IJERD – International Journal of Environmental and Rural Development (2013) 4-2

Research article
erd

Toward Measuring the Vulnerability of Agricultural
Production to Flood: Insight from Sangkae River
Catchment, Battambang Province, Cambodia
CHINDA HENG
The Learning Institute, Phnom Penh, Cambodia

SOTHEAVIN DOCH*
The Learning Institute, Phnom Penh, Cambodia
Email: sotheavin@learninginstitute.org, sotheavin_doch@yahoo.com

JEAN-CHRISTOPHE DIEPART
The Learning Institute, Phnom Penh, Cambodia
Received 15 December 2012

Accepted 10 June 2013(*Corresponding Author)

Abstract The study proposes an indicator-based analysis on the vulnerability ofagricultural production to flood issues in a river catchment area. The study site is the
Sangkae River catchment area located in the Northwestern region of Cambodia and theunit of observation is the commune. Flood hazards are not restricted to the downstream
lowland Tonle Sap plain; the study also considers river overflow and run-off flood eventsoccurring upstream in Sangkae River catchment. We address the concept of vulnerability
in three dimensions (exposure, sensitivity and adaptation capacity) and operationalize it ina multi-level analytical framework.We first identify indicators relevant with each of thethree dimensions of vulnerability. We then combine the standardized and weighted
indicators into composite exposure, sensitivity and adaptive capacity indexes, which weanalyze statistically and spatially with a geographic information system. We further
integrate the indicators in a hierarchical cluster analysis to establish a typology ofcommune vulnerability across the catchment. The results of the study showed the link
between the vulnerability of agriculture to flood and the different farming systems of ruralcommunities.
Keywords flood management, vulnerability assessment, agricultural production,watershed management, Cambodia

INTRODUCTION
Cambodia is one of the most vulnerable countries to climate change in Southeast Asia (Yusuf andFransico, 2009). The processes of climate change are complex and diversified but are mainly atplay through the intensification of the water cycle (Huntington, 2006). Climate change increases
the occurrences of extreme weather phenomena, such as heavy rainfall, flood, drought, storms, etc.
(Solomon et al, 2007). The modification of rainfall pattern has affected the water level of MekongRiver and Tonle Sap Lake (MRC, 2010). Due to the river run-off from upper Mekong River, thewater level of the Tonle Sap is projected to increase from 1 meter to 2.3 meters by the year 2030
(Eastham et al., 2008).
However, flood is not a new phenomenon in Cambodia. Many parts of the country haveflooding experiences every year, particularly in the central area of the country where floods areassociated with the reversal of water in the Tonle Sap River and the flooding of the large
Cambodian central plain. People have developed ways to practice agriculture and fishing, which
are well adapted to this unique phenomenon. As Suon rightly put, floods are usually good for ricebased agriculture but their irregularity and unpredictability bring negative impact on agricultural
production and rural livelihood systems (Suon, 2007). Major flood events, such as the one that

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occurred in 2011, had for instance very serious consequences in the Cambodia economy. The lossof agricultural productions and degradation of physical infrastructures were worth over $400
million (CRED, 2011).
Over the last 10 years, Battambang has witnessed a dramatic agricultural colonization of
peripheral forest areas. Forest cover has become the substitute for agro-industrial cash crops very
rapidly, in a process fuelled by important internal immigration movements of people coming fromthe lowland densely populated areas (PMPSWG, 2011). The conversion of the upland evergreen
forest areas into agricultural land is also very likely to affect the hydrological system and toincrease surface and river water run-off (Kirsch, 2010). Future flood patterns are thus very likely to
be modified by the combined effect of climate and land use change. These transformations are very
likely to result in a change in agricultural production and the challenges at stake are important as
agriculture is the main source of livelihood for a very large majority of Cambodian householdliving in the rural and who make up to 80% of the entire population (RGC, 2010a). Flooding may
contribute to increased poverty in rural Cambodia and have serious consequences in terms ofavailability and accessibility to food (NAPA, 2006; Helmers and Jegillos, 2004).
Flood risk management has been considered as key priority for poverty alleviation anddevelopment of Cambodia (RGC, 2010b). Several institutions and committees have been
established from national down to local level to respond to natural disasters (Committee forDisaster Management). In Battambang province, principles and concepts of Integrated Water
Resources Management (IWRM) have been introduced for the management of water resources atthe catchment level (Yem, et al., 2011) and integrated in the provincial spatial plan (PMPSWG,
2011). However, weak cross-sector coordination and the lack of tools to support decision-making
have considerably impeded effective flood management (Eng, 2009). The involvement andparticipation of Battambang Provincial Spatial Planning Team in this research process can beviewed as a first step toward the design of a flood management, decision-making tool for provincial
authorities.

Fig. 1 Sangkae River catchment area
OBJECTIVE
The study aims to achieve two things. First, we aim to understand the vulnerability of agricultural
production in Sangkae River catchment area and second, to provide recommendations to improve
flood management as part of an integrated water resource management system.

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METHODOLOGY
Conceptual framework
The concept of vulnerability of social and environmental systems has a history of several decades.
One of the best known definitions was formulated by the International Strategy for Disaster Risk
Reduction (UN/ISDR), which defines vulnerability as “the condition determined by physical, social,economic and environmental factors or processes, which increase the susceptibility of a community
to the impact of hazard” (UN/ISDR, 2004). Originally envisaged in the context of natural disaster
reduction, the concept of vulnerability was further developed with contributions made by climatechange scientists (IPCC, 2001). Adger (2006) stresses that vulnerability is most often
conceptualized as being constituted by components that include exposure to change or external
stresses, sensitivity to change, and the capacity to adapt. Exposure comprises the degree, duration,
and/or extent in which the system is in contact with a hazard, or subject to the change (Kasperson
et al., 2005; Adger, 2006). Sensitivity is the extent to which a human or natural system can absorb
impacts without suffering long-term harm or other significant state change (Adger, 2006). The
system’s coping capacity (Turner et al., 2003), or capacity of response (Gallopin, 2006), is also
called adaptive capacity by the IPCC (2001); Adger (2006) and Smit and Wandel (2006). As notedby Smit and Wandel (2006), some authors apply “coping ability” to shorter-term capacity or theability to just survive, and employ “adaptive capacity” for longer-term or more sustainableadjustment.
Following the IPCC framework, we address the concept of vulnerability of agriculture
production to flood with three lenses: exposure, sensitivity and capacity of responses. While
exposure refers to the occurrence, magnitude, and locations of the flood events; sensitivity
concerns the impacts of the flood events on agricultural land and production in the River catchment.
Capacity of responses deals with both the short-term coping mechanism and long-term adaptive
strategy to respond to the flood impacts (Fig.2).

Vulnerability of Agricultural Production
Land use Change

HydrologicalsystemClimate Change

Exposure

Sensitivity

Flood (Major & Minor) Flood)

Agricultural
Production

Capacity of responses [Coping
mechanism and adaptive
strategy]

Fig. 2 Research conceptual framework
Helmers and Jegillos (2004) reported that there are two types of flood usually occurring in
Cambodia: flash flood and central area flood. Flash floods result from heavy downpours upstream
on the Mekong River and affect the provinces along the Mekong as well as in the southern areas of
the country. The central area floods result from a combination of run-off from the Mekong andheavy rains around the Tonle Sap Lake (Helmers and Jegillos, 2004). We retain the central area
flood as one separate type of flood. However, our study differentiates between river overflow flood(Sangkae river and its tributaries) and surface water run-off flood. The combinations of thesefloods are also considered (Fig. 3). As flood may not solely affect the down-stream part of the
Sangkae River catchment (Tonle Sap flood plain), but also the up-stream part of the catchment; wedecided to investigate flood in the whole Sangkae River catchment.

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According to the Mekong River Commission, flood intensity is classified into three
categories: minor flood, medium flood, and major flood, with frequency of occurrence ofrespectively every one, ten and twenty years (MRC, 2002). Our study focuses on two types offlood: normal flood considered here as a usual annual flood (minor flood according to MRC
classification), and the severe 2011 flood, equivalent in magnitude to a medium flood according to
MRC classification.

Fig. 3 Flood incidence in the Sangkae River catchment
Analytical framework
The assessment framework designed to approach, analyze and understand the vulnerability ofagricultural production to flood in the study area is based on indicators. Each dimension ofvulnerability (exposure, sensitivity and adaptive capacity) is measured through a number ofindicators along several data sources (Table 1). The values of each indicator are standardized,
weighted by a coefficient determined with local stakeholders and then combined into composite
exposure, sensitivity and adaptive capacity indexes, which we analyze spatially with a geographic
information system. We compute an overall vulnerability index by using the formula“Vulnerability = [(Exposure +Sensitivity)-Adaptive Capacity]”. We further integrate the indicators
in a hierarchical cluster analysis to establish a typology of commune vulnerability across thecatchment, which we interpret by computing, for each vulnerability type, the mean values of eachindex.
Data collection and tools
Primary data collection on the vulnerability of agricultural production to flood was mainly carriedout through commune workshops organized in each commune in 2012 with a group of 10-15
participants for each commune. These workshops started with a participatory flood mapping
exercise focusing on the exposure and sensitivity dimensions of vulnerability. The mapping offlood areas in normal year (minor flood) and in 2011 rests on the knowledge of local authorities, as
they are the main information providers. Aerial photos retrieved from the Google Earth Pro server(www.googlearth.com) covering the entire communal territory were printed on A0 size paper andwere overlaid with plastic covers. The group of participants was invited to identify the main water
ways and bodies in the commune as well as the agricultural land areas. They were then asked tomap out the agricultural land affected by a usual flood and affected by the 2011 flood and for each

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flood area, to provide information on the type of flood, their duration and their actual impact on the
different agricultural productions. Lastly, a structured questionnaire provided data on theinstitutional capacity of the commune to adapt to flood. This included questions on i) the efficiencyof the flood warning system, ii) the mobilization of self-help groups in case of flood, iii) the
existence and efficiency of external support, iv) the allocation of communal funds for post-disastermanagement, v) the efficiency of the natural disaster management committee, vi) the provision andquality of training programs for farmers and how well these training programs address flood
management and vii) the existence of farmer organizations in the commune.
In addition, secondary datasets were consulted to build a number of other indicators. The GIS
provincial spatial planning database was useful to calculate indicators based on spatial layers: theagricultural land area and road density network (PMPSWG, 2011). The commune databaseprovided useful updated statistical references for other indicators (http://db.ncdd.gov.kh/cdbonline).
Table 1 Survey analytical framework
Indicators

Dimension

Exposure

Sensitivity

Adaptive
capacity

Weight in overallcomposite index

Major (2011) flood area size, expressed as a percentageof the total agricultural area in the commune
Minor flood area size, expressed as a percentage of thetotal agricultural area in the commune
Major (2011) flood area size weighted by the durationof the flood and expressed as a percentage of the total
agricultural area in the commune
Total agricultural land area expressed as a percentage ofthe total commune area size
Percentage of population involved in agriculture in thecommuneMajor (2011) flood area weighted by impact on
production and expressed as a percentage of the total
agricultural area in the commune.
Total cultivated area during flood period expressed as apercentage of total cultivated area in commune.

Data source

0.4
0.4

Commune workshop
conducted in 2012

0.2
0.3
0.1
0.3
0.3

Institutional capacity of commune

0.3

Percentage of population in commune above poverty
line

0.25

Density of road network in commune

0.2

Literacy rate in commune

0.25Interpretation of Land sat
satellite image (2010)
Commune data base(2010 update)Commune workshop
conducted in 2012
Commune data base(2010 update)Commune workshop
conducted in 2012
Commune data base(2010 update)
GIS-based calculation
based on roadinformation (2010)
Commune data base(2010 update)

RESULTS AND DISCUSION
Annually, flood affects 252.83 km2 of agricultural land area in the Sangkae River catchment. The
flooded area in 2011 was 32% larger than the area flooded during a normal flood (Table 2). Amongthe three different types of floods, the Tonle Sap flood is by far the most important in terms ofaffected area size and duration, if compared with river overflow and surface run-off floods.
However, the difference of flooded area size between a minor flood and the 2011 flood is
proportionally much more important for a river overflow and surface run-off flood than for the
Tonle Sap flood (54% against 25% increase).In the event of more frequent extreme flood events
driven by climate change, our preliminary results suggest that flood management strategy in thecatchment should pay greater attention to upper stream areas where floods hit and are likely to
become more important in the future.

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Exposure
There are several factors that determine the level of exposure of the communes; the topography, theorigin of the flood and flood duration. Figure 4 shows that not only communes located in the Tonle
Sap flood plain are exposed to flood. Some communes located in the middle and up-stream also
have high exposure levels where the effects of river overflow are aggravated by surface run-off
flood (Fig. 4). Second, flooding by Tonle Sap has a longer duration than both of the other types offlood (Table 2). This duration factor reinforces the high exposure levels of the down-stream
communes.
Table 2 Flood impacts, duration and occurrence
Flood types
Tonle Sap (central area)
Direct river overflow
River overflow & Surface runoff
Surface run-off
Total

Agriculture flooded
area (km2)
major
minor
flood
flood
249.94
188.45
62.72
46.44
40.62

1.01

16.88
370.16

16.93
252.83

Percentage ofincreasing
flood area size
24.60
54.08

Duration (meanvalues in days)
major
minor
flood
flood
63.6
66.35
15.26
23.89

Occurrence(mode of values)
major
minor
flood
flood
Sep-Oct
Sep-Nov
Aug-Oct
Sep-Oct

8.31

24.75

Jul-Nov

Aug-Sep32

40.25

Aug-Oct

Aug-Oct

32

Sensitivity
The factor being the most determinant in explaining the sensitivity of communes to flood is thepossibility for the commune to diversify its agriculture production. We refer here to both thedevelopment of multi-cropping systems and the development of dry season production. The agroecological environment is of primal importance. The communes with high sensitivity are logicallythe communes in which agriculture land dominates in the land use. Amongst this group, thecommunes with little crop diversification besides the rain-fed rice are particularly sensitive. For thecommunes located at the down-stream of Sangkae River catchment where the agro-ecologicalenvironment allows for crop diversification and the practice of a dry season recession rice to avoid
the flood peak period, the level of sensitivity decrease sharply even for commune which are locatedin the Tonle Sap plain. In the up-stream, the commune implements a multi-cropping systemcovering both dry and rainy seasons and are less sensitive to flood (Fig. 4).
Adaptive capacity
Down-stream communes have higher adaptive capacity (Fig. 4). This is due to the proximity withthe city of Battambang, which greatly improves access to external intervention. The proximity of
the city also offers opportunities of labor diversification away from agriculture, which can beconsidered here as an important adaptation strategy. We also noted that communes with extensive
experience with floods have developed more efficient adaptation mechanisms and strategies. We
also note important differences of adaptive capacity between communes that are located in the upstream area of the catchment. What explains these differences is the relative capacity of thecommune councils to mobilize resources internally and externally to address post-flood
management.
Vulnerability
The interpretation of the multivariate statistical analysis of all indicators allows for theidentification of five main types of vulnerability that we further ranked into five classes from “verylow” to “very high” vulnerability (Table 3 and Fig. 4).
Table 3 indicates that exposure and sensitivity have a preponderant influence on the© ISERD
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vulnerability of agricultural production to flood. The exposure and sensitivity indices are positively
correlated (Tab.3). However, exposure is more decisive in explaining the highest vulnerability level.
However, the measures that need to be addressed to reduce the exposure implies heavy civilengineering works (dikes, riverbanks reinforcement), which are far beyond what the communes andcommunities can actually afford. In the upstream areas, measures to reduce exposure could be theterracing of steep land in a view to reduce water surface run-off.
Notwithstanding, the communes have more options and opportunities to reduce their
sensitivity to flood: diversification of cropping systems, water storage systems and the paralleldevelopment of dry season agriculture are processes that could considerably reduce thevulnerability of agriculture to flood. Table 3 also indicates that the level of commune vulnerability
is also quite influenced by the adaptive capacity. This is a domain where provincial, district, andcommune authorities should prioritize their action. What is particularly needed is the establishmentof an effective institutional platform where communes could formulate their need to improve flood
management (pre/post disaster) and through which support and external assistance should bedelivered. The watershed management committee may play this role.
Table 3 Commune vulnerability index
CommuneOverall exposure
Overall sensitivity
vulnerability*
Index (mean of Z-score)
Index (mean of Z-score)
Very low
Very low [-0.7323808]
Very low [-1.5983150]
Low
Medium [-0.3817195]
Medium [0.4457365]
MediumLow [-0.7077337]
Low [-0.2380573]
High
High [1.1328966]
Very high [0.9314991]
Very highVery high [1.8084319]
High [0.6203428]* Vulnerability classification made with a hierarchical cluster analysis (Ward’s method)

Overall adaptive capacity
index (mean of Z-score)Low [-0.3817778]
High [1.0304744]
Very low [-0.8889612]
Very high [1.0362228]
Medium [-0.2394899]

Fig. 4 Flood exposure, sensitivity, adaptive capacity and overall vulnerability indexes bycommune in across the Sangkae River catchment area

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CONCLUSION
Our study has presented a methodology to analyze and understand the vulnerability of agricultural
production to flood. A great deal of information collected through the research process wasprovided by local stakeholders who are directly affected by flood. The tools and data collection
methods are easy to grasp and the approach can be easily replicated in the context of other river
basins. The approach is based on the measurement of specific indicators related to three dimensionsof vulnerability, namely exposure, sensitivity and adaptive capacity. It further develops composite
indexes that integrate a large number of factors in a single value. The use of a geographic
information system enables us to comprehend the geography of vulnerability across a river basin
area. Additionally, the indicators can be compared over time so that they are indicative values towatershed authorities for making strategic and operational decisions to improve flood management.
The framework can be refined further in a number of ways. One is by revising the existing
indicators and by identifying new ones. Another could be by incorporating rainfall data so that
better links can be made with climate change. The survey has been realized in partnership with aspatial planning team from Battambang and should be considered as a step towards developing aflood management decision-making tool for provincial authorities.
ACKNOWLEDGMENT
We are grateful to the Canadian International Development Research Center (IDRC) for thefinancial support of this study. We address our sincere thanks to the provincial spatial planningteam of Battambang, students of Royal University of Agriculture in Cambodia and to all
respondents for their support and participation. We are also grateful to Valentin Joyeux, student atUniversity of Liège (Gembloux Agro-Bio Tech, Belgium) for his support during the survey design
and the analysis.
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