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

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

iii

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.

v

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

vii

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

viii

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

ix

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

x

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

xi

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.

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

2

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

3

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.

4

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).

5

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

6

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

7

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).

8

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

9

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.

10

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

11

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.

12

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.

13

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 (

14

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).

15

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

16

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

17

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

19

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

21

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

23

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

26

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).

27

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)

28

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

29

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.

33

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

34

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)

35

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.

36

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).

37

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-

38

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,