Rural Household’s Vulnerability Assessment to Climate
Variability: A Case Study of the Peang Lvea Commune, Odongk
District, Kampong Speu Province, Cambodia
Hoeurn Cheb,
Royal University of Phnom Penh, Cambodia
E-mail: cheb.hoeurn@rupp.edu.kh
Soriya Yin
Royal University of Phnom Penh, Cambodia
Nyda Chhinh
Flinders University, Australia
Abstract Cambodia is one of the nations most vulnerable to climate variability and change. If
as severe as predicted, the effects of climate change may erode any efforts to alleviate poverty
and improve the food security of Cambodian farmers. Climate change vulnerability assessment
studies have been conducted to inform the need for adaptation. However, there have been few
studies at the community level in Cambodia, where these effects are often felt the hardest. This
study aims to address this knowledge gap through a vulnerability assessment study at the
household level. A composite indicator was employed to assess vulnerability. Qualitative and
quantitative research approaches were used in the study, and information from both primary
and secondary sources was examined. The study found that 92.26 per cent of households in the
Peang Lvea commune are vulnerable to climate variability. Though the commune is not often
exposed to climate-related hazards, the study indicates that the commune is vulnerable to
1
climate variability due to a medium degree of sensitivity and low adaptive capacity to climate
variability. Livelihood diversification, repairing and building reservoirs, irrigation and water
storage, practicing climate-smart agriculture that uses less water, crop diversification and
establishing a saving group and farmer cooperative are highly recommended to decrease the
vulnerability of the commune to climate variability.
Keywords Rural households, vulnerability, climate variability, drought.
Journal of Economic Literature classification numbers: Q54
1. Introduction
Cambodia has been identified as one of the countries most vulnerable to climate
variability and change (Yusuf & Francisco, 2009) and Cambodia’s agricultural and fishery
sectors are the most vulnerable within the country (Allison et al., 2009; Asian Development
Bank, 2009). It is estimated that between 1994 and 2011 2,763,535 hectares of rice-cultivated
land was destroyed by climate-related hazards. Flood accounted for 65.59 per cent of the total
destruction of rice and drought for 32.22 per cent. It is estimated that flood destroys 100,697
hectares of rice field annually, while drought destroyed 49,469 hectares of rice-cultivated land
between 1994 and 2011 (Ministry of Agriculture Forestries and Fisheries, 1995-2012). For
example, more than 50 per cent of Cambodia’s national rice production comes from the Prey
Veng, Takeo, Kampong Cham, Battambang and Siem Reap provinces, but these rice-producing
areas experience flood and drought almost every year (Ministry of Environment, 2005). The
impacts of these hazards on agriculture are a major concern for the rural Cambodian people
whose livelihood highly depends on this climate-sensitive sector.
Extreme climate events such as flood and drought significantly affect the livelihood of
farmers. Farmers have reported that droughts and floods have negative impacts for them; for
2
instance, food insecurity is a constant problem for rural farmers (Ministry of Environment,
2005; The NGO Forum on Cambodia, 2012). Flood and drought are two climate events
recognised as main causes of poverty increases and chronic food insecurity in Cambodia
(National Committee for Disaster Management, 2008) and both of these events have been
explicitly highlighted as needing to be addressed for agricultural improvement to occur (Royal
Government of Cambodia, 2010).
Although, flood has a greater impact than drought in damaging rice production, drought
is considered a major problem because it is difficult to observe. It is classified as a chronic
hazard (a slow onset event that is barely perceptible by society) while flood is considered as
sudden onset hazard (a hazard that appears rapidly but only lasts for a short period) (Cutter,
Emrich, Webb, & Morath, 2009). Flood is a seasonal hazard and farmers who live in floodaffected areas have developed strategies to deal with floods, for example they can
autonomously adapt or adjust their rice production cycle and techniques. Many of the farmers
who have suffered heavily from floods in previous years have improved their resilience by
gaining more knowledge and experience concerning how to best prepare, respond to and
recover from floods. (The NGO Forum on Cambodia, 2012). In contrast, drought is not
seasonal and it is hard to investigate because most the agricultural fields in Cambodia are rainfed. Irrigation coverage in Cambodia is limited. Farmers have reported that the intensity and
severity of droughts is high based on their experiences (NGOs, 2012). Although drought can
occur at different times, drought affects grain yield the most severely when it occurs late in the
growing season, and longer duration genotypes are more likely to encounter drought during
grain filling. (Tsubo, Fukai, Basnayake, & Ouk, 2009). In Cambodia, drought can occur at any
time during the wet season, and when it occurs rice production may be greatly reduced (Ouk
et al., 2006). For instance, lowland rice production in the Mekong region is generally low
because crops are cultivated under rain-fed conditions and often exposed to drought. The
3
occurrence of drought is associated with a long absence of heavy rainfall events and the
development of drought is further enhanced with low clay content of the soil. The impact of
drought on grain yield mostly depends on the degree of drought severity (Tsubo et al., 2009).
To address the impact of climate variability and change, particularly climate-induced
hazards, vulnerability assessments are required (Füssel, 2007). According to USAID (2007)
the use of vulnerability assessments is common because vulnerability is a function of exposure,
sensitivity and adaptive capacity. Adapting to climate change involves reducing exposure and
sensitivity and increasing adaptive capacity (USAID, 2007). Climate change vulnerability
assessments are regarded as a principal vehicle for informing the need for adaptation because
they address the question of “What are we adapting to?” as well as aiding the selection and
evaluation of specific adaptation options (Preston & Stafford-Smith, 2009). While vulnerability
assessment is inevitably needed to address climate change impacts and identify the need for
adaptation (Füssel, 2007), there are few studies about climate change vulnerability in
Cambodia, a country which, as discussed, is highly vulnerable to climate change. Most of these
studies focus on the national and sub-national levels, with little research at the community
level, where households are severely affected.
1.1 Current vulnerability versus future vulnerability
There is clear evidence that climate change is occurring and the impacts of climate
change and variability are recognised worldwide. Researchers and scholars often use the terms
“change” with “variability”; however, climate change and climate variability are two different
terms with different meanings. The Intergovernmental Panel on Climate Change (IPCC) states:
Climate change refers to shifts in the mean state of the climate or in its variability
persisting for an existing period (decade or longer). Climate change may be due to
natural change or to persistent anthropogenic change of composition of atmosphere or
in land use (Intergovernmental Panel on Climate Change, 2001).
4
However, climate variability only refers to variations in the mean state of climate on all
temporal and spatial scales beyond that of individual weather events. Examples of climate
variability include extended droughts, floods and conditions that result from periodic El Niño
and La Niña events. Climate variability can cause abrupt disruptions, such as floods, droughts
or tropical storms (USAID, 2007).
The term “vulnerability” has been typologised and there is a difference between social
vulnerability and physical vulnerability (Adger, 2006), contextual vulnerability and output
vulnerability (Brooks, 2003),
current vulnerability and future vulnerability (Preston &
Stafford-Smith, 2009). According to Preston et al. (2009), a common question that arises when
making decisions about what future to anticipate is that of whether we adapt to climate change
or climate variability. The distinction between these two terms is that variability is a
fundamental component of climates past, present and future. The assessment of climate change
and its impacts has traditionally conceptualised climate change as changes in the mean state of
the climate system (for example, average annual means) (Intergovernmental Panel on Climate
Change, 2001), which is future vulnerability. There is a failure to integrate mean changes in
the climate with the natural climate variability that can be caused by environmentally relevant
consequences. The extensive existing experience in how to adapt better to climate change is
also dependent on an understanding of current climate variability, which helps in predicting
the future. Therefore a climate vulnerability assessment that takes into account climate
variability is needed.
1.2 Frameworks and indicators for assessing households’ vulnerability
The concept of vulnerability has been a powerful analytical tool for describing the state
of susceptibility to harm, powerlessness and marginality of both physical and social systems,
and for guiding normative analysis of actions to enhance well-being through the reduction of
5
risk (Adger, 2006). However, there are other definitions of vulnerability that have been used
and discussed (Adger, 2006; Alwang, Siegel, & Jørgensen, 2001; Brooks, 2003; Füssel, 2007;
Füssel & Klein, 2006). While the term “vulnerability” is now a central concept in a variety of
research contexts such as ecology, public health, poverty and development, secure livelihoods
and famine, sustainability science, land change, and climate impacts and adaptation (Adger,
2006; Füssel, 2007), this term has no universally accepted definition, largely because different
disciplines use the term differently to explain their areas of concern (Deressa, Hassan, &
Ringler, 2009; Füssel, 2007). However, the scientific use of vulnerability has its roots in
geography and natural hazards research (Füssel, 2007). According to Preston and StaffordSmith (2009), the IPCC’s definition of vulnerability is an integrated view of two concepts in
natural hazards: the Risk-Hazard Model the Press-and-Release Model. The IPCC’s definition
is:
Vulnerability is the degree to which a system is susceptible to, or unable to cope with,
adverse effects of climate change, including climate variability and extremes.
Vulnerability is function of the character, magnitude, and rate of climate change and
variation to which a system is exposed, its sensitivity, and its adaptive capacity
(McCarthy et al., 2001).
Vulnerability, therefore, is a function of the sensitivity of a system to changes in climate
(the degree to which a system will respond to a given change in climate, including beneficial
and harmful effects), adaptive capacity (the degree to which adjustments in practices,
processes, or structures can moderate or offset the potential for damage or take advantage of
opportunities created by a given change in climate) and the degree of exposure of the system
to climatic hazards (McCarthy et al., 2001). There are common terms across the theoretical
approaches utilised in the climate change community and vulnerability is most often
conceptualised as being constituted by components that include exposure and sensitivity to
6
perturbations or external stresses and the capacity to adapt (Adger, 2006). The framework
outlined by McCarthy et al. above has been employed in many recent studies such as (Asian
Development Bank, 2009; Gbetibouo & Ringler, 2009; Hahn, Riederer, & Foster, 2009; Yusuf
& Francisco, 2009).
Exposure is defined as the nature and degree to which a household experiences
environmental or socio-political stress. The characteristics of these stresses include the
magnitude, frequency, duration and real extent of the hazard (Brton, Kates, & White, 1993).
The effect of exposure to environmental hazards, especially drought, has been extensively
studied in many countries where the economy is highly dependent on agriculture (Luers,
Lobell, Sklar, Addams, & Matson, 2003; Zarafshani et al., 2012). This study applies the
experience of drought at the village level as a proxy of exposure to drought, as per a number
of studies that have employed hazard frequency as proxy for exposure (Deressa et al., 2009;
Hahn et al., 2009). It is assumed that the households in the study are in the same village and
exposed to drought in the same year. Since there are only drought hazards in the study site,
water shortage is another selective indicator of the exposure, as per Hahn et al. (2009). There
are two indicators used in this research to capture drought exposure: the frequency of drought
between 2004 and 2012 (the number of years in this period that drought was experienced at the
village level) and experiences of water shortage in the 12 months of 2012 (the number of
months water shortage was experienced at the household level) (Table 1).
Sensitivity refers to how households could be negatively affected by environmental
stresses (Adger, 2006). Sensitivity to climate change and variability can be operationalised by
a number of indicators, which have already been used in studies by Aulong and Kast (2011),
Below et al (2012), Gbetibouo and Ringler (2009) and Hahn et al (2009). However, only some
of these indicators have been used in this study due to data availability and their relevance to
this specific site, especially to drought hazard. There are three indicators measuring the
7
sensitivity of rural households to drought hazard used in this study: dependency ratio,
percentage of active agricultural labourers of each household, and percentage of annual income
generated from agriculture (Table 1).
Adaptive capacity is a household’s ability to withstand or recover from climate events
(Adger, 2006) and encompasses a number of components. Households which are highly
adaptive to hazards can be considered to be adaptive in terms of six components (Smit &
Pillifosova, 2001): economic stability, technology, information and skills, infrastructure,
institutions and networking, and equity. These six dimensions are similar to sustainable
livelihood approach, which tests adaptability by evaluating social, human, financial, physical,
and natural capital. The sustainable livelihood approach has been used to assess vulnerability
in research by Aulong and Kast (2011), Below et al (2012), Gbetibouo and Ringler (2009), and
Hahn et al (2009). Hence, adaptive capacity in responding to drought hazard is measured by
measuring the five capitals of livelihood framework: natural capital, physical capital
(infrastructure), social capital (institution and networking), human capital (information and
skill), and financial capital (economic stability) (Table 1).

Social capital refers to the relationship of households with other villagers. Households
that have a good relationship with others villagers have a high adaptive capacity in
terms of social capital because they will receive help from those villagers and
neighbours. Social capital is represented by average number of occasions agricultural
assistance was given and/or received and that money was borrowed and/or lent (Hahn
et al., 2009).

Natural capital refers to the resources that a particular household owns. The higher
natural capital is, the higher the adaptive capacity of the household, because they have
their own resources to respond to a climate event. Natural capital is represented by
agricultural land size and average crop diversity.
8

Financial capital is represented by income per capita and percentage of income
generated from non-agricultural sectors. The higher the financial capital, the higher the
adaptive capacity of the household is in responding to climate-related hazards.

Human capital is represented by the percentage of working labourers within a
household and the education level of the head of the household. The higher human
capital is, the higher the adaptive capacity of a household.

Physical capital is represented by the percentage of land held by a household that is
irrigated and the number of accessible water sources. The higher of physical capital is,
the higher the adaptive capacity.
2. Methodology
Both qualitative and quantitative research methods are employed in this study.
Qualitative research employs both desk review and key informant interviews. The desktop
research involves an extensive and comprehensive literature review in order to capture the
indicators for households’ vulnerability assessment. Then, six key informants were selected
and in-depth interviews conducted in order to consolidate the indicators. These informants were
selected after being introduced by a commune chief or representative of an area. The in-depth
interviews were conducted with two academics in Phnom Penh, one person from the Provincial
Department of Agriculture in Kampong Speu, one person from the Provincial Department of
Water Resource in Kampong Speu, one person from the Provincial Department of Environment
in Kampong Speu, and a commune chef from Peang Lvea in the Odongk District, Kampong
Speu Province.
Consolidated indicators were gathered by using households’ survey. Face-to-face
interviews were conducted to get primary data from 190 households and six village heads.
Structured questionnaires were employed to facilitate the interviews. Lastly, random sampling
9
was employed to conduct the interview. All of the interviewees were farmers: 52 male and 138
female. The six village heads were also interviewed but using different questions, primarily to
gather the drought frequency information.
2.1 Data collection
The number of samples for the face-to-face interviews to be conducted is determined
by probability sampling. Sampling involves using two procedures. Firstly, the number of
samples to obtain from the commune is determined by (Yamane, 1967)’s equation, which is
illustrated in Equation 1:
n
N
1  Ne 2
Equation 1
Where n and N is number of sample and population in the commune, respectively, e is
standard error (7%). According to this formula, out of the 1,371 households in the commune,
a sample of 178 should be interviewed. However, to ensure an appropriate quality of data and
information be obtained from the questionnaire, 190 questionnaires were conducted.
To ensure data reliability and validity, cluster sampling was employed in the commune.
There are three zones: high impact (Zone I), medium impact (Zone II) and low impact (Zone
III). There are two villages in each zone, and the sample of zones is selected using Equation 2.
nx 
n
hx
N
Equation 2
Where nx is sample in each zone, n is the total sample in the commune, N is the total
population in the commune and hx is the population in each zone. Using the results from this
equation, 60 respondents were selected from both zone I and zone II and 70 respondents form
zone III (totaling 190). Lastly, random sampling was employed to select households with
which to conduct the interview. The questionnaires used in the face-to-face interviews were
10
structured. Additionally, six village heads from the commune were also interviewed using
semi-structured interviews, primarily to gather the drought frequency information.
2.2 Selection of the study area
Kampong Speu is one of provinces most vulnerable to climate change in Cambodia
(Chhinh & Cheb, 2013; Ministry of Environment, 2006; Yusuf & Francisco, 2009), and it is
also one of the poorest in the country. The Peang Lvea commune has been selected for this
study because it has been mapped as the commune most impacted by and vulnerable to natural
hazards in Cambodia (Figure 1) (Chann & Kong, Fourthcoming; National Committee for SubNational Democratic Development, 2003; Provinical Committee for Disaster Management of
Kampong Speu, 2010). Located in the northern part of Kompong Speu, the Peang Lvea
commune is administratively located in the Odongk district. There are 18 villages in the Peang
Lvea commune. Households in the commune are primarily dependent on agriculture and its
population has high agricultural dependency. Out of the total 7,859 households in the
commune, 99.2 per cent of the labour force works in agriculture (National Committee for SubNational Democratic Development, 2011).
2.3 Analytical approach
The study employs an indicator approach for assessing vulnerability to climate
variability. This approach could be applied at the local, national, regional, and global scales
(Deressa et al., 2009). An indicator approach is only suitable for finer scales where the system
is not complicated and there are four types of methods to select indicators for vulnerability
assessment to climate change and variability: deductive, inductive, normative and nonsubstantial (Hinkel, 2011). This study only employs the normative argument of indicator
approach to assess vulnerability to climate variability, where the weight of the indicators are
11
assumed to be equal. There are four steps in conducting vulnerability indexing. The first step
is normalisation, where all indicators are normalized to obtain an index from 0 to 1 (Equation
3). 0 is lowest value and 1 is the highest.
Step1: Normalization of indicator
Ii 
( Ih  Im in)
(Im ax  Im in)
Equation 3
Where Ii is index of indicator, Ih is the actual value of each indicator of each household,
and Imin and Imax is minimum and maximum value of indicator. After the normalisation of
indicators, the index of capital was generated. For example, in the case of adaptive capacity
there are five capitals, and each capital index was generated by using Equation 4.
Step 2: Generating index of capital
n
Ci 
 Ii
i 1
Equation 4
n
Where Ci is index of capital, and Ii is index of the each indicator in each household, and
n is the number of indicators. After generating the index of capital, step three will generate the
index of vulnerability attribution (exposure, sensitivity and adaptive capacity). For example,
again in the case of adaptive capacity, there are five capitals, and they were calculated by using
Equation 5.
Step 3: Generating index of Vulnerability Attribute (Exposure Index (EI), Sensitivity
Index (SI), and Adaptive Capacity Index (ACI)).
n
VA 
 Wci * Ci
i 1
Equation 5
n
 Wci
i 1
Where Ci is index of capital, and Wci is weight of the capital and n is the number of
indicators in each vulnerability attribute. Please note that all vulnerability attributes do not
necessarily follow all three steps because it depends whether each vulnerability attribute consist
12
of dimensions or not. After generating the vulnerability attribute, the last step is to generate
vulnerability index, which is shown in Equation 6.
Step 4: Generating Vulnerability Index (VI)
VI 
( EI  SI )  (1  ACI )
3
Equation 6
Where VI is vulnerability index and EI, SI and ACI are the indexes of exposure, sensitivity
and adaptive capacity respectively. The index ranges from 0 (lowest) to 1 (highest).
3. Results and discussion
Local vulnerability assessment is measured by examining exposure, sensitivity and
adaptive capacity. This study finds that households living in the Peang Lvea commune are
vulnerable to climate variability, particularly drought, due to low exposure (index of 0.15),
medium sensitivity (0.51), and low adaptive capacity to climate variability (0.25 or inverse
adaptive capacity at 0.75) (Table 3). 95.26 per cent of households are vulnerable to climate
variability, with an average index of vulnerability of 0.62.
3.1 Exposure assessment
Exposure is calculated by establishing the drought frequency and the number of months
of water shortage in each household over one twelve-month period. The level of the exposure
varies depending on these individual variables. However, for the variable of frequency of
drought it is assumed that households in the same village have same frequency of drought. For
instance, Village A experienced drought in four years between 2004 to 2012, so all households
in Village A are assumed to have encountered drought for four years in the same time period.
On average, households in the Peang Lvea commune experienced drought for 1.8 years in the
nine years from 2004 to 2012. The second operational variable of exposure is water shortage.
While drought frequency captures exposure at the village level, water shortage can inform of
13
different exposures at the household level. Overall, all households experienced water shortage
in 2012, with some households reporting encountering water shortage for four months during
the year. On average, households in the Peang Lvea commune experienced water shortage for
1.21 months in 2012. There, households are slightly exposed to climate variability, as its index
is relatively low at 0.15. The exposure is significantly different among the zones with Zone I
having the highest value, while Zone III has the lowest (Table 3).
3.2 Sensitivity assessment
In this study, the local sensitivity to climate variability is measured by the function of
dependency ratio, percentage of agricultural laborers, and percentage of income generating
from agriculture. It should be recognised that with the advantage of labour force availability in
rural areas, the dependency ratio indicator is relatively low. The result of the survey found that
the average household size in the commune is 5.6 people, with each household containing on
average 1.9 dependent members, resulting in a dependent ratio of 0.34. Another indicator of
sensitivity is percentage of agricultural labourers. While the average total number of family
members is 5.6, the average number of family member who are labourer is almost 4 per family.
Three of these four family member are active labourers in the agricultural sector, especially
rice farming. Therefore the percentage of agricultural labourers is relatively high at 71.74 per
cent. The households in the studied site are highly sensitive to climate variability due to the
labour distribution in this area, since labour is primarily in the agricultural sector. In term of
percentage of income generated from agriculture, households depend on agriculture for 47.63
per cent of their annual income. As a result, the agricultural labourer is the main variable
contributing to sensitivity, followed by share of annual income generated from agriculture and
dependency ratio. This research finds that 53.75 per cent of households are sensitive to climate
14
variability and the average index of sensitivity is 0.51. Sensitivity is not statistically significant
among the zones (Table 3).
3.3 Adaptive capacity assessment
Adaptive capacity is represented by the five capitals of livelihood: natural, human,
physical, financial and social. The level of adaptive capacity varies depending on these capitals.
The result of the adaptive indices assessment reveals that households living in this commune
have advantages in certain attributes to adaptive capacity such as human capital, but weakness
in natural, financial, physical and social capitals. The average index of adaptive capacity is
0.25. The individual values for each of the capitals were 0.08 of social capital, 0.12 of natural
capital, 0.14 of physical capital, 0.40 of financial capital and 0.52 of human capital. Adaptive
capacity is not significant among the zones (Table 3).
Natural capital is the first dimension of adaptive capacity. Natural capital is determined
by amount of agricultural land and crop diversity. The average agricultural land per household
is 0.85 hectares with high standard deviation. The average crop diversity (rice, cucumber,
watermelon, corn and beans) is relative low. The majority of households (86.3 per cent) only
grow rice and, on average, households grow 1.23 crops. Therefore, the natural capital is low
with an index of 0.12.
Human capital is another important resource for local adaptive capacity to climate
variability and drought disaster. It is measured by the percentage of labourers per household
and the household head’s highest level of education. There are many working in labour in the
households in the Peang Lvea commune. Out of the average of 5.6 total family members, there
are approximately 4 family members working, with 70.58 per cent of family members working
in each household. The education of households’ head is relatively low at grad 4.2. This means
15
that they only finished study at primary school. Overall, households have a medium human
capital with an index of 0.54.
Physical capital is the third dimension of the adaptive capacity of each household and
is measured through the percentage of irrigated agricultural land and number of accessible
water sources for domestic consumption. Only 1.55 per cent of the agricultural land of each
household had any access to water. There is also only limited water available for domestic
consumption. Normally, households have access to rainwater and water from ponds and/or
wells in addition to other sources, such as buying water from nearby households. Each
household had access to 1.81 sources of water on average. It is concluded that households in
this commune have a low adaptive capacity in term of physical capital with an average index
of 0.14.
The fourth capital of adaptive capacity to be measured is the financial aspect. There are
two variables to measure financial capacities: per capita income and percentage of income
generated from non-agricultural sectors. The per capita income of rural households in this
commune is approximately USD 385 per annum (32 dollars per month or 1.07 dollars per day),
with 51.22 per cent of annual household income generated from non-agricultural sources. The
overall index of financial capital of households is 0.40.
Finally, social capital plays an important role in the local prevention and mitigation of
the impact of drought hazards in the context of rural communities. This study investigates
social capital with two indicators: the number of times assistance is given and/or received and
the number of times money is borrowed and/or lent. The highest number of assistances reported
in any household in the Peang Lvea commune was 4 assistances, while some household did
not even have one assistance. In average, the household only has 0.22 out of 8 assistances.
Borrowing and lending money without interest is another variable of social capital. Ranging
from 0 to 2, households in Peang Lvea had only 0.27 on average. Social capital as a whole is
16
relatively low in the study site with an index of only 0.08. As a result, only one household has
an index higher than 0.5. It is therefore concluded that adaptive capacity of households living
in Peang Lvea commune is relatively low.
4. Conclusion
In general, Cambodia is highly vulnerable to climate change and variability (Allison et
al., 2009; Asian Development Bank, 2009; Ministry of Environment, 2001; Yusuf & Francisco,
2009). Rural households in Cambodia are also vulnerable and strongly impacted by the climate
change and variability which occur as a result of climate hazards (Ministry of Environment,
2005; The NGO Forum on Cambodia, 2012). Now it can be also seen on the smaller scale of
the Peang Lvea commune in the Odongk district that the rural households are vulnerable to
climate variability due to their sensitivity to climate variability and low adaptive capacity to
the climate hazard of drought.
Though the commune is not often exposed to climate-related hazards, the study finds
that the commune is vulnerable to climate variability due to its high sensitivity and low adaptive
capacity. Households have a low exposure degree because they have never experienced any
hazards other than drought and drought is hard to measure or notice because it is a chronic
hazard. The community is sensitive to climate variability because most of the household
labourers work in the agricultural sector, particularly in rice production, and the source of
income generation for these households is also dependent on the climate-sensitive sector of
agriculture. Additionally, the community has a relatively low ability to adapt to environmental
changes. Rural households have insufficient water for agricultural production and domestic
consumption, a one-crop planting habit, low income and lack a network with neighbours and
other villagers in the commune. Out of the five capitals through which households can respond
17
to drought hazards, only human capital has a medium value while the physical, natural,
financial and social capitals are low.
While the exposure reported here cannot be addressed in the short term, the way to
reduce the vulnerability is to decrease sensitivity and increase adaptive capacity. Improving
local livelihoods through livelihood diversification - creating other job opportunities for
commune occupants - would decrease sensitivity to climate variability. To increase adaptive
capacity, adaptive strategies such as repairing and building reservoirs, irrigation and water
storage, implementing climate-smart agricultural practices using less water, increasing crop
diversification and establishing a saving group and farmer cooperative are highly
recommended for this commune.
Acknowledgements
This work was carried out with the aid of a grant and technical supports from IDRC-EEPSEA,
Mekong Institute-New Zealand Ambassador Scholarship (MINZAS) and UNESCO Chair of
the University of Bergamo, Italy.
References
Adger, W. N. (2006). Vulnerability. Global Environmental Change, 16(3), 268-281. doi:
10.1016/j.gloenvcha.2006.02.006
Allison, E. H., Perry, A. L., Badjeck, M.-C., Neil Adger, W., Brown, K., Conway, D., . . .
Dulvy, N. K. (2009). Vulnerability of national economies to the impacts of climate
change on fisheries. Fish and Fisheries, 10, 173–196. doi: 10.1111/j.14672979.2008.00310.x
Alwang, J., Siegel, B. P., & Jørgensen, L. S. (2001). Vulnerability: A View From Different
Disciplines: The world bank.
18
Asian Development Bank. (2009). Building Climate Resilience in the Agriculture Sector in
Asia and the Pacific. Mandaluyong City, Philippines.
Brooks, N. (2003). Vulnerability, risk and adaptation: A conceptual framework: Tyndall
Centre for Climate Change Research and Centre for Social and Economic Research
on the Global Environment (CSERGE)
Brton, I., Kates, R. W., & White, G. F. (1993). The Environment as Hazard.
Chann, S., & Kong, S. (Fourthcoming). Climate Chagne Vulnerability Assessment:
Commune level in Kampong Speu. Phnom Penh, Cambodia: RUPP Press.
Chhinh, N., & Cheb, H. (2013). Climate Change Vulnerability Assesment: Household Level
in Kampong Speu. International Journal of Environmental and Rural Development,
4(1), 31-38.
Cutter, S. L., Emrich, C. T., Webb, J. J., & Morath, D. (2009). Social Vulnerability to
Climate Variability Hazards: A Review of the Literature.
Deressa, T. T., Hassan, M. R., & Ringler, C. (2009). Assessing Household Vulnerability To
Climate Change: The Case Of Farmers In The Nile Basin Of Ethiopia International
Food Policy Research Institute (IFPRI).
Füssel, H.-M. (2007). Vulnerability: A generally applicable conceptual framework for
climate change research. Global Environmental Change, 17(2), 155-167. doi:
10.1016/j.gloenvcha.2006.05.002
Füssel, H.-M., & Klein, R. (2006). Climate Change Vulnerability Assessments: An Evolution
of Conceptual Thinking. Climatic Change, 75(3), 301-329. doi: 10.1007/s10584-0060329-3
Gbetibouo, G. A., & Ringler, C. (2009). Mapping South African Farming Sector
Vulnerability to Climate Change and Variability: A Subnational Assessment Paper
19
presented at the Human Dimensions of Global Environmental Change ‘Earth System
Governance: People, Places and the Planet’.
Hahn, M. B., Riederer, A. M., & Foster, S. O. (2009). The Livelihood Vulnerability Index: A
pragmatic approach to assessing risks from climate variability and change--A case
study in Mozambique. Global Environmental Change, 19(1), 74-88. doi: DOI:
10.1016/j.gloenvcha.2008.11.002
Hinkel, J. (2011). “Indicators of vulnerability and adaptive capacity”: Towards a clarification
of the science–policy interface. Global Environmental Change, 21(1), 198-208. doi:
10.1016/j.gloenvcha.2010.08.002
Intergovernmental Panel on Climate Change. (2001). Climate Change 2001: Impact,
Adaptation, and Vulnerability.Contribution of working group II to the third
assessment report of the intergovernmental panel on climate change. . Cambridge,
United Kingdom.
Luers, A., Lobell, D., Sklar, L., Addams, C., & Matson, P. (2003). A method for quantifying
vulnerability, applied to the agricultural system of the Yaqui Valley, Mexico. Global
Environmental Change, 13(4), 255-267.
McCarthy, J., Canziani, O., Leary, N., Dokken, D., & White, K. (2001). Climate Change
2001: Impacts, Adaptation and Vulnerability. Cambridge: Cambridge University
Press.
Ministry of Agriculture Forestries and Fisheries. (1995-2012). Agricultural Statistics 19942011. Phnom Penh: MAFF.
Ministry of Environment. (2001). Vulnerability and Adaptation Assessment to Climate
Change in Cambodia. Phnom Penh, Cambodia: MoE.
20
Ministry of Environment. (2005). Vulnerability and Adaptation to Climate Hazard and to
Climate Change: A Survey of Rural Cambodian Household. Phnom Penh: Ministry of
Environment.
Ministry of Environment. (2006). National Adaptation Program of Action. Phnom Penh,
Cambodia: Ministry of Environment.
National Committee for Disaster Management. (2008). Strategic National Action Plan for
Disaster Management Phnom Penh , Cambodia.
National Committee for Sub-National Democratic Development. (2003). Mapping
Vulnerability to Natural Disaster in Cambodia: NCDM, Phnom Penh, Cambodia.
Ouk, M., Basnayake, J., Tsubo, M., Fukai, S., Fischer, K. S., Cooper, M., & Nesbitt, H.
(2006). Use of drought response index for identification of drought tolerant genotypes
in rainfed lowland rice. Field Crops Research, 99(1), 48-58. doi: DOI:
10.1016/j.fcr.2006.03.003
Preston, B., & Stafford-Smith, M. (2009). Framing vulnerability and adaptive capacity
assessment: Discussion paper. Canberra: CSIRO.
Provinical Committee for Disaster Management of Kampong Speu. (2010). Disaster Hotspots
Map: PCDM of Kampong Speu.
Royal Government of Cambodia. (2010). Policy Paper on The Promotion of Paddy
Production and Rice Export.
Smit, B., & Pillifosova, O. (Eds.). (2001). Adaptation to Climate Change in the Context of
Sustainable Development and Equity (Vol. 7). Cambridge, UK: Cambridge University
Press.
The NGO Forum on Cambodia. (2012). Impact of Climate Change on Rice Production in
Cambodia. Phnom Penh, Cambodia: The NGO Forum on Cambodia.
21
Tsubo, M., Fukai, S., Basnayake, J., & Ouk, M. (2009). Frequency of occurrence of various
drought types and its impact on performance of photoperiod-sensitive and insensitive
rice genotypes in rainfed lowland conditions in Cambodia. Field Crops Research,
113(3), 287-296. doi: DOI: 10.1016/j.fcr.2009.06.006
USAID. (2007). Adapting to Climate Variability and Change: A Guide Manual for
Development Planning.
Yamane, T. (1967). Statistic: An Introductory Analysis (2nd ed.). New York, USA.
Yusuf, A. A., & Francisco, H. (2009). Climate Change Vulnerability Mapping for Southeast
Asia: Economy and Environment Program for Southeast Asia (EEPSEA)
Zarafshani, K., Sharafi, L., Azadi, H., Hosseininia, G., De Maeyer, P., & Witlox, F. (2012).
Drought vulnerability assessment: The case of wheat farmers in Western Iran. Global
and Planetary Change, 98–99(0), 122-130. doi:
http://dx.doi.org/10.1016/j.gloplacha.2012.08.012
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Table 1. Indicators for assessing households’ vulnerability
Variables
Explanation
Exposure
Frequency of drought
Number of drought years between 2004 and 2012 in a
particular village. It is assumed that households in the
same village have same frequency of drought.
Frequency of water shortage
Number of months household struggled to obtain water
for their domestic consumption in the 12 months of
2012.
Sensitivity
Dependency ratio
(Age below 15+ age over 65)/ total family members
Proportion of active family
Active agricultural labourers/total family labourers
labourers working agriculture
Percentage of annual income
Income generated from rice production/total annual
generated from agriculture
income
Adaptive Capacity
Agricultural land size
Total size of agricultural land in particular households
The number of crops grown by a household, for
Natural
Average Crop
example rice, cucumber, watermelon, corn and beans).
Diversity
For each of these crops grown by a household, they get
capital
one score.
Income per capita
Total annual income/total family members
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Percentage of income
Financial
Total income generated from non-agricultural
generated from non-
capital
sector/total annual income
agricultural sectors
Percentage of working
Total family labourer/total family members
Human
labourers
capital
Level of education of
Education grade which household’s head has finished
household’s head
Receiving number (rice mill, rice seed, pull rice
Average receiving and
seedling, sowing rice seedling, and harvesting) + Giving
giving agricultural
number (rice mill, rice seed, pull rice seedling, sowing
assistance
rice seedling, and harvesting). Each time household
Social
capital
gave or received assistance, they received one score.
Average borrowing
If a household either borrowed or lent money without
and lending money
interest, they will get one score.
Percentage of irrigated
Total agricultural land size/total agricultural land
Physical
agricultural land
capital
Number of water
Total accessible water source for domestic consumption
sources
Table 2. Value of vulnerability’ indicators
Variables
Mean
Zone I
Zone II
Zone III
Frequency of drought (year)
1.79
2.50
2.00
1.00
Water shortage (month)
1.21
1.03
1.62
1.01
Exposure
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Sensitivity
Dependency ratio (ratio)
0.34
0.33
0.33
0.46
Percentage of agricultural labourers (percentage)
71.74
75.77
68.64
46.25
47.63
46.10
46.25
50.12
Agricultural land size (hectare)
1.02
1.11
0.91
1.06
Average crop diversity (number)
1.23
1.27
1.18
1.23
384.92
448.87
381.66
332.91
52.84
53.91
55.21
49.88
Percentage of working labourers (percentage)
70.58
71.99
72.05
68.11
Level of education of household’s head (year)
4.25
4.12
3.52
4.99
0.22
0.13
0.33
0.20
0.27
0.17
0.30
0.34
1.55
1.75
2.03
0.95
1.81
1.87
1.87
1.81
Percentage of annual income generated from
agriculture (percentage)
Adaptive capacity
Income per capita (USD)
Percentage of income generated from nonagricultural sectors (percentage)
Average receiving and giving agricultural
assistance (number)
Average borrowing and lending money (number)
Percentage of Irrigated agricultural land
(percentage)
Accessible water sources (number)
Table 3. Index of vulnerability and its attribute with ANOVA test
Vulnerability attribute
Zone I
Zone II
Zone III
Average
ANOVA test
Exposure
0.18
0.18
0.10
0.15
0.00
Sensitivity
0.52
0.49
0.52
0.51
0.52
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Inverse adaptive capacity
0.74
0.75
0.75
0.75
0.65
Vulnerability
0.62
0.62
0.62
0.62
0.92
One-way ANOVA test at Alpha: 0.05
Figure 1. Map of the study site in Peang Lvea Commune, Odongk District, Kampong Speu
Province, Cambodia
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