majority of the HWC occurred in the dry season months of July (n=114), January (n=99) and October (n=96). The overall trend indicated increasing HWC cases over the 9 years in both counties. The analysis of economic cost of HWC showed that a total of 64.09 hectares of crops were damaged in 2010-2018, with 70% of the cases reported in Kajiado County. In terms of predation, Kajiado lost livestock worth KES 1,785, 000 (U$ 16,780.53) while Laikipia lost KES 407,000 (U$ 3826.15). This study provides empirical evidence that can be used to develop strategies for mitigating HWC based on types, seasons and conflict species.
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This paper reviews the Human-Wildlife Conflict (HWC) studies in Kenya with special interest on the hidden opportunity costs. The paper considered the negative implications to the victims of HWC and explores ways of ensuring full integration of all costs especially in regard to the search for more comprehensive compensation frameworks. One of the specific interests in the paper was to determine whether the hidden costs of HWC in Kenya are well considered in the Wildlife Conservation and Management Act 2013 (WCMA 2013) or whether there is need for a review and amendments. Data for this review was obtained from Google Scholar and Crossref references and citation-enhanced indexing databases. Content analysis from the two databases showed a lot of research interest on the cost of HWC to societies around conservation areas. Further content analysis revealed that most of the HWC costs estimation studies have mostly concentrated on the visible costs (127 publications and 1507 citations) without serious consideration of the hidden costs (33 publications and 893 citations). As such, more research is required on the hidden costs in order to formulate more effective HWC loss compensation frameworks as a strategy for ensuring sustainable coexistence between society and wildlife.
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Abstract Damage to homesteads by brown bears (Ursus arctos) has become commonplace in Asia, Europe, and the Americas. Science‐based solutions for preventing damages can contribute to the establishment of mechanisms that promote human–bear coexistence. We examined the spatial distribution patterns of house break‐ins by Tibetan brown bears (U. a. pruinosus) in Zhiduo County of the Sanjiangyuan region in China. Occurrence points of bear damage were collected from field surveys completed from 2017 to 2019. The maximum entropy (MaxEnt) model was then used to assess house break‐in risk. Circuit theory modeling was used to simulate risk diffusion paths based on the risk map generated from our MaxEnt model. The results showed that (a) the total risk area of house break‐ins caused by brown bears was 11,577.91 km2, accounting for 29.85% of Zhiduo County, with most of the risk areas were distributed in Sanjiangyuan National Park, accounting for 58.31% of the total risk area; (b) regions of alpine meadow located in Sanjiangyuan National Park with a high human population density were associated with higher risk; (c) risk diffusion paths extended southeast to northwest, connecting the inside of Sanjiangyuan National Park to its outside border; and (d) eastern Suojia, southern Zhahe, eastern Duocai, and southern Jiajiboluo had more risk diffusion paths than other areas examined, indicating higher risk for brown bear break‐ins in these areas. Risk diffusion paths will need strong conservation management to facilitate migration and gene flow of brown bears and to alleviate bear damage, and implementation of compensation schemes may be necessary in risk areas to offset financial burdens. Our analytical methods can be applied to conflict reduction efforts and wildlife conservation planning across the Qinghai–Tibet Plateau.
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Human-carnivore conflicts and retaliatory killings contribute to carnivore popula-tions' declines around the world. Strategies to mitigate conflicts have been developed, but their efficacy is rarely assessed in a randomized case-control design. Further, the economic costs prevent the adoption and wide use of conflict mitigation strategies by pastoralists in rural Africa. We examined carnivore (African lion [Panthera leo], leopard [Panthera pardus], spotted hyena [Crocuta crocuta], jackal [Canis mesomelas], and cheetah [Acinonyx jubatus]) raids on fortified (n = 45, total 631 monthly visits) and unfortified (traditional, n = 45, total 521 monthly visits) livestock enclosures ("bomas") in northern Tanzania. The study aimed to (a) assess the extent of retaliatory killings of major carnivore species due to livestock depredation, (b) describe the spatiotem-poral characteristics of carnivore raids on livestock enclosures, (c) analyze whether spatial covariates influenced livestock depredation risk in livestock enclosures, and (d) examine the cost-effectiveness of livestock enclosure fortification. Results suggest that (a) majority of boma raids by carnivores were caused by spotted hyenas (nearly 90% of all raids), but retaliatory killings mainly targeted lions, (b) carnivore raid attempts were rare at individual households (0.081 raid attempts/month in fortified enclosures and 0.102 raid attempts/month in unfortified enclosures), and (c) spotted hyena raid attempts increased in the wet season compared with the dry season, and owners of fortified bomas reported less hyena raid attempts than owners of unforti-fied bomas. Landscape and habitat variables tested, did not strongly drive the spatial patterns of spotted hyena raids in livestock bomas. Carnivore raids varied randomly both spatially (village to village) and temporally (year to year). The cost-benefit analysis suggest that investing in boma fortification yielded positive net present values after two to three years. Thus, enclosure fortification is a cost-effective strategy to promote coexistence of carnivores and humans.
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Direct road mortality and the barrier effect of roads are typically identified as one of the greatest threats to wildlife. In addition, collisions with large mammals are also a threat to human safety and represent an economic cost to society. We documented and explored the effects of animal-vehicle crashes on human safety in São Paulo State, Brazil. We estimated the costs of these crashes to society, and we summarized the legal perspectives. On average, the Military Highway Police of São Paulo reported 2,611 animal-vehicle crashes per year (3.3% of total crashes), and 18.5% of these resulted in human injuries or fatalities. The total annual cost to society was estimated at R$ 56,550,642 (US $ 25,144,794). The average cost for an animal-vehicle crash, regardless of whether human injuries and fatalities occurred, was R$ 21,656 (US $ 9,629). The Brazilian legal system overwhelmingly (91.7% of the cases) holds the road administrator liable for animal-vehicle collisions, both with wild and domestic species. On average, road administrators spent R$ 2,463,380 (US $ 1,005,051) per year compensating victims. The logical conclusion is that the Brazilian legal system expects road administrators to keep animals, both wild and domestic species, off the road. We suggest an improved coordination between the laws that relate to animal-vehicle collisions and human safety, and the process for environmental licenses that focusses on reducing collisions with wildlife and providing habitat connectivity. In addition, we suggest better management practices, raising awareness and social change with regard to abandoned domesticated animals including horses, cattle, and dogs. This should ultimately result in a road system with improved human safety, reduced unnatural mortality for both domestic and wild animal species, safe crossing opportunities for wildlife, and reduced monetary costs to society.
BACKGROUND Wildlife damage to crops is a persistent and costly problem for many farmers in the United States. Most existing estimates of crop damage have relied on direct assessment methods such as field studies conducted by trained biologists or surveys distributed to farmers. In this paper, we describe a new method of estimating wildlife damage that exploits federal crop insurance data. We focused our study on four crops: corn, soybean, wheat, and cotton, chosen because of their economic importance and their vulnerability to wildlife damage. RESULTS We determined crop‐raiding hot spots across the United States over the 2015‐2019 period and identified the Eastern and Southern regions of the United States as being the most susceptible to wildlife damage. We estimated lower bounds for dollar and percent losses attributable to wildlife to these four crops. The combined loss across four crops was estimated at $592.6 million. The highest total estimated losses to wildlife were incurred by soybeans ($323.9 million) and corn ($194.0 million) and the highest percentage losses were estimated for soybeans (0.87%) and cotton (0.72%). CONCLUSION We believe the proposed method will be a reliable way to evaluate geographic and temporal heterogeneity in damages for the coming years. Accurate information on damages benefits various management agencies by allowing them to allocate management resources to crops and regions where the problem is relatively severe. A better understanding of damage heterogeneity can also help guide research and development of new management techniques. This article is protected by copyright. All rights reserved.
The use of livestock guarding dogs (LGDs) has been widely advocated as a responsible tool for reducing livestock predation and conserving wildlife. However, their hidden ecological costs have rarely been investigated. We analysed scats (n = 183) from six LGDs and visited Global Positioning System (GPS) location clusters (n = 352) from nine GPS-collared LGDs to reconstruct their diet and assess impacts on wildlife and livestock in Namaqualand, South Africa. Wild mammals, including 10 native species, and small-livestock were the main secondary foods (i.e. besides dog food pellets). A total of 90% of scats and one third of GPS clusters investigated had associated animal remains. When accompanied by a human attendant, fewer LGD scats contained animal matter (39.9%; of which 32.3% wild mammals and 4.6% livestock), in contrast to scats of LGDs on their own (93.2%; 14.4% wild mammals, 75.4% livestock). Similarly, few clusters of accompanied LGDs included animal remains (5.7%; of which 43.8% wild mammals and 31.3% livestock), whereas unaccompanied dogs clustered frequently at carcasses (92.4%; 16% wild mammals, 74% livestock). While sample sizes were relatively small and some dogs might have scavenged, we emphasize the importance of rigorous training and intensive monitoring of LGDs to correct unwanted predation behaviour and to maximize their ecological and protective benefits.
Crop damage caused by herbivorous wildlife species on farms located within conservation landscapes, is a driver of human-wildlife conflict (HWC). Guarding of farms, whereby farmers spend the night out in the fields, in areas adjacent to protected areas is, therefore, very common in many African and Asian countries. Furthermore, guarding is often combined with other crop protection measures, but little is known about the efficacy of these measures. We examined the effect that different traditional and advanced crop protection measures (active and passive guarding strategies, barriers and combinations of measures) had on the magnitude of damaged crops. For this, we examined the cost of crop damage caused by a total of 20 wildlife species in two African and two Asian study areas, where different protection types were applied. Data was compared with the cost of crop damage on unprotected fields. We continuously used a standardised HWC assessment scheme over six years (2009–2014), based on site observations and measurements in addition to interviews with victims. The analysis of crop damage costs revealed substantial losses, especially from that caused by elephants (Loxodonta africana and Elephas maximus) and other large herbivores, such as zebra (Equus quagga) and common eland (Taurotragus oryx). Once wildlife had entered the farms, it was found that crop protection measures by farmers were only able to reduce damage costs when applied as a communal, strategic guarding system. Surprisingly, all other traditional crop protection strategies have proven ineffective in reducing crop damage costs. Electrical fences actually increased the risk of crop damage when combined with guarding and the chasing of wildlife strategies. Therefore, we recommend reviewing the practice of traditional guarding strategies and the effectiveness of fences. Furthermore, we emphasise the need for objective evaluation of HWC mitigation strategies in the long-term.