ratchanok dissertation final
DESCRIPTION
rice dissertationTRANSCRIPT
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The Pennsylvania State University
The Graduate School
College of Earth and Mineral Science
VULNERABILITY OF THAI RICE PRODUCTION TO SIMULTANEOUS
CLIMATE AND SOCIOECONOMIC CHANGE:
A DOUBLE EXPOSURE ANALYSIS
A Dissertation in
Geography
by
Ratchanok Sangpenchan
2011 Ratchanok Sangpenchan
Submitted in Partial Fulfillment
of the Requirements
for the Degree of
Doctor of Philosophy
December 2011
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The dissertation of Ratchanok Sangpenchan was reviewed and approved* by the
following:
Brent Yarnal
Professor of Geography
Associate Head of the Department of Geography
Dissertation Advisor
Chair of Committee
William Easterling
Professor of Geography
John Kelmelis
Professor of International Affairs
James S. Shortle
Distinguished Professor of Agricultural and Environmental Economics
Karl Zimmerer
Professor of Geography
Head of the Department of Geography
*Signatures are on file in the Graduate School
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ABSTRACT
This dissertation explores the vulnerability of Thai rice production to
simultaneous exposure by climate and socioeconomic change so-called double
exposure. Both processes influence Thailands rice production system, but the
vulnerabilities associated with their interactions are unknown. To understand this double
exposure, the research adopts a mixed-method, qualitative-quantitative analytical
approach consisting of three phases of analysis involving (in order) a Vulnerability
Scoping Diagram, a Principal Component Analysis, and the EPIC crop model. Using
proxy datasets collected from secondary data sources at the provincial level, the first and
second phases together identify the key variables representing each of the three
dimensions of vulnerability exposure, sensitivity, and adaptive capacity. Results show
that the greatest vulnerability in the rice production system occurs in households and
areas with high exposure to climate change, high sensitivity to climate and
socioeconomic stress, and low adaptive capacity. The results also show the geographical
distribution of vulnerability across the country and locate four provinces with low
vulnerability to the double exposure. In the third phase, for each of these four provinces,
the EPIC crop model simulates rice yields associated with future climate change as
projected by two downscaled global climate models. Climate change-only scenarios
demonstrate that yields are expected to decrease 10% from the current productivity
during 2016-2025 and 30% during 2045-2054 under projected changes in climate and
rising CO2 levels. Scenarios applying both climate change and improved technology and
management practices show that a 50% increase in rice production is possible, but
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requires strong collaboration between sectors to advance agricultural research and
technology. Moreover, disseminating these advancements requires the strong adaptive
capacity in the rice production system characterized by well-developed social capital,
social networks, financial capacity, and infrastructure and household mobility at the local
scale. The vulnerability assessment and climate and crop adaptation simulations used
here provide useful information to decision makers developing vulnerability reduction
plans in the face of concurrent climate and socioeconomic change.
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TABLE OF CONTENTS
LIST OF FIGURES ..................................................................................................... viii
LIST OF TABLES ....................................................................................................... x
ACKNOWLEDGEMENTS ......................................................................................... xi
CHAPTER 1 INTRODUCTION ................................................................................ 1
1.1 Impact factors: Climate change, the socioeconomic system, and their
interaction ...................................................................................................... 3
1.1.1 Climate factors ...................................................................................... 4
1.1.2 Socioeconomic factors .......................................................................... 6 1.1.3 Interactions between climate and socioeconomic factors: Double
exposure ................................................................................................. 8 1.1.3.1 Demand ...................................................................................... 9 1.1.3.2 Supply ......................................................................................... 10
1.2 Agriculture in Thailand ................................................................................... 13 1.2.1 Overview ...................................................................................................... 13
1.2.2 Climate and socioeconomic impacts ........................................................... 15 1.3 Research Framework: Vulnerability and Scale .............................................. 18 1.4 Research Goal, Questions, and Objectives ..................................................... 24
1.4.1 Research questions ............................................................................... 24
1.4.2 Research objectives .............................................................................. 25 1.5 Study Area ...................................................................................................... 25 1.6 Scope of the Study .......................................................................................... 30
1.7 Thesis Overview ............................................................................................. 31
CHAPTER 2 METHODS ........................................................................................... 32
2.1 Phase 1: The Vulnerability Scoping Diagram ................................................ 33 2.1.1.1 Physical vulnerability ................................................................. 35
2.1.2.1 Socioeconomic vulnerability ...................................................... 36 2.1.2.1.1 Human capital .................................................................. 36 2.1.2.1.2 Financial capital ............................................................... 37 2.1.2.1.3 Social capital .................................................................... 39
2.1.2.1.4 Physical capital................................................................. 40 2.1.2.1.5 Natural capital .................................................................. 41
2.1.3 Developing the VSD ............................................................................. 42
2.1.4 VSD input data ..................................................................................... 46 2.1.4.1 Climatic variables: temperature, rainfall and SPI ...................... 46 2.1.4.2 Other biophysical data ................................................................ 50 2.1.4.3 Socioeconomic proxies .............................................................. 51
2.2 Phase 2: The Principal Component Analysis .................................................. 53
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2.3 Phase 3: Crop Model ...................................................................................... 55 2.3.1 Description of EPIC and the applications ............................................ 58
2.3.2 EPIC input data ..................................................................................... 62 2.3.2.1 Climate change projection data .................................................. 62 2.3.2.2 Soil data ...................................................................................... 66 2.3.2.3 Crop growth and crop management data .................................... 67 2.3.2.4 Adaptation options ..................................................................... 68
2.4 The justification for this research and its focus .............................................. 69
CHAPTER 3 PRINCIPAL COMPONENT ANALYSIS OF VULNERABILITY .... 71
3.1 Overview of PCA .......................................................................................... 71
3.2 Exposure ......................................................................................................... 76 3.2.1 Preliminary analysis ............................................................................. 76 3.2.2 PCA results ........................................................................................... 78
3.2.3 Interpretation of PCA results ................................................................ 82 3.3 Sensitivity ....................................................................................................... 85
3.3.1 Preliminary analysis ............................................................................. 85 3.3.2 PCA results ........................................................................................... 86 3.3.3 Interpretation of PCA results ................................................................ 90
3.4 Adaptive Capacity .......................................................................................... 96 3.4.1 Preliminary analysis ............................................................................. 96
3.4.2 PCA results ........................................................................................... 97 3.4.3 Interpretation of PCA results ................................................................ 100
3.5 Conclusions..................................................................................................... 104
CHAPTER 4 VULNERABILITY MAPPING ........................................................... 106
4.1 Physical vulnerability ..................................................................................... 107 4.2 Social vulnerability ......................................................................................... 112
4.2.1 Sensitivity ............................................................................................. 112
4.2.2 Adaptive capacity ................................................................................. 119 4.3 Calculating overall vulnerability .................................................................... 123
CHAPTER 5 CLIMATE AND CROP YIELD SCENARIOS ................................... 126
5.1 Climate projection scenarios ........................................................................... 127
5.2 Crop and crop management scenarios ............................................................ 135
5.2.1 Crop and crop management baseline parameterization ........................ 135
5.2.1.1 Crop parameter ........................................................................... 136 5.2.1.2 Soil data ...................................................................................... 138
5.3 Simulation results for scenarios 1 and 2 ......................................................... 139 5.3.1 Crop yields ............................................................................................ 140 5.3.2 Evapotranspiration and water use efficiency ........................................ 142 5.3.3 Discussion: impacts of combined crop-climate relationships on
yields ...................................................................................................... 145
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5.3.3.1 Scenario 1 ................................................................................... 145 5.3.3.2 Scenario 2 ................................................................................... 147
CHAPTER 6 ADAPTIVE STRATEGY SCENARIOS ............................................. 149
6.2 Simulation results for Scenarios 3 and 4 ........................................................ 154 6.2.1 Crop yields ............................................................................................ 154
6.2.1.1 Option 1: No Sc + Min ............................................................... 154 6.2.1.2 Option 2: No Sc + Max .............................................................. 155
6.2.1.3 Option 3: Sc + Min ..................................................................... 158 6.2.1.4 Option 4: Sc + Max .................................................................... 158
6.2.2 Water use efficiency and evapotranspiration ....................................... 159
6.2.3 Discussion: impacts of combined crop-climate and elevated CO2
relationships on yield ............................................................................. 165 6.2.4 Integrating the results from all phases .................................................. 166
CHAPTER 7 DISCUSSION AND CONCLUSIONS ................................................ 175
7.1 Thai rice production and double exposure ...................................................... 175
7.2 Is Thai rice production moving towards resilience? ....................................... 178
Appendix A Socioeconomic variables and proxies for sensitivity and adaptive
capacity ................................................................................................................. 184
Bibliography ................................................................................................................ 188
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LIST OF FIGURES
Figure 1.1: Political and topographic map of Thailand ............................................... 27
Figure 2.1: Vulnerability Scoping Diagram................................................................ 34
Figure 2.2: Vulnerability Scoping Diagram for the indicators of rice farm
household vulnerability ........................................................................................ 43
Figure 2.3: Methods applied to this study ................................................................... 63
Figure 3.1: Eigenvector-based classification framework ............................................. 73
Figure 3.2: Scree plot for the PCA of exposure variables .......................................... 80
Figure 3.3: PCA scree plot of the sensitivity components .......................................... 87
Figure 3.4: PCA scree plot of the adaptive capacity components .............................. 98
Figure 3.5: Final VSD with key vulnerability indicators ............................................ 105
Figure 4.1A: Exposure component 1: Minimum temperature .................................... 110
Figure 4.1B: Exposure component 2: Agro-climate ................................................... 110
Figure 4.1C: Exposure component 3 Maximum temperature ..................................... 111
Figure 4.2A: Sensitivity component 1: Household economy ..................................... 116
Figure 4.2B: Sensitivity component 2: Land scale ..................................................... 116
Figure 4.2C: Sensitivity component 3: Human capital ............................................... 117
Figure 4.2D: Sensitivity component 4: Production capacity ..................................... 117
Figure 4.2E: Sensitivity component 5: Land tenure and security of land
ownership .............................................................................................................. 118
Figure 4.3A: Adaptive capacity component 1: Social capital and social network ..... 121
Figure 4.3B: Adaptive capacity component 2: Financial capacity .............................. 121
Figure 4.3C: Adaptive capacity component 3: Infrastructure and household
mobility ................................................................................................................. 122
Figure 4.4: Location of the four case study provinces ................................................ 125
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Figure 5.1: Maximum temperature compared between CSIRO (left) and MIROC
(right).. .................................................................................................................. 130
Figure 5.2: As in Figure 5.1, but for minimum temperature....................................... 131
Figure 5.3: As in Figure 5.1, but for diurnal temperature range ................................. 132
Figure 5.4: As in Figure 5.1, but for average rainfall ................................................. 133
Figure 5.5: Phenology of KDML 105 (expressed in number of days); sowing
starts in May and transplanting in June ................................................................ 137
Figure 5.6: Simulated yields from EPIC under Scenario 1 and 2 ............................... 141
Figure 5.7: Crop water use efficiency (WUEF) simulated by EPIC........................... 143
Figure 5.8: Evapotranspiration (ET) simulated by EPIC ............................................ 144
Figure 6.1: Simulated yields from EPIC under Scenario 3 during STF (upper) and
LTF (upper) .......................................................................................................... 156
Figure 6.2: Simulated yields from EPIC under Scenario 4 during STF (upper) and
LTF (lower) .......................................................................................................... 157
Figure 6.3: Water use efficiency simulated by EPIC under Scenario 3 for STF
(upper) and LTF (lower). ...................................................................................... 160
Figure 6.4: Water use efficiency simulated by EPIC under Scenario 4 for STF
(upper) and LTF (lower) ....................................................................................... 161
Figure 6.5: Evapotranspiration simulated by EPIC under Scenario 3 for STF
(upper) and LTF (lower) ....................................................................................... 163
Figure 6.6: Evapotranspiration simulated by EPIC under Scenario 4 for STF
(upper) and LTF (lower) ....................................................................................... 164
Figure 7.1: Interactions of global and local/national scales in determining the
resilience of Thai rice production ......................................................................... 180
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LIST OF TABLES
Table 2.1: List of proxy variables for vulnerability indicators .................................... 45
Table 2.2: Description of climate models ................................................................... 65
Table 2.3: Grid points for each study areas ................................................................. 65
Table 3.1: The KMO and Barletts test results for exposure variables ....................... 80
Table 3.2: Three-component solution with temperature and moisture items
identified as important on the exposure dimension of vulnerability .................... 81
Table 3.3: Percentage of variance explained by the three components retained in
the exposure PCA ................................................................................................. 81
Table 3.4: The KMO and Bartletts test results for sensitivity variables .................... 85
Table 3.4: Five-component solution with items identified as important on the
sensitivity dimension of vulnerability .................................................................. 88
Table 3.5: Percentage of variance explained by the five components retained in
the sensitivity PCA ............................................................................................... 89
Table 3.6: The KMO and Bartletts test results for adaptive capacity variables ........ 97
Table 3.7: Three-component solution with items identified as important on the
adaptive capacity dimension of vulnerability ....................................................... 99
Table 3.8: Percentage of variance explained by the three components retained in
the adaptive capacity PCA .................................................................................... 100
Table 5.1: Scenarios established for the four case studies .......................................... 126
Table 5.2: Agronomic and management parameter input data of KDML105 for
Scenarios 1 and 2 .................................................................................................. 138
Table 5.3: Characteristics of selected soils used for Scenarios 1 and 2 ...................... 139
Table 6.1: Four options in adaptive strategies designed for Scenarios 3 and 4 ........... 151
Table 6.2: Summary of results .................................................................................... 169
Table A.1: Socioeconomic variables and proxies for sensitivity and adaptive
capacity ................................................................................................................. 185
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ACKNOWLEDGEMENTS
First of all, I wish to thank the Agricultural Research Development Agency,
Thailand for providing the financial support for my graduate studies. To my family and
friends, I deeply appreciate and would like to thank for their support in helping me
overcome the hard time in my academic life. To the faculties and community of the
Department of Geography, I would like to extend my appreciation to them for creating a
positive academic environment to all of us in the department.
I wish to convey my deep gratitude to the dissertation committee, Drs. William
Easterling, John Kelmelis, and James Shortle for their intellectual support in developing
my research. It has been a valuable experience to be able to work with them. I would like
to extend my gratitude to Dr. Jimmy R. Williams at Blackland, Texas Agrilife Center for
his guidance in creating parameters for EPIC crop model analysis. Without this help, the
analysis would not have been completed. A number of officials from various institutes in
Thailand, such as the Meteorological Department, Land and Development Department,
Office of Agricultural Economic, and National Statistical Office, have provided valuable
data for developing my dataset. Without all of these help, this research would not have
been possible. To them, I would like to express my deep appreciation for their support.
Most of all, I am deeply grateful to my dissertation advisor, Dr. Brent Yarnal for
his intellectual guidance, encouragement, and dedication in building my intellectual and
academic success. His guidance and attitude have made me believe that I could become a
good scholar and it has been an honor to have known and worked with him.
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CHAPTER 1
INTRODUCTION
This research will examine the agricultural impacts of and vulnerabilities to
integrated global climate change and socioeconomic change. More specifically, the
investigation will examine the interacting effects of climate change and socioeconomic
conditions on rice production in Thailand. Agricultural systems are vulnerable systems
due to a high dependency on temperature and precipitation. Variations and long-term
changes in these variables pose challenges to farmers and to a society that relies on the
output of the agricultural system. Although there are recent findings that the CO2
fertilization associated with rising temperature may offset the loss of crop yield by
enhancing crop water use efficiency (e.g. Kimball et al. 2002), severe impacts could
occur if that benefit does not materialize. Individual farmers are inevitably vulnerable to
the negative impacts of climate change, and particular adaptation strategies, such as
adopting new seed varieties, relocating the farm, or installing irrigation systems, are
usually required (Easterling et al. 1993). The adaptation strategies implemented,
however, must cater not only to direct climate manifestations but also to non-climatic
factors, such as socioeconomic change in the agricultural system (Parry et al. 2004). I
will employ the double exposure framework (OBrien et al. 2000) in this study to
assess the joint impact of climate change and socioeconomic change.
The double exposure framework recognizes that socioeconomic change is an on-
going process that can pose a direct or an indirect effect on an agricultural sector through
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economic policy, market price, and crop yield (OBrien et al. 2000). This process can
help mitigate the loss of or exacerbate the impacts on an existing agricultural system in
addition to the impacts from climate change. Therefore, this study assesses the
vulnerability of agricultural production by taking into consideration the processes of
climate change and socioeconomic change rather than focusing on a single process,
which has been the trend in previous research (Bachelet et al. 1992; Matthews et al. 1997;
Adejuwon 2006). Even though agricultural effects are mostly discussed at larger scales,
individual farmers are likely to confront and respond to the impacts resulting from this
double exposure, and they are likely to be the most sensitive group in the agricultural
production system. Therefore, the gains/losses from double exposure at the national level
should not be extrapolated as the gain/loss at a lower level (e.g., an individual farmer)
(Reilly et al. 1994). Hence, in addition to addressing larger-scale relationships, the study
will assess the vulnerability of farmers.
This research uses a case study approach to assess the impacts and vulnerabilities
associated with double exposure in Thailand. Thailand is currently experiencing
economic prosperity and is ranked as a top global exporter of rice. However, the country
faces challenges due to both biophysical and socioeconomic constraints, especially in the
major rice production area of central Thailand. Given these constraints, the long-term
competitive position of the Thai rice economy is uncertain. This research questions
whether Thai rice production can overcome the current and future impacts from double
exposure and remain competitive in the global rice market. What are the ideal
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characteristics and adaptive strategies required to mitigate the negative impacts that may
occur in the future?
The outline and format of this chapter is described by section. Section 1.1
addresses the two major impact factors (climate change and the socioeconomic system)
and their interaction with rice production. Section 1.2 gives some brief information about
agriculture, including specific details about rice production in Thailand. Section 1.3
explains the vulnerability framework that will be used in this research, and presents a
discussion of scale considerations as well. Sections 1.4 and 1.5, respectively, describe the
objectives and the study area Thailand. Section 1.6 notes the scope of the study, and
Section 1.7 concludes the chapter with an overview of the dissertation.
1.1 Impact factors: Climate change, the socioeconomic system, and their interaction
There are increasing numbers of studies focusing on assessing vulnerability to
multiple stressors rather than to a single factor. This research focuses on the interaction of
two processesclimate change and socioeconomic changethat result in positive and
negative impacts on Thai agricultural production. I will first address the basic ideas
behind both factors beginning with climate and moving to the socioeconomic system.
Then I will review the research on agricultural production as an exposure unit influenced
by the interconnection between these two factors. Double exposure will frame the
research idea, the literature review, and the methods used in this research.
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1.1.1 Climate factors
Many regional studies have identified how climate plays a major role in
influencing biophysical sectors and that the impacts are not uniformity distributed. The
change in climate refers to the change in the parameters of the distribution (Kate et al.
1985). As widely recognized, an increase of greenhouse gas (GHG) emissions has
contributed to global climate changes including rising sea levels, elevated temperatures,
higher variability in seasonal rainfall, and changes in the frequency and intensity of
weather- and climate-related natural hazards. The projected changes in climate identified
by the Intergovernmental Panel on Climate Change (IPCC) reflect spatial differences in
magnitude and direction of climate trends for multiple regions of the world.
Based on the Fourth Assessment Report (AR4) of IPCC, atmosphere-ocean
general circulation models (AOGCMs) project an increase in global temperature from
2011-2030 compared to the historical baseline 1961-1990 of about 0.64-0.69 C. Greater
increases in temperature of 1.3-1.8 C are projected for mid-century, 2046-2065 (Meehl
et al. 2007). Different magnitudes of warming are reported for various regions. For
example, most areas of Northern America, Europe, Africa, the Mediterranean, and
continental areas of Australia are expected to be warmer than the global annual mean
temperature. The projected temperatures in South Asia, East Asia, and most areas of
Southeast Asia are similar to the global annual mean temperature (Christensen et al.
2007).
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Projected precipitation changes have different patterns than temperature changes.
Increases in average rainfall are projected for northern Europe, Canada, the northeastern
US, northern Asia, and most areas of Southeast Asia. Decreases in average rainfall are
projected to occur in North Africa, southern Australia, Central America, the southwestern
US, Central Asia, and Central Europe. The Mediterranean and southwestern Australia are
projected to be at high risk from drought conditions (Christensen et al. 2007). On the one
hand, monsoonal precipitation is likely to increase in Asia and the southern part of the
West Africa; on the other hand, decreases are expected in the Sahel, Mexico, and Central
America in association with increasing precipitation over the eastern equatorial Pacific
through changes in the Walker Circulation and local Hadley circulation (Meehl et al.
2007).
Data produced by AOGCMs, however, has coarse resolution and cannot
sufficiently capture the finer resolution needed to assess climate impacts at the regional
scale. Therefore, multiple regional climate models and statistical techniques have been
developed to downscale regional-scale climate variables from the coarse-resolution data
of the AOGCMs (Mearns et al. 2003; Christensen et al. 2007). Nowadays, the climate
information simulated from regional climate models, such as CCSM3, CSIRO-Mk3,
UKMO-HadCM3, and ECHAM5, has been widely used in the study of climate change
impacts (Polsky et al. 2000; Parry et al. 2004). Despite claims that downscaling
techniques have successfully simulated future regional climates (Reilly 2002; AIACC
2006; Christensen et al. 2007), the accuracy of simulated climate variations is still poor
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for some regions, such as Southeast Asia, which requires a finer-scale analysis to capture
its physical diversity (Boer and Faqih 2004). Additionally, models still have a low ability
to represent the ENSO variability crucial to defining accurate interannual monsoonal
rainfall (Christensen et al. 2007). Analysis of the performance of several regional climate
models has also shown significant differences from one climate model to another, thereby
requiring further regional model development (Mearns 2003; Boer and Faqih 2004;
Wang et al. 2005).
Because deficiencies of the models in projecting future regional climates remain,
it is preferable to use multiple regional climate models to cover a range of potential
impacts from future climate changes (Brown and Rosenberg 1999; Reilly 2002). The
climate variables used in this research come from two regional climate models: the
Australian CSIRO model from the Division of Atmospheric Research and the Japanese
MIROC (hires) model from the Center for Climate System Research Institute. Two
climate datasets will establish a climate envelope indicating a range of possible climate
conditions and impact scenarios for the study area.
1.1.2 Socioeconomic factors
Early impacts studies generally considered regional economic effects of climate
change or of economic change (Kates et al. 1998), rather than the interactive process of
simultaneous changes in the climate and economy (OBrien and Leichenko 2000). These
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early studies paid attention to cause-effect relationships between climate and
socioeconomic factors for example, how climate will potentially affect regional
livelihood issues, such as food security, farm income, or market price (Kumar and Parikh
2001). This suggested possible vulnerability of individuals or sectors to future changes in
climate.
However, this view assumes the socioeconomic system is static rather than
dynamic, changing through space and time (Fssel 2007; OBrien et al. 2007). The
assumption of a static socioeconomic system leads to mismatches caused by
extrapolating the societal conditions associated with future climate change from present
societal conditions. This approach therefore overlooks the ability of individuals and
social systems to adjust to the constant changes across a range of spatial and temporal
scales (Fssel 2007). Adaptation strategies responding to such results can also be
misleading (Dockerty et al. 2006). For these reasons, there is a need foCr more
integrative approach that links the two dynamic factors, climate change and economic
globalization (OBrien and Leichenko 2000; Cutter 2003; Dockerty et al. 2006; Fssel
2007). One important way to address impacts based on the interactions between these two
stressors is the double exposure framework of Leichenko and OBrien (2008).
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1.1.3 Interactions between climate and socioeconomic factors: Double exposure
The key idea behind the double exposure framework recognizes that responses
and decision-making of individuals, groups, or societies are influenced by the interactions
of at least two simultaneously operating systems in their case, climate and economics
(OBrien and Leichenko 2000). As suggested above, this framework argues that
traditional impacts research, which considers multiple impact-driven factors in
separation, overlooks the cumulative effects of both climate and economics, which
simultaneously interact with each other (Belliveau et al. 2006). The result of the
interactions can both mitigate the losses of and exacerbate the impacts on an existing
system from climate change alone (OBrien and Leichenko 2000). To date, many
researchers have shown interest in addressing the dynamic role of various factors (e.g.
political, cultural, technological, and economic) integrated with an impact study of the
changes in climate conditions (e.g. OBrien and Leichenko 2000; Belliveau et al. 2006;
Acosta-Michlik 2008). For example, double exposure studies of agriculture generally pay
attention to the linkage between processes of climate and of economic globalization
(Belliveau et al. 2006). Variations and changes in climate can pose a threat to agricultural
production, such as decreasing yield and/or lower yield quality. At the same time,
international, regional, and local market price and policy are also constantly adjusting and
changing in response both to climate and to other influences. Therefore, potential impacts
on agriculture at all scales do not simply derive from climate but also from complex
interactions with economics, market policy, and so on. In the next section, the literature
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review will discuss demand, supply, and resulting prices as key influences on crop
production
1.1.3.1 Demand
Recent research has focused on the transforming role of interacting driving forces
such as population increase, income growth, and prices as major factors that, in addition
to climate factors, influence the changing demand in food crops (Nelson et al. 2009;
Rosegrant et al. 2001). Driven primarily by developing countries, the world population
increasing from 6 billion people in 2009 to about 7.5 billion people in 2020 and to about
9 billion people in 2050 resulting in an increasing absolute demand for cereals (Rosegrant
et al. 2001; FAO 2009a; Nelson et al. 2009). Moreover, the crop demand is also
determined by the changes in dietary preference due to higher incomes in developing
countries that shift grain crop consumption towards high protein food. This shift may
result in higher demand for animal feedstock, leading to the conversion of land from
grain crops for human consumption either to grassland for feedstock or grains for animal
consumption (Rosegrant et al. 2001). Yet, the demand for human grain consumption
remains high because of low-income countries such as Bangladesh, Nepal, Cambodia,
Myanmar, and Philippines (Nguyen 2002; Nelson et al. 2010). As a consequence, the
overall growth rate of grain crop demand continues to increase.
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Price is another indicator that influences the impacts of climate change and
socioeconomic change on an agriculture sector. Based on fundamental economic
principles, changes in supply and demand are related to changes in prices, except for
inelastic commodities such as rice. As rice is an essential food for daily consumption in
many countries, consumers continue to buy rice even when the price increases. The
International Food Policy Research Institute (IFPRI 2010) shows that world food prices
for most agricultural crops including rice will continue to increase by 60% between 2000
and 2050 under a no climate change scenario. Price increases are driven by population
and income growth as well as increased demand for biofuel. Under a climate change
scenario, projected lower grain supplies will increase relative demand and then drive the
price higher than the no climate change scenario by 30% with no CO2 fertilization
effect. Price is a bit lower when CO2 fertilization is accounted for.
1.1.3.2 Supply
Besides climate factors, energy prices, urbanization, agricultural investment, and
technology and government trade policies are key factors that affect agricultural output
on the supply side (Lambin et al. 2001; Rosegrant 2001; Von Braun 2008; Thongrattana
2009). Energy prices are fundamental determinants of food crop production and prices.
High energy prices affect agricultural production by directly increasing the costs of
operating machinery and using fuel-based inputs, such as fertilizers, pesticides, irrigation,
and transport (Braun 2008). The intensive use of fuel-based inputs means a significant
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increase in production cost and decrease in farm income. This economic constraint on
Thai farmers has been reported to reduce the adaptive capacity of farmers by inhibiting
them from adopting farm management techniques that increase yields (Isvilanonda and
Hossain 2000; Mitin 2009). However, in 2007, despite the rising costs of energy and
fertilizers, rice crops in Thailand used over 262 thousand tons of nitrogen fertilizer,
which resulted in the increase in rice production costs up to 50% (Krisner 2008).
Nonetheless, because of high demand and consequent high prices from the international
market, the production of Thai rice remained high despite the very high cost of
production (USDA 2007).
Urbanization associated with emerging economic development places demands on
essential agricultural resources. With economic growth in Thailand, population has
concentrated in cities and metropolitan areas in the nations Central Plain and has
extended into the southern North region. These areas are also major cultivation zones for
Thai rice, making up over 80% of the nations total rice production land area
(Kupkanchanakul 2000). This urban growth reduces the available crop area and
agricultural employment through competition between urban and farm work and attrition
of farm workers from lost land. Urban growth simultaneously increases the competition
for water among household and commercial consumption, electric generation, and crop
production (Shivakoi et al. 2008). Meanwhile, the demand for rice continues to increase
despite increasingly limited resources. Thus, Thai rice production faces the twin
production challenges of shrinking supply of available cropland and water for irrigation.
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These declines cause a reduction in the food supply and consequently lead to higher food
prices (Lambin et al. 2001; Shivakoti et al. 2008).
The increased demand for biofuel feedstock has contributed to a rise in food
prices and further constrained food supplies. With high concerns over surges in oil prices,
energy security, and climate change, many experts think that the transition from fossil
fuels to biofuels promises to buffer price shocks, improve energy security, and reduce
carbon emissions. However, an increase in demand for biofuel feedstock reduces supplies
of cereals because farmers naturally convert their land to more profitable crops. Low
supplies contribute to rapid price increases for rice and other cereal crops (Rosegrant
2001; Nelson et al. 2009).
With or without climate change and even with the limitations to production noted
above, agricultural research and technology is expected to increase productivity and meet
the continually growing demand for food (Phelinas 2001; Shivakoti et al. 2005; Nelson et
al. 2009). To make sure that these constraints do not overwhelm the agricultural systems
ability to meet this demand, it is crucial for the government and its policy makers to give
priority to investments in rice production technologies such as new high-yield varieties
that meet customer taste and market demand, demand less water, require less intense
inputs, and suit local biophysical conditions.
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1.2 Agriculture in Thailand
1.2.1 Overview
Agriculture has continuously played an important role in Thailands economy and
society by providing food, commodities, and employment. Despite the significant
decrease of its contribution to Thailands Gross Domestic Product from 25% of GDP
in the mid 1980s to 12.3% of GDP in 2009 (CIA 2010) because of the rise of
industrialization and urbanization in the twentieth century agriculture is still the
largest sector of the Thai economy. The country is a leading exporter of crops such as
rice, corn, soybeans, sugarcane, tapioca, and rubber (USDA 2010). Approximately 50%
of the labor force is employed in the agricultural sector (GAIN 2010).
More than half of the cultivated area in Thailand is used for rice production.
Approximately 70 million ha or 53% of the total cultivated area was used for this purpose
in 2007 (OAE 2010). Geographically, rice can be grown under a wide range of
biophysical and climatic conditions from deep water (>80 cm height of water), to lowland
(50-100 cm height of water), to upland (
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grown only during the rainy season, but with irrigation farmers can grow rice two or three
times a year. Irrigated rice is produced mainly in the Central Plain (Shivakoti et al. 2005).
Similar to other Asian countries, the advent of high-yielding varieties (HYV) of
rice during the Green Revolution has significantly improved the quantity of rice
production; nonetheless, the benefit of these varieties is uneven. The adoption of HYV in
parallel with the construction of irrigation systems allowed farmers to grow multiple
crops and increased the productivity of rice (Ishii 1998; Molle and Keawkulaya 1998).
However, HYV have been criticized for their intensive production inputs, such as
fertilizers and pesticides, their susceptibility to local pests and diseases, their unsuitability
for rainfed areas, and their low quality, which has generated low market prices (Ishii,
1975; Molle and Keawkulaya 1998; Phelinas 2001). Therefore, HYV for Thailand have
had only minimal impact and limited growth in some areas (Molle and Keawkulaya
1998; Phelinas 2001). Moreover, the poor taste of HYV is not favored in the international
market, and production costs of HYV are higher compared to other rice varieties. Under
these circumstances, the Thai rice economy has focused on high-quality aromatic rice,
which receives higher price yet produces lower yields than HYV. Thailands agricultural
sector has decided to assert its comparative advantage in international trade by focusing
on the quality-based rice market rather than the quantity-based market (Phelinas 2001).
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1.2.2 Climate and socioeconomic impacts
Rice production in Thailand has faced some constraints due to climatic and
socioeconomic factors. Even though the country is located in the tropics, rice production
is affected by variations in rainfall frequency, total rainfall during the growing period,
flooding, and mid-season dry spells (Bachelet 1992; Chinvanno et al. 2008). The direct
and indirect impacts from the changes in climate variation have been reported as the
major concerns on the current rain-fed rice production in Thailand. For example,
biophysical impacts (e.g. soil physical changes or flooding) are classified as the first-
order impacts from climate events. The consequences of the biophysical impacts in the
forms of damages to immature plants and reduction and losses in harvested yields are
classified as the second-order impacts. The human well-beings (e.g. household income,
financial and wealth, migration of household members, and labor force, etc.) are
classified in the higher-order impacts (see Chinvanno et al. 2008). In addition to the
current climate, previous climate change studies analyzing rice suggested that the
projected temperature increase may reduce yields in the region (Buddhaboon et al. 2008;
Felkner et al. 2009) and may shift the potential production areas towards the upper
central region of Thailand (Buddhaboon et al. 2008).
Recent research shows that there are increasing challenges to Thai rice production
because climate impacts occur in parallel with the expansion of urbanization,
industrialization, and population growth, all of which take land, water, and labor from the
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agricultural sector (Kupkanchanakul 2000; Phelinas 2001). The expansion of urban areas
results in the competition for water among various sectors, and this competition continues
to increase across the nations regions. Historically, water extracted from northern
Thailand was diverted for domestic and agricultural use in the Central Plain (Ishii 1975).
In recent years, however, this flow has decreased significantly because a larger share has
gone to the upper and lower basins (Shivakoti et al. 2005). In addition to water resources,
labor shortages attributable to the diversion of the labor force from agricultural to
industrial sectors have constrained agricultural production. Agricultural sector demand
for wage labor exceeds the availability of the local supply (Ishii 1975; Johnson 1981;
Phelinas 2001).
The scarcity of production resources has increased the cost of rice production.
Due to an intensive demand for production inputs, deprivation of land, competition for
water, and scarcity of wage labor, the cost of production has increased and income is
expected to fluctuate. In a world market, developing countries usually set a low crop
price to compete with opponents (Shivakoti et al. 2005). As a result, profits are marginal
and farmers who depend primarily on the income from rice are highly sensitive to the
changes in market price. However, farmers who have enough capital have more options
and may decide to switch to other cash crops that are more profitable.
Moreover, economic globalization has resulted in more intensive use of fertilizers,
agricultural chemicals, irrigated water, and labor in order to increase rice yields to meet
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the demands of the market. Additionally, in those areas that adopted HYV rice, Thailand
transitioned from a single-crop agricultural system to multiple cropping, which extracts
massive amounts of water from natural sources as well as irrigation (Shivakoti et al.
2005).
Thus, rice production in Thailand faces substantial challenges because of various
factors. Despite limited natural resources and declining arable land, labor, and water
supplies, Thailand needs to increase yields and lower the costs of production while
maintaining the grain quality widely expected in the world market. More important, tastes
have changed and demand more high-quality rice. Thailand must maintain the ability to
respond to increases in demand (Ishii 1975; Shivakoti et al. 2005). Moreover, in the
future, it is likely that there will be significant changes in agricultural practices, rural
society, the national economy, and the relationship between government and individual
economic sectors. Mechanisms are needed to improve the flexibility and capacity
required to deal with these stresses (Shivokoti et al. 2005). It is important to note that
agricultural development plans and strategies to address the stresses typically operate at
the national scale, but the impacts function at the local, household, and individual scales.
Currently there is varying ability and resources to deal with stress among farmers.
Farmers appear to be the first group affected by the negative impacts of climate and from
the changes of market policy, price, national or international demand, or limited access to
necessary production resources (Kupkanchanakul 2000; Parry et al. 2004). Some places
or persons will gain or lose depending on their capacity to cope with future changes.
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Recent research has assessed impacts and vulnerabilities of rice production in order to
find agricultural and economic strategies that consider stakeholders at multiple scales,
including farmers at local scales.
1.3 Research Framework: Vulnerability and Scale
The concept of vulnerability has been used in a variety of research contexts to
refer to the degree to which a system is likely to be harmed by climate and other stresses.
The three major dimensions of vulnerability are exposure, sensitivity, and adaptive
capacity (Polsky et al. 2007). Exposure refers to the stresses caused by changes in
frequency, intensity and the nature of climate and non-climate stresses. Sensitivity refers
to the degree to which an individual or group (as the system of interest) is affected by
exposure to climate and other stresses. The ability of the system to respond to the
exposures and the effects in order to adjust to and cope with the impacts is referred to as
adaptive capacity (Kelly and Adger 2000; Fssel and Klein 2006).
The vulnerability framework in previous research recognizes the roles of both
climate and non-climate exposures and stressors that contribute to the vulnerability of a
system (Cutter 1996; Kelly and Adger 2000). Vulnerability research, especially in studies
of climate change, traditionally focused on climate variability and climate-related
exposures such as sea-level rise, flood, drought, and extreme events (e.g., Adger and
Kelly 1999; Rygel et al. 2006). The expansion towards social dimensions has been
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featured in more recent literature. Social exposures include economic, policy-making,
social, environmental, technological, and other socioeconomic factors. (Moser 2010).
However, climate and social exposures have generally been considered functioning
independently of each other. This separately functioning, factor-driven vulnerability
research has been criticized for overlooking the real-world context in that vulnerability is
driven by the interaction of exposures rather than a single factor. Vulnerability studies
should consider this interaction among multiple stressors as a dynamic rather than a static
process (Adger and Kelly 1999; Turner et al. 2003; OBrien et al. 2004; Belliveau et al.
2006). One of the studies that emphasized the interacting processes between climate and
socioeconomic exposures was the double exposure framework of OBrien and Leichenko
(2000) introduced earlier.
The challenges underlying the examination of two global processes in multiple
exposures research rest on two major concerns the scale used in the analysis and the
definition and framework of vulnerability (Wilbanks and Kates 1999; Fssel and Klein
2006; Eriksen and Kelly 2007). Theoretically, scale matters in the study of global change,
because (1) the changes and consequences of the interaction across scales are
complicated to predict and understand, and (2) the interpretations of the results and the
impacts can mean something different at global and local levels (Wilbanks and Kates
1999; Kelly and Adger 2000; Wilbanks 2006).
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Wilbanks (2006) argues that there are three reasons for focusing on the local,
detailed scale. First, complex interactions of key processes e.g., environmental,
economic, and social processes moving across time and areal extents and underlying
environmental systems are too complex to unravel at any scale beyond the local. This
perspective is supported by the work of Cutter (1996), Kasperson et al. (1995), Easterling
(1997), Wilbanks and Kates (1999), and Carlo and Tol (2002). The second reason is that
observed processes at a detailed scale contain more variance than observed processes at a
general scale, and the greater variety of observed processes and relationships at a local
scale can provide important knowledge about the substantive questions being asked
(Wilbanks 2006). Third, looking at a particular issue top-down can lead to significantly
different conclusions from researchers looking at that very same issue bottom-up
(Kasperson et al. 1995; Wilbanks 2006). ). However, research should consider the
importance of the linkages between different scales and the research questions being
asked (Easterling 1997; Wilbanks 2006).
This research will consider the three arguments presented by Wilbanks and,
although it will focus mainly on the local scale, it will also take into consideration the
linkages that exist between local and regional conditions. The research will also examine
the regional-to-national linkages that may influence rice production. Research conducted
by Easterling (1997) supports the approach taken in this study and its focus on the local
scale with linkages to regional scale. For example, the knowledge of dynamic processes
embedded in integrated regional assessment is often derived from the understanding
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gained from location-specific field studies (Easterling 1997; Carlo and Tol 2002). In
another example, the efficacy of adaptation varies from place to place. In the short term,
flexibility in the use of agricultural practices and in the capital investment of individual
farmers and regional marketing systems influences adaptability. In the long term,
however, regional differences in rates of depreciation of capital investment are also
influential (Easterling 1997). Finally, socioeconomic and environmental data sets are
most likely to match best at relatively small spatial scales (Lonergan and Prudham 1994).
Although national and international linkages are important, the understanding of
the processes most often comes from in situ and regional experimentation (Easterling
1997; Carlo and Tol 2002). If a detailed global-scale approach were taken in this study,
the robustness of individual localities would tend to be overestimated because of the lack
of sensitivity to local obstacles and constraints (Wilbanks 2006). The spatial variability of
climate change would also be obscured. Given the fact that marginal systems are best
studied at the local to regional scales (Easterling 1997), such as would be the case with
Thailand, there is justification for the scalar decision taken in this study.
There is a need to clarify the terminology and conceptual framework used in
vulnerability studies because vulnerability means different things to different scholars.
According to Fssel (2007), OBrien et al. (2007) and Nelson et al. (2010a), the
terminology describes the dimensions of vulnerability, while the conceptual framework
defines the methodological approach in assessing the vulnerability. Empirical evidence
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for hazard assessment and exposure is generally confined to floods, droughts, storms, or
other extreme events. For agricultural vulnerability assessment, exposure often refers to
changes in climate variability, such as temperature and rainfall variations, which
influences crop biophysical sensitivity, annual yield, agricultural land use changes, and
food security (Berry et al. 2006; Nelson et al. 2010b). Agricultural socioeconomic
research can focus on the sensitivity and adaptive capacity of market mechanisms,
international trade and policy, or the well-being of society.
Apart from the above discussion, vulnerability has been viewed in two other ways
end-point and starting-point vulnerability. The end-point vulnerability approach views
climate change as the root problem and initiates the analysis with attempts to establish the
future climate impacts and the potential adaptation options. In contrast, the starting-point
perspective considers social vulnerability the root problem and focuses on uncovering
current social vulnerability to climate before suggesting the effective adaptation options
(Fssel and Klein 2006; Eriksen and Kelly 2007; Fssel 2007).
According to OBrien et al. (2007), conceptual frameworks can be classified into
two major groups contextual (qualitative) vulnerability assessments and outcome
(quantitative) vulnerability assessments. The differences relate to the choice of
appropriate methodological designs. Outcome vulnerability, which is similar to the end-
point approach, focuses on the linear relationship between exposure and the projected
impacts of climate change on a specific exposure unit. Outcome vulnerability then
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suggests adaptation options to reduce or limit the negative outcomes. Most of the
research studies in this category use impact models (e.g., crop, hydrologic, or economic
models) as analytical tools.
The contextual vulnerability model, which is similar to the starting-point
approach, considers processes of climate-society interactions as a robust exposure factor
that influences vulnerability. Important connotations of contextual vulnerability are: (1)
impacts are unevenly distributed over the exposure unit; (2) the exposure unit has
differential ability to respond to, adapt to and recover from the impacts it will experience;
and (3) the existing vulnerability of the exposure unit will also influence its capacity to
cope with future impacts (Wilbanks and Kates 1999; Kelly and Adger 2000; Eriksen and
Kelly 2007). Hence, identifying key indicators of existing vulnerability will enhance the
ability of investigators to understand the nature and characteristics of future vulnerability
(see Eriksen and Kelly 2007).
The literature agrees that failure to outline a clear definition and conceptual
framework in vulnerability assessment studies will result in a common methodological
fallacy, as described by Nelson et al. (2010a). This fallacy results from the
overwhelming use of biophysical or macroeconomic models in assessing and predicting
impacts over the starting-point research approach, which results in the drivers that cause
the vulnerability to be overlooked (OBrien et al. 2007; Nelson et al. 2010a). Moreover,
there is a need for any future vulnerability research to integrate both quantitative and
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qualitative analyses to develop insights and results that are meaningful to users (Cutter
2003; Moser 2010; Nelson et al. 2010a). Both, the quantitative and qualitative approach
used in this study will be discussed in subsequent sections.
1.4 Research Goal, Questions, and Objectives
Emerging from the above, the goal of the study is to understand the spatially
distributed impacts and vulnerabilities of local rice production in Thailand resulting from
the double exposure to climate change and socioeconomic change. To reach this goal, the
research seeks to answer three questions and strategic objectives.
1.4.1 Research questions
1) Who will be vulnerable to double exposure and what are key indicators of
vulnerability of Thai rice production?
2) What are the key characteristics of places and agricultural practices that might
reduce the vulnerability of rice production to double exposure?
3) What are the consequences on rice production resulting from double exposure to
climate and socioeconomic change?
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1.4.2 Research objectives
1) To identify the important climatic and socioeconomic indicators associated with
the vulnerability of Thai rice production, including the dynamic interactions
between climatic and socioeconomic indicators
2) To isolate the most influential indicators and distinguish the four least
vulnerable provinces
3) To model the range of sensitivities of rice crop yields to varying climatic and
socioeconomic scenarios
1.5 Study Area
Thailand is a Southeast Asian tropical country covering approximately 51 million
hectares. It shares borders with Myanmar, Laos, Cambodia, and Malaysia. The country
extends from 5 to 40 north latitude and 97 to 106 east longitude (Figure 1.1). The
monsoon dominates temperature and precipitation over Thailand, with a dry season
associated with the Northeast Monsoon and a wet season associated with the Southwest
Monsoon. From the beginning of November to February, except for the southernmost
portions of the country, the Northeast Monsoon brings cool and dry air from the Siberian
anticyclone to Thailand. The Southwest Monsoon, the main source of precipitation in
Thailand, brings humidity from the Indian Ocean for a rainy season that lasts from May
to October (Ratanopad and Kainz 2006; Chinvanno et al. 2008). The average annual
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rainfall in most areas ranges from 1100-1500 mm, although rainfall totals up to 4,500 mm
are found along the southeastern coast and in peninsular Thailand. Average temperature
in Thailand varies from 24.429.3 C (7685 F).
Climate characteristics in Thailand fall into three major Kppen classification
groups: Aw, Am, and Cw. Despite overall dominance by the monsoon, the majority of
Thailand has a Tropical Savanna (Aw) climate, with the exception of the southeastern
coast and southern peninsular provinces where Tropical Monsoon (Am) predominate.
The northern mountainous area is categorized as Humid Subtropical (Cw).
Thailand has three main seasons. A rainy season from May to October during
this period the Southwest Monsoon brings a stream of warm moist air from the Indian
Ocean causing abundant rainfall. About 90 percent of the annual rainfall occurs during
this season. A cool dry season occurs from November to February, and warm weather
and variable wind is present in March and April. The warmest and coolest months during
the year are April and January, respectively (Attanandana and Kunaporn 2005).
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Thailand is divided politically into 76 provinces situated in six physiographic
regions northern, central, northeastern, western, southern, and eastern regions divided
by attributions of Thailands physical setting. Only the northern, central, and northeastern
regions (hereafter referred to as the North, Central Plain, and Northeast) are considered as
potentially suitable rice-producing environments (Buddhaboon et al. 2008); these three
regions and their 62 provinces will be the focus of the first phase in this study.
The North is characterized by high mountains with steep river valleys and upland
areas that border the Central Plain. Some upland rice is grown in the high areas and at the
Figure 1.1: Political and topographic map of Thailand (source: wikipedida.org)
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lower slopes of the high hills. Lowland rice is grown mainly in the lower valleys in which
water is available. About 22% of Thailands rice area is in the North, which account for
approximately 25% of total rice production. Major rivers in the North, the Ping, Wang,
Yom, and Nan, flow and unite to form the Chao Phraya River and tributary network in
the lowland Central Plain; all rivers drain to the Gulf of Thailand (Shivakoti et al. 2005).
In the Central Plain, the Chao Phraya drainage system occupies about one-third of
the nations territory. This region, known as the rice bowl, contains fertile soil suitable
for paddy rice cultivation. Central Plain wet-season rice occupies about 21% of the
countrys total cultivated rice area and produces 30% of total rice. About one fourth
(450,000 ha) of that cultivated land has irrigated dry-season rice (OAE 2010). Because
the Bangkok Metropolitan Area is situated on the southern portion of the Central Plain,
this region is a national hub for trade, transport and industrial activity, as well as for
major irrigation development projects (Ishii 1975; Shivakoti et al. 2005).
The Northeast consists mainly of the dry Khorat Plateau where some parts are
extremely flat with a few low, rugged, and rocky hills. The Phetchabun, Sankambeng,
and Dong Phaya Yen mountains separate the Northeast from the rest of Thailand. The
Mekong River delineates much of the northern and eastern rim and drains into the South
China Sea. The Northeast is known for its infertile soil with high salinity and poor
drainage and its tendency for drought due to a long dry season; both of these factors do
not favor agricultural activities. However, rice cultivation is possible as the short
monsoon season brings enough rainfall to harvest two-crop cycles per year. Rice
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occupies 80% of the regions arable land and about 53% of Thailands rice-producing
land is in the Northeast, but the region only accounts for 41% of the nations total rice
production (OAE 2010). The Northeast produces mostly rainfed rice. Rice farmers in this
region are always confronted with the risk of uncertain production due to floods in the
rainy season and water shortages in the dry season (Ishii 1975; Shivakoti et al. 2005).
There is evidence showing that climate change and socioeconomic change will
significantly affect rice production in Thailand. Climate change could influence the
monsoon and subsequently alter the intensity of both temperature and precipitation in
various areas (Kripalani et al. 1995; Mitchell and Hulme 1999; IPCC 2007). Projections
for Thailand show a significant increase in extreme climate events that could occur in the
form of high temperatures, heavy rainfall, and flooding (Chinvanno et al. 2008; Cruz et
al. 2007). The scarcities of land, labor, water, etc. mentioned earlier are the major
challenges resulting from socioeconomic change. Research from many disciplines is
needed to determine how Thailand could increase yields to meet the future demands
while maintaining high grain quality, increasing labor productivity per land area,
increasing farmers incomes, and developing the water-saving and related technologies
that could overcome climatic disturbances (Shivakoti et al. 2005; Bouman et al. 2007)
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1.6 Scope of the Study
This study will not analyze adaptation strategies because adaptation analysis is
complicated and when combined with the analyses used here would require much
more time than is available for this research. Nonetheless, some adaptation possibilities
will be suggested in this study through the critical adaptive capacities developed for Thai
rice production to cope with future impacts of climatic and socioeconomic changes.
These adaptive capacities are important for developing meaningful adaptation strategies,
policies, and fundamental understanding of place-specific agro-climatic problems.
The study will focus primarily on analysis at the local scale, although the
interactions of climatic and socioeconomic factors that influence Thai rice production
involve four different scales (local, regional, national, and international). As mentioned in
the previous section, the interactions of climatic and socioeconomic factors, especially in
the agricultural context, are too complex to unravel, and the processes and patterns of
relationship may not be well observed beyond the local scale. Moreover, in the
agricultural context, crop producers are usually the first group that experiences or suffers
from climatic and socioeconomic impacts; the local-scale focus will reveal important
information that points to place-specific conditions. However, the linkages existing
between scales (local-regional, and regional-national) will be recognized in the study to
suggest more meaningful and realistic adaptation possibilities.
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1.7 Thesis Overview
The remainder of this disseration will be structured as follows. Chapter 2
provides the methodology and data for this study and details the Sequential Exploratory
Strategy, a mix-method approach with three phases of analysis. This chapter also
highlights the first phase of analysis, which uses a Vulnerability Scoping Diagram (VSD)
to structure the proxy data. Chapter 3 provides the results of the second phase analysis. In
this phase, I conduct a Principal Component Analysis (PCA) to distinguish the variables
that contribute to the three dimensions of vulnerability: exposure, sensitivity, and
adaptive capacity. Chapter 4 extends the results of the second phase by presenting
vulnerability maps and using them to explore the vulnerability patterns of individual Thai
provinces. Chapter 5 shows results of the third phase of analysis, which uses the EPIC
crop model to explore the impacts of future and projected climate change on Thai rice
production without adaptation, whereas Chapter 6 discusses the plausible impacts of
climate change with adaptation. Chapter 7 discusses the findings of the three-phase
analysis and also draws conclusions on the potential resilience of future Thai rice
production.
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CHAPTER 2
METHODS
This research adopted a Sequential Exploratory Strategy type of mix-method
approach (Creswell 2009). In a typical Sequential Exploratory Strategy, the research
implements two phases of analysis. The first phase employs a qualitative framework to
explore and inform the selection of data and the second phase uses a quantitative
framework to analyze the selected data. This research differed in that there were three
phases of analysis: an exploratory qualitative analysis, an exploratory quantitative
analysis based on the qualitative analysis, and a quantitative modeling study based on the
exploratory quantitative analysis.
Specifically, the exploratory qualitative analysis structured the proxy data that
represented climatic and socioeconomic-related indicators influencing Thai rice
production. The result of this first phase of research generated the input that allowed the
measuring and comparing of vulnerability components among production areas in
Thailand in the second phase. The second phase employed a Principal Components
Analysis (PCA) to identify key vulnerability indicators, distinguish four provinces (the
case study areas) that are likely to succeed in the face of an evolving climatic and
socioeconomic system, and demonstrate how rice production might be affected by future
projected climate conditions. The four case study areas distinguished by the PCA formed
the basis of the third phase of analysis: crop modeling.
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The following sections provide details of each phase of analysis. Phases 1 and 2
encompass the qualitative analysis and PCA, respectively, and Phase 3 covers the EPIC
crop model used in this dissertation.
2.1 Phase 1: The Vulnerability Scoping Diagram
The first phase of analysis employed a Vulnerability Scoping Diagram (VSD) to
develop a social vulnerability profile for rice production. Polsky et al. (2007) designed
the VSD with three rings circling around a bullseye (Figure 2.1). The bullseye represents
the concept of vulnerability. The first and nearest ring represents the three dimensions of
vulnerability discussed earlier in this dissertation: exposure, sensitivity, and adaptive
capacity. The middle ring represents the components of these three dimensions. Finally,
the outer ring represents the measurements of the components. The VSD offers two major
functions for a vulnerability assessment, providing a starting point for researchers to
understand the details of vulnerability, and facilitating the comparisons of vulnerability
indicators at different places and times.
Adopting the VSD also facilitated the processes of data collection, the
development of conceptual frameworks, and the evolution of a methodological
framework that suited the collected data. For instance, Pearsall (2009) adopted the VSD
to investigate vulnerabilities of residents and communities to multiple stressesthe
consequences of environmental mitigation projects and regional hazardsat four study
areas in New York City. The study showed that the VSD could practically monitor
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vulnerabilities to multiple stressors and provide better understanding of the linkages
among vulnerability dimensions in a complex human-environmental system.
Figure 2.1: Vulnerability Scoping Diagram (source: Polsky et al. 2007)
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2.1.1.1 Physical vulnerability
Indicator 1: Climate variables (temperature and precipitation)
Climate variables are mostly defined as the main factor affecting production
yields and cultivated area (as the first-order impacts) and socioeconomic activities and
well-being (as the second or higher-order impacts) of farm households (Kates et al. 1985;
Parry et al. 1985; Dabi et al. 2008). Some research pays particular attention to rainfall
variations because rice is often cultivated under rainfed conditions. Deviations of rainfall
distribution from normal could change yields from the expected and consequently affect
farm incomes, benefits, practices, and so on. Two types of climate events droughts and
floods are the major concerns of farmers in most developing countries (Dabi et al.
2008). For example, the occurrence of prolonged dry spells during mid-season after
sowing or transplanting rice could delay farm schedules and impose additional costs on
farmers. Flooding that coincides with harvest could cause severe damage to produce at a
time when replanting may be too late. Therefore, farmers who depend on rainfed
cultivation are vulnerable (Chinvanno et al. 2008). Temperature stress, especially during
the growing season, potentially affects crop growth and functioning. Specifically,
temperature stresses can affect crop physiological process by decreasing dry matter
accumulation, influencing productive tillers, reducing grain weight, and increasing floret
sterility (Manju et al. 2010). As a result, crop yield and quality are lower than expected.
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2.1.2.1 Socioeconomic vulnerability
2.1.2.1.1 Human capital
Indicator 2: Education. The educational attainment of household members can reflect
their vulnerability to climate and economic stresses in two ways. According to Adejuwon
(2008) and Dabi et al. (2008), the households investment in higher education, on the one
hand, can result in good health, labor productivity, and the agricultural information
accessibility because educated household members can understand and participate in the
technological and administrative processes in the modern economy better than members
with little or no formal education.
On the other hand, household members receiving high levels of education
characterize high mobility and flexibility. Huffman (2001) points out that farm household
members who receive higher education often choose off-farm employment because of
higher wage incomes and the perception of less physical work compared to farm work,
which can lead to permanent migration from the farm (Huffman 2001). Although the loss
of labor can hurt the household, the absence of household members due to the off-farm
employment does not necessarily indicate high household vulnerability. Instead,
remittance of wage income from off-farm employment helps secure and diversify
household income. As a result, this alternative source of household incomes helps
decrease reliance on farm production and income driven by the climate variation
(Huffmann 2001; Phelinas 2001; Dabi et al. 2008).
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Indicator 3: Farm labor. Urbanization and industrialization can cause a shift of farm
labor from rural areas to urban areas for employment or educational opportunities. The
average number of farm household members in Thailand is approximately five people per
household, which is sufficient to supply family laborers for a farm of less than 50 ha.
During the peak season, developing country farmers often complement the absent
household members with the hired laborers to achieve production goals (Morgan and
Munton 1971; Phelinas 2001). However, small farm households may experience hardship
if farm outputs do not generate enough income to meet the labor costs per unit of land
(Morgan and Munton 1971).
2.1.2.1.2 Financial capital
Indicator 4: Household incomes and income sources. Farm household incomes and
income sources can serve as measures of the vulnerability of their production. Dabi et al.
(2008) suggests that households with low incomes, savings, and saleable assets are
generally vulnerable to stresses from climate variability and socioeconomic changes.
With low financial status, the ability of farmers to invest in farm improvements, e.g., the
purchase of farm inputs, farm equipment, and other farm technology, is limited.
Furthermore, low financial status reduces the capacity and ability of farm households
because poor households are likely to focus on the survival and well being of household
members rather than the improvement of farm quality or the production system, which
would decrease their vulnerability to future climate and socioeconomic stresses (Osman-
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Elasha and Sanjak 2008). Low-income household vulnerability is worst when it relies
exclusively on agricultural production for income and food source (Morgan and Munton
1971).
Indicator 5: Size of farm operation. The size of farm operation (i.e., farmland plus farm
equipment) can measure farm household vulnerability. Eakin et al. (2008) suggest that
both large and small landholdings are sensitive to the variety of climate events; however,
the overall social vulnerability of small landholdings is higher. Large landholdings
represent higher wealth and financial status of the households; they can invest more on
farm production and generate greater yields and incomes than smaller landholdings. With
more access to physical and material resources, large landholdings have greater flexibility
and more stable financial status, which in turn increase their capacity to cope with a
changing economy and environment. Similar to farm size, the size and ownership of
animal units and tractors also indicate the production scale and financial capital of farm
households (Huffman 2001).
Indicator 6: Land ownership and tenure security. Land ownership and land tenure can
determine the ability of farm households to generate food, income, and social and
financial status. According to Deininger and Feder (2001), with land ownership and
tenure, farmers gain the opportunity to obtain financial credits and loans from banks to
invest in the farm. It is the opposite for households that lack ownership and tenure:
farmers have less access to formal banks and rely on non-formal financial institute or do
without investment. With less accessibility to funds, farmers tend to use fewer farm
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inputs such as fertilizer, pesticide, and insecticide, which may result in relatively lower
yields. Farmers without ownership or tenure are less likely to find room in their tight
budgets to improve the land (Deininger and Feder 2001).
2.1.2.1.3 Social capital
Indicator 7: Governmental support. Social capital and social networks within the
community also determine the vulnerability of farm households. Government support in
the form of production policies and agricultural extension services could reduce this
sensitivity and increase the adaptive capacity of the farm households. For example,
extension units could support adaptive capacity by introducing new farm strategies and
developing necessary skills and knowledge to overcome climate stresses (Dabi et al.
2008). Supporting strategies and policies from government, such as research and
development, education, infrastructure and facilities, and information, could also help
increase adaptive capacity of farm households. Chavas (2001) found that government
policies promoting the use of crop price insurance, farm subsidies, production contracts,
disaster payments help reduce the adverse effects from decreases in crop price and
uncertainties in crop production due to climate and socioeconomic stresses. Farm
households participating in such government programs are more likely to be buffered
against production risks.
Indicator 8: Market channel. Market institutions and their structures within the local area
indicate the strengths and weaknesses of the domestic farm production system. According
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to Beininger (2001) and Barrett and Mutambatsere (2005), agricultural markets provide
fundamental functions for agricultural input and output distribution, and post-harvest
processing and storage. Basically, farmers purchase farm inputs (e.g., fertilizer, seed, and
machinery), sell their products, and earn incomes back from the agricultural market.
However, the efficiencies of market institutions, physical infrastructure, trading
competition efficiency, and market accessibility to farmers in each local area are unequal,
particularly in developing countries. Agricultural communities with poor communication
and poor transportation systems are less flexible and more sensitive to constraints from
climatic and social stressors. Nonetheless, the formation of local markets and communal
marketing in the form of credit unions, farmer cooperatives, and wholesale-level
cooperatives increases the capabilities of local farmers by facilitating bulk input
procurement, negotiating price, and sharing transportation costs. The cooperation of
farmers also increases their competitiveness and negotiating power relative to
commercial markets.
2.1.2.1.4 Physical capital
Indicator 9: Basic infrastructure and services. The availability and accessibility of basic
production resources can determine the coping capacity of farm households. For rice
cultivation, deep wells, effective water pumps, or well-developed irrigation systems are
vital because these resources can provide water for agricultural and household use when
water becomes scarce during dry periods. In addition, basic infrastructure and facilities
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such as road, electricity, and telephone located within the accessible distance can improve
and smooth the production process. For example, a well-conditioned road and short
distance between the farm and the market place could reduce delivery time, which could
also minimize yield-quality losses. Good roads also enable large pieces of farm
equipment such as tractors and trucks to move from field to field with ease. Similarly,