IJERD – International Journal of Environmental and Rural Development (2012) 3-2

Special Contribution on EFA and ESD Rsearch article
erd

Climate Change Impacts on Agriculture and Vulnerability
as Expected Poverty of Kampong Speu Province, Cambodia
NYDA CHHINH*Royal University of Phnom Penh, Phnom Penh, Cambodia
Email: chhinh.nyda@rupp.edu.kh

BUNNAK POCH
Western University, Phnom Penh, Cambodia
Received 30 August 2012

Accepted 22 September 2012(*: Corresponding Author)

Abstract As a developing country that is heavily dependent on agriculture, Cambodia has
been severely affected by natural hazards. Since the 1990s, there has been an increase in
the frequency and severity of flooding and drought induced or exacerbated by climatechange. Literature has demonstrated that Cambodia is very vulnerable to the adverse impacts of climate change. Vulnerability, as posited by the Intergovernmental Panel on Climate Change (IPCC), is a function of the character, magnitude and rate of climate change
and variation to which a system is exposed, its sensitivity and its adaptive capacity. This
paper measures the vulnerability as expected poverty (VEP), introduced by Shubham
Chaudhuri, in a province in Cambodia, Kampong Speu (KPS), using household survey data. The results show that among all of the exposures to natural hazards in Cambodia,
drought poses the highest risk for rain-fed agriculture dependent households. The VEP
shows that more than 60% of households in the KPS province are vulnerable to climatechange with an income threshold of US $1 per day. Some policy recommendations resulting from this study include building irrigation systems to mitigate the impacts of droughts,
ensuring secondary income generation opportunities for poor households so that households do not depend exclusively on agriculture, and increasing the education level of villagers.
Keywords Cambodia, climate change, vulnerability, adaptation, education

INTRODUCTION
The  main  sectors  that  drive  Cambodia’s  economic  growth  are  Agriculture  (including  Fisheries andForestry), Industry and Service. Each  sector’s  share  of  GDP  is  28%,  28.6%  and 38.3%, respectively(NIS, 2008). The Strategic National Development Plan (SNDP) places the agricultural sector as thecentral focus of Cambodian development for the next decade, and it is believed that agriculture sector will be a major contributing factor to inclusive growth (CDRI, 2012). The agricultural sector
currently employs 59%  of  Cambodia’s  total  labor  force  (NIS, 2008).
The industry that generates the most money in Cambodia is Textile, Wearing Apparel andFootwear (CDRI, 2007). However, the garments produced in Cambodia are created from raw materials supplied from outside of Cambodia, including fabrics. The Royal Government of Cambodia(RGC) believes that once the agricultural sector is developed, Cambodia can produce the raw materials (the agro-industrials) to supply to industry, particularly cotton and rubber. Therefore, thegrowth of agriculture will contribute to the growth of the industrial sector.
Paddy rice is the central livelihood of rural people. More than 50% of Cambodian workers are
employed in the agriculture sector, the majority of whom work in the cultivation and harvesting of
paddy  rice.  Cambodia’s  2008  general  census  (NIS, 2008) showed that more than 80% of Cambodians live in rural area and are engaged in subsistence farming. Approximately 47% of them own lessthan one hectare of land and have an average of five household members; more than one-fourth ofⒸISERD28

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all households in Cambodia are headed by females (NIS, 2009). The majority of paddy fields arerain fed; therefore, the planting index of rice in Cambodia is around 1.2 per year (MoE, 2005).
Cambodia’s rice yield is low at around two tons per hectare compared to approximately three tons
per hectare in Vietnam and Thailand (Yu and Fan, 2011).
Agricultural activities in Cambodia are not sophisticated and require only low levels of skills
and training. Farmers have an average of seven years of education (NIS, 2009). With this level of
education, it is hard for agricultural experts to share knowledge and skills about how to use hightech equipment to improve agricultural practices. Since many farmers are still using traditional
methods and tools to cultivate their land, Cambodian farmers are highly sensitive to environmental
changes, especially drought.
Frequently, Cambodia experiences floods, drought, windstorms, insect outbreaks, underground water salinisation and seawater intrusion (MoE, 2005). Since the country is heavily dependent on the agriculture sector, these natural hazards have severe effects on the livelihoods of the
80% of Cambodians who live in rural areas (MoE, 2005, NCDM, 2008, Geres-Cambodia, 2009,
MoE and UNDP Cambodia, 2011). On a national scale, flood and drought caused crises in threeconsecutive years from 2000 to 2002. Flooding in 2000 was considered the most severe in four
decades and was followed by flooding of a similar intensity in 2001, while in 2002 both flood anddrought hit the country (Chan and Sarthi, 2002).
Due to the negative effects of natural calamities caused by climate change on the agriculturalindustry, achievement of the Millennium Development Goals in Cambodia may be hindered, especially the elimination of poverty and hunger. The United Nations World Food Program (WFP,
2003) identified provinces along the Mekong River as areas prone to flooding and flash flooding,and found that more than 80% of famers are living in drought-prone areas in Cambodia. This is ofgreat concern as the irrigation scheme is very limited in Cambodia. Assessments from government
institutions show that flooding did similar damage to drought (Fig. 1) (MoE, 2006). The Economy
and  Environment  Program  for  Southeast  Asia’s  (EEPSEA)  Framework  to  Assess  Climate  Change  Vulnerability demonstrated that Cambodia is highly vulnerable to climate change; not because of
exposure but because of its low adaptive capacity (Yusuf and Francisco, 2010).

Fig. 1 Flood and drought damages on rice fields in Cambodia 1982-2007
Source: Am, 2010

Shocks, including natural disasters, are regarded as the driving force that pushes vulnerable
people into extreme poverty, especially those who have few assets or savings (World Bank, 2006).
A case study by the Analyzing Development Issues (ADI) Team found that the severe floods anddrought in Cambodia that occurred in 2000/01 and 2004/05 contributed to crop failure and thatpeople exploited common pool resources (Ang et al., 2007). Droughts have impact when they occur during the three stages of rice production activities: planting (June-July), growing (AugustSeptember) and harvesting (October-November) period. At a national level, the National Adaptation Programme of Action (NAPA) has been developed to mitigate the impact of climate change,
which includes a proposal for an irrigation scheme and a flood prevention dike (MoE, 2006). NAPA states that the Agriculture and Water resource sectors are the highest priority in both short- andlong-term strategies.
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This paper investigates the impacts of climate change on farmers in the Kampong Speu (KPS)
province, specifically the dynamic impacts on households. It argues that the natural hazards facedby households in this province are the major factors in preventing people from escaping poverty
and that integrated policy intervention is necessary to address climate change impacts.
METHODOLOGY
Vulnerability framework
Vulnerability is defined differently in different disciplines. In this study, we adopt the Intergovernmental  Panel  on  Climate  Change’s  (IPCC)  definition:  “The  degree  to  which  a  system  is susceptible to, and unable to cope with, adverse effects of climate change, including climate variability
and extremes. Vulnerability is a 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 etal., 2001, p. 995).
Exposure to environmental hazards, especially flood and drought, has been well studied inmany countries where agriculture is dominant. Methods employed to understand the nature of theseclimate-induced  disasters  vary  from  author  to  author.  Liverman’s  (1990)  study  on  vulnerability  of  farmers to drought in Mexico, for example, suggests that using diverse quantitative data makes it
possible to identify the places and people who are prone to drought. National Committee for Disaster Management (NCDM) (2003) identified where drought and flooding occurred through examining the affected areas, rice dependency and food security based on rice production in Cambodia.
Vulnerability is conceptualized by many scholars (Adger, 2006, Eriksen and O'Brien, 2007,
Smit et al., 1996, Smith et al., 2000, Smit and Wandel, 2006). It can be best understood as an interaction between exposure, sensitivity and adaptive capacity. At any point in time, the greater the
exposure (of people, agricultural systems and businesses), the higher the vulnerability. Vulnerability, however, is reduced when the sensitivity of the system(s) is lower. Sensitivity decreases or increases over time due to the adaptation measures taken following the disaster(s). If exposure andsensitivity remain constant from household to household, adaptive capacity of individual households will be highly associated with their vulnerabilities.
This study adopts the approach to measuring household economic vulnerability posited andelaborated  in  Chaudhuri’s  (2003) study of household vulnerability. Household vulnerability as expected poverty is defined as the probability that households will move into poverty given certain
environmental shocks, current poverty status and household characteristics of respondents. While
poverty reflects the current state of deprivation, vulnerability reflects what   a   household’s   future  prospects are (Elbers and Jan, 2003). Thus, a household’s  consumption  or  income  can  be  regressed  on household characteristics and shocks in order to obtain the estimated coefficients to be used forfurther prediction of the household’s  future  poverty.  In  this  regard,  households  with  high  predicted  poverty are considered vulnerable. Unlike Chaudhuri (2003),   who   analysed   households’   monthly  per capita consumption  expenditure,  this   study   analyses   households’   monthly   income   to   measure  the household vulnerability index due to the lack of expenditure data.
Technically, the household vulnerability index is derived from the difference between the expected log per capita income and the minimum log per capita income threshold, with households
having per capita incomes lower than the minimum per capita income defined as vulnerable (poor).
The expected log per capita income is estimated using the three-step feasible generalised leastsquares (FGLS) method.
The predictors of log per capita income used in the analysis include: droughts in the past 12
years (dummy); windstorms in the past 12 years (dummy); floods in the past 12 years (dummy);
household size; level of education; possession of motored vehicle (dummy); access to credit(dummy); presence of disabled persons in the households (dummy); and the dependency of livelihood on agriculture (dummy).

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Survey design
The total land area of the KPS province is approximately 653,396 hectares divided into eight districts and 87 communes. The household survey samples were selected from six communes in six
districts within two geographical areas: highland areas (two districts) and the lowland area (fourdistricts). Three natural disasters were considered: flash flooding, drought, and windstorms. Areas
were defined using Geographical Information Systems, which can be used to produce a Digital Elevation Model. Administrative boundaries were used to define provinces, districts and communes.
Natural disaster occurrence was based on information given from key informant interviews fromthe Sub-national and local authorizes. A total of 600 questionnaires were collected from households.
RESULTS
Profile of the respondents
Among the six communes we selected, five communes were classified as rural and two communes
were located in highland. The majorities of the respondents were female and constituted 60% to
77% of each commune (Table 1). Also, an overwhelming number of respondents reported thatthey did not rely exclusively on agricultural work. In the rural highland commune of Tasal, thehighest number of respondents (13%) identified agriculture as the only source of income. On average, all respondents completed secondary education, and urban dwellers (Chbar Mon commune)
have higher education than their rural counterparts. The lowest school attendance was reported in
the highland communities of Tasal and Morhasaing.
Table 1 Sex and occupation of respondents
CommuneTopography

Number of
Respondents

Sex of respondents

Occupation of respondents

FemaleMale

Agriculture
only

Agriculture
and others

Chbar Mon

Urban/Lowland

100

65

35

9

91

Kork

Rural/Lowland

100

69

31

7

93

Morhasaing

Rural/Highland

100

73

27

6

94

Peang Lvea

Rural/Lowland

100

61

39

7

93

Rolang Chork

Rural/Lowland

100

65

35

7

93

Tasal

Rural/Highland

100

77

23

13

87

Total number

600

410

190

49

551

Total percentage
Source: Survey 2011

100%

68.3%

31.7%

8.2%

91.8%

Household Characteristics
There were on average five people within a household within the surveyed areas. This is well
above the national average household size (4.7) in 2008 (NIS, 2008). Rolang Chork has the smallest average household size (4.99 people per household on average) and the highest level of education among its population (9.6 years of schooling on average). The Kork and Chbar Mon communes had larger household sizes than the other selected communes (5.64 and 5.59 people perhousehold on average, respectively).
About 60% of respondents reported that their households have at least one motorcycle. There
was a large variation in the proportion of households possessing motorcycles between communes,
with the Chbar Mon (73%), Peang Lvea (74%) and Rolang Chork (68%) communes having a higher percentage of motorcycle-possessing households than the Tasal (44%), Kork (50%) andMorhasaing (53%) communes. The survey also revealed that 11.7% of respondents live in houseⒸISERD

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holds with at least one person with disability. Peang Lvea commune has the highest proportion ofhouseholds containing a person with a disability (21%), followed by Rolang Chork (13%), Kork(12%), Tasal (11%), Morhasaing (7%) and Chbar Mon (6%).
Natural hazards and exposure index
The three natural hazards that impact  on  people’s  livelihoods  in  Cambodia  including  flood,  windstorms and drought are investigated in this paper. It is important to note that the indicators of these
events are measured as dummy, indicating whether the respondents have experienced drought,
flood and windstorm over the last 12 years (1999-2010).
As indicated in Table 2, an overwhelming majority of respondents have reported experiencing
drought ranging in the last 12 years. In the rural communities of Morhasaing, Peang Lvea and Tasal,
100 % of the respondents reported experiencing drought. In contrast, the percentages of respondents who have experienced floods or windstorms in those 12 years are significantly lower thanthose who have experience drought.
Although respondents reported a slight decline in the number of droughts over the 12-year period, they had experienced a number of particularly devastating droughts in 1999, 2000, 2003 and2004. Villagers from the Pongrek village (Peang Lvea commune, Odung district) stated that the2010 drought was also a very severe drought which badly affected rice yields.
Table 2 Households experiencing floods, windstorms or drought in the last 12 years(1999-2010)
CommuneUrban/
rural

Total number of
respondents

Floods% of households
Windstorms

Drought

Chbar Mon

Urban

100

12

5

83

Kork

Rural

100

2

6

99

Morhasaing

Rural

100

9

13

100

Peang Lvea

Rural

100

2

5

100

Rolang Chork

Rural

100

31

12

90

Tasal

Rural

100

4

1

100

Total number

600

60

42

572

Total (%)

100%

10%

7%

95%

Source: Survey 2011

Vulnerability analysis based on expected poverty
Table 3 presents the results of the FGLS analysis. Among the environmental impacts, only drought
was found to have had a significant negative impact on log per capita income (b= -0.379, p<0.001).
This indicates that households who have experienced drought over the last three years are predicted
to have log per capita income 0.379 less than those who have not experienced drought (controllingfor other household and individual characteristics in the model). Windstorms and flooding did nothave significant impacts on log per capita income.
The windstorms and flash floods that occurred in the period between 2005 and 2010 did nothave a significant impact on log per capita income. This may be due to the long period of time overwhich the occurrences of windstorm and flash flooding were measured in this study. With the exception  of  drought,  the  respondents’  experience  of  windstorm  and  flash  floods  in  the  last  five  years  has not changed considerably. It may be difficult to detect environmental impacts that have frequently occur over long periods of time due to people having developed adaptive strategies to mitigate their impacts. Few respondents experienced flash flooding or windstorms in the years prior tocompleting the survey.
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Household size, the possession of motor vehicle and a livelihood dependency on agriculture
are significantly and inversely associated with log per capita income. Specifically, the larger thehousehold size, the lower the expected log per capita income (the coefficient is -0.182, p<0.001). In
addition, the possession of a motor vehicle is positively related to expected per capita income (thecoefficient is 0.312, p<0.001); while households who depend on agricultural work alone tend tohave lower per capita income than those households who have an additional secondary occupation(the coefficient is -0.899, p<0.001). In addition, the education attainment of respondents has a positive effect on log per capita income, although the effect is small (the coefficient is 0.044, p < 0.001).
Access to credit and the presence of person living with disability in the household does not significantly affect log per capita income.
Table 3 Results of least squares regression analysis of log monthly household
per capita income
Initial model
Coefficient

FGLS model

Drought in past 3 years-0.143Standard
error
0.067

Windstorm in past 12 years-0.264

0.133

0.047

-0.199

0.132

0.133

Flashfloods in past 12 years-0.076

0.113

0.503

-0.137

0.116

0.238

Household size

-0.197

0.017

0.000

-0.182

0.017

0.000

Education

0.047

0.010

0.000

0.044

0.010

0.000

Having motorcycle

0.265

0.071

0.000

0.312

0.072

0.000

Access to credit
Presence of person withdisability
Agriculture plus secondary
occupation-0.091

0.068

0.183

-0.061

0.068

0.375

-0.033

0.105

0.753

-0.026

0.106

0.808

-1.103

0.123

0.000

-0.899

0.118

0.000

3.700

0.138

0.000

3.714

0.139

0.000

Constant
Model summary
Adjusted R-squared

P>tCoefficient-0.379Standard
error
0.071

0.035

0.000

0.339

0.341

F (9,590)

33.64

33.97

N
Source: Survey 2011

600

600

P>t

Household vulnerability
The expected log per capita income obtained from the above FGLS analysis was used to create
vulnerability index at a US $1.00 daily threshold (Cambodia poverty line) and at a US $1.25 daily
threshold. Households are considered vulnerable (or poor in the future) if they score higher than
0.50 on the vulnerability index. Table 4 presents the results of the vulnerability analysis disaggregated by commune.
Given different household characteristics, the overall mean vulnerability is 0.65 using the
US$ 1.00 threshold with some small variation in the mean vulnerability across all six communes(Table 4). The vulnerability incidence is found to be high in highland and low in urban communities. The analysis suggested that contributing factors to vulnerability incidence among households
are livelihood (highly depending on agriculture) and low education. In our sample, we found thatvulnerability incidence is 70 %. This figure is high compared to the rate generated from generalvulnerability assessment in a provincial poverty report (NCDD, 2009).

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Table 4 Vulnerability index and incidence per commune at US $1.00 daily threshold
CommuneMean vulnerability

Standard deviation ofvulnerability

Vulnerability
incidence (%)

Rank

Chbar Mon

0.63

0.18

73.00

5

Kork

0.68

0.16

82.00

3

Morhasaing

0.64

0.17

76.00

4

Peang Lvea

0.66

0.16

83.00

2

Rolang Chork

0.61

0.17

73.00

5

Tasal

0.70

0.17

85.00

1

Total

0.65

0.17

78.67

When the threshold is increased to US $1.25 the mean vulnerability increases to 0.72 and theincidence of vulnerability increases to 90.67% (an increase of 39.5%). This means that with a cutoff point of US $1.25, more than 90% of these respondents will become poor in the future. The
highest percentage of vulnerable households is in Kork (with 98 %), followed by Peang Lvea with
93%, Kork with 92%, Rolang Chork with 89%, Morhasaing with 87% and Chbar Mon with 85%.
Based on the model, it is apparent that poor communes, like Tasal and Kork, as indicated by
low percentage of motor vehicle possession, high percentage of villagers depending solely on agriculture and a high percentage of households with a person with a disability, are likely to be vulnerable now and in the future.
Vulnerability and per capita income
Each graph below is divided into four sections representing the vulnerability cut-off point and poverty cut-off point at a US $1.00 threshold in Fig. 2 and at a US $1.25 threshold in Fig. 3. Dots in
the graphs represent the interviewed households. Those households in the upper left quadrant areboth currently poor and vulnerable, that is, they are likely to continue to be poor in the future.
Those in the lower left quadrant are households that are currently poor but are not vulnerable.
Those in the upper right quadrant are households that are currently not poor but vulnerable, meaning that they are likely to be poor in the future. Households that are in the lower right quadrant areneither currently poor nor vulnerable, meaning that they are less likely to be poor in the future.

Fig. 2 Vulnerability vs log per capita income at US $1.00 threshold
Comparing Fig. 2 and Fig. 3, it is evident that increasing the threshold of per capita income
from US $1.00 to US $1.25 decreases the number of households that are not currently vulnerable
and increases the number of households that are vulnerable. This means increasing the per capitaⒸISERD34

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income threshold results in moving more households into future poverty, regardless of their current
poverty status. However, the increase in the future incidence of poverty due to an increase in thethreshold is more severe among those who are currently poor than among their counterparts who
are not currently poor in all communes, with a higher increase in the number of poor and vulnerable in poor communes like Tasal, Rolang Chork and Kork.

Fig. 3 Vulnerability versus log per capita income at US $1.25 threshold
DISCUSSION
Natural hazards have had multiple impacts on communities in the KPS province. Windstorms had
the smallest impact on people, while flashfloods occurred infrequently but caused medium degrees
of damage while drought was the most severe and pronounced hazard.
The drought in 2003 decreased rice yields, this means that villagers had inadequate food.
Some villagers began eating vines as a substitute for rice. As a result of the impacts of disasters, inevery study site commune people migrated to find employment opportunities elsewhere. Among
the respondents from the six communes, the percentage of temporary migration is about 30% in
Tasal and Kork, about 10% in Morhasaing and Chbar Mon, 20% Rolang Chork, with Peang Lvea
having the highest rate of migration at 60%. Many anecdotes were shared in the focus group discussions about children dropping out of school and women bearing health and livelihood burdens
due to the impacts of disasters.
Discussions in Kork commune stated that poor families were the most vulnerable to drought incommunity, especially those with children who consume unclean water, therefore getting sick andincurring an additional cost for their families. During periods of drought some households had theability to rent pumping machines so they could pump water from nearby sources in order to supply
their rice fields with the water necessary for rice production. Others that were short on capitalwould have to take loans from microcredit institutions and local lenders. For some this resulted inhigh levels of debt which could not be paid back and hence assets and property were confiscated.
The survey here found that farmers usually undercultivated land and produced low yields (aslow as 500 kilograms per hectare during the drought spell). Believing that agricultural adaptation
options could increase yield and income as well as food security, farmers have considered many
strategies to mitigate drought impacts and increase rice production including changes in crop calendar and crop variety and the increased use of chemical fertilizer. The most urgent adaptation
measure in KPS is to provide water for rice fields, especially when there is a prolonged drought
during the middle of a wet season. The experts in agriculture of the province highlighted thatproviding training on modern agricultural methods and small-scale water management are also apriority.
While the model used in this study captures only household characteristics and natural hazards
and  escaping  from  disasters  are  beyond  households’  capacity  and  should  rest  on  other  stakeholders  ⒸISERD35

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such as government agencies, non-governmental organizations (NGOs) and private sectors. Currently, there are a number of stakeholders working to assist farmers such Department of Agriculture but it may not effective in terms of the increasing threats from climate change.
CONCLUSION
Changing climate and climate variability may prevent Cambodia from reaching its development
goals, particularly those related to reducing poverty. As indicated by the Ministry of the Environment (MoE), flash flooding and drought are responsible for the reduction in rice production in
Cambodia (MoE, 2001, MoE, 2005). At the provincial level, Yusuf and Francisco (2010) indicatedthat the KPS province is the third most vulnerable province to climate change in the country.
The study aims to identify the impact of environmental shocks (flash floods, windstorms anddrought) and household characteristics on per capital income and to create vulnerability indexes topredict future poverty incidence in the communities of the KPS province. Drought is found to be
the most common environmental hazard experienced by people in these communities. The study
found that, among the three indicators of environmental shocks, only drought resulted in a decreasein the per capita income of households between 2008 and 2010. Drought can be more devastating
than either windstorms or flash flood, as it can easily destroy an entire season of crop.
Two of the household characteristics that negatively affected household per capita income area household size and agricultural dependency. This suggests that households that have many children and do not have other sources of income are likely to be poor and continue to be poor (vulnerable)  in  the  future.  In  contrast,  the  education  of  respondents  and  households’  possession of motor
vehicles are positively related to per capita income.
Some policy recommendations resulting from this study include building irrigation systems to
mitigate the impacts of droughts, ensuring secondary income generation opportunities for poor
households so that they do not depend exclusively on agriculture, and increasing the education level of villagers.
In conclusion, drought is the most severe natural hazard in KPS and farmers reported that they
regularly experience a late onset of rainfall. There is a widespread problem of water scarcity andmost farmers do not have access to irrigation systems or other technology that can supply water,such as water pumps. Based on the VEP, the study shows that many people are unable to cope withenvironmental shocks, especially drought. As a result, farmers are not able to fully cultivate their
paddy fields and therefore experience reduced crop yields. It is imperative to increase the adaption
capacity of farmers who have livelihoods that are highly sensitive to natural hazards. Diversifying
livelihoods is also recommended, but it is beyond the scope of this paper. While enhancing adaptive capacity is very critical, there is currently limited intervention from the local government andsub-national government. Whereas NGOs are working to enhance the livelihoods of communities
through community risk management, there has only been limited cases of direct resource andknowledge transfers. Farmers have limited access to credit at their locality when they need it mostto save their livestock and paddy fields during natural hazards.
ACKNOWLEDGEMENTS
This work was carried out with the aid of a grant from the International Development ResearchCentre, Ottawa, Canada and Economy and Environmental Program for Southeast Asia. Also, there
were contributions from the Hue College of Economics, SEARCA, the University of the Philippines Los Banos and the Provincial Department including Provincial Hall, the Department of Water
Resource and Meteorology and the Department of Agriculture in the Kampong Speu province. Rural communities are highly appreciated for their time answering our questions. Authors are gratefulto Dr. Yoko Mochizuki for her comments on the paper.

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REFERENCES
Adger, W.N. 2006. Vulnerability. Global Environmental Change, 16, 268-281.
Am, P. 2010. Impact of climate change on rice production of Cambodia, MAFF, Phnom Penh.
Ang, S., Oeur, I. and McAndrew, J. 2007. Understanding social captal in response to floods and droughts.
http://www.ccc-cambodia.org.
CDRI. 2007. Annual Development Review 2006-07, Cambodia Development Resource Institute (CDRI),
Phnom Penh.
CDRI. 2012. Cambodia’s   Priorities   for   Inclusive   growth,   Regional   Integration   and   ASEAN   Leadership,
Phnom Penh,
Chan, S. and Sarthi, A. 2002. Facing the Challenges of Rural Livelihoods, CDRI, Phnom Penh.
Chaudhuri, S. 2003. Assessing vulnerability to poverty: concepts, empirical methods and illustrative
example http://info.worldbank.org/etools/docs/library/97185/Keny_0304/Ke_0304/vulnerabilityassessment.pdf.
Elbers, C. and Jan, W.G. 2003. Staying Poor: Chronic Poverty and Development Policy, University ofManchester, UK.
Eriksen, S.H. and O'Brien, K. 2007. Vulnerability, poverty and the need for sustainable adaptation measures.
Climate Policy, 7, 337-352.
Geres-Cambodia. 2009. Public perceptions of climate change in Cambodia. Phnom Penh Franke, Sandra.
2005., Canada.
McCarthy, J., Canziani, O., Leary, N., Dokken, D. and White, K. 2001. Climate Change 2001: Impacts,
Adaptation and Vulnerability, Cambridge University Press, Cambridge.
MoE. 2001. Vulnerability and Adaptation Assessment to Climate Change in Cambodia, Ministry ofEnvironment, Phnom Penh.
MoE. 2005. Vulnerability and adaptation to climate hazards and to climate change: A survey of rural
Cambodia households, Ministry of Environment, Phnom Penh.
MoE. 2006. National Adaptation Programme of Action to Climate Change, Ministry of Environment, Phnom
Penh.
MoE and UNDP Cambodia. 2011. Cambodia Human Development report 2011. Building Resilience: theFuture of Rural Livelihoods in the Face of Climate Change.
NCDD. 2009. Kampong Speu Data Book 2009, NCDD, Phnom Penh.
NCDM. 2003. Mapping vulnerability to natural disasters in Cambodia. Phnom Penh, National committee fordisaster management (NCDM), Phnom Penh.
NCDM. 2008. Strategic National Action Plan, National Committee for Disaster Management, Phnom Penh.
NIS. 2008. Statistical Year Book of Cambodia 2008, Ministry of Planning, Phnom Penh.
NIS. 2009. Cambodia Socio-economic Survey 2009, Ministry of Planning, Phnom Penh.
Smit, B., McNabb, D. and Smithers, J. 1996. Agricultural adaptation to climatic variation. Climatic Change,
33, 7-29.
Smit, B. and Wandel, J. 2006. Adaptation, adaptive capacity and vulnerability. Global Environmental Change,
16, 282-292.
Smith, B., Burton, I., Klein, R.J.T. and Wandel, J. 2000. An Anatomy of Adaptation to Climate Change andVariability. Climatic Change, 45, 223-251.
WFP. 2003. Mapping vulnerability to national disasters in Cambodia, Phnom Penh.
World Bank. 2006. Managing risk and vulnerability in Cambodia: An assessment and strategy for social
protection, Washington DC.
Yu, B. and Fan, S. 2011. Rice production response in Cambodia. Agricultural Economics, 42, 437-450.
Yusuf, A.A. and Francisco, H.A. 2010. Mapping climate change vulnerability in Southeast Asia, Singapore.

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