The Pennsylvania State University

The Graduate School

Department of Geography

CLIMATE CHANGE IMPACTS ON CASSAVA PRODUCTION

IN NORTHEASTERN THAILAND

A Thesis in

Geography

by

Ratchanok Sangpenchan

 2009 Ratchanok Sangpenchan

Submitted in Partial Fulfillment of the Requirements for the Degree of

Master of Science

August 2009 ii The thesis of Ratchanok Sangpenchan was reviewed and approved* by the following:

Amy Glasmeier Professor of Geography Thesis Advisor

William E. Easterling Professor of Geography

Karl Zimmerer Head of the Department of Geography

*Signatures are on file in the Graduate School

iii ABSTRACT

Analyses conducted by the Intergovernmental Panel on Climate Change (2007) suggest that some regions of Southeast Asia will begin to experience warmer temperatures due to elevated CO2 concentrations. Since the projected change is expected to affect the agricultural sector, especially in the tropical climate zones, it is important to examine possible changes in crop yields and their bio-physiological responses to future climate conditions in these areas. This study employed a climate impact assessment to evaluate potential cassava root crop production in marginal areas of Northeast Thailand, using climate change projected by the CSIRO-Mk3 model for 2009–2038. The EPIC

(Erosion Productivity Impact Calculator) crop model was then used to simulate cassava yield according to four scenarios based on combinations of CO2 fertilization effects scenarios (current CO2 level and 1% per year increase) and agricultural practice scenarios

(with current practices and assumed future practices). Future practices are the result of assumed advances in agronomic technology that are likely to occur irrespective of climate change. They are not prompted by climate change per se, but rather by the broader demand for higher production levels. This thesis illustrates the potential impact on cassava production due to climate change, and the use of advanced technologies in agricultural practices that will probably occur in the future.

Generally, crop losses stem from higher mean temperatures. However, there are also benefits from elevated CO2 concentrations. Depending on the climate change scenario without a CO2 fertilization effect, the average annual yield for cassava is projected to decrease from current production by 28%. CO2 fertilization effect cannot iv fully overcome these negative results. A positive increase in yield of about 35% is also projected in light of future adjustments in agricultural technology. Future cassava production in Northeast Thailand will be significantly affected by the climate change projected there, but detrimental effects may be mitigated by utilizing CO2 enrichment which promotes crop survival in marginal areas.

v TABLE OF CONTENTS

LIST OF FIGURES ...... vii

LIST OF TABLES...... ix

ACKNOWLEDGEMENTS...... x

Chapter 1 Introduction ...... 1

Chapter 2 Relevant Research...... 8

Approach to Climate Impact Assessment...... 8 Justification for Research ...... 10 Climate Impacts...... 10 Climate Changes in Southeast Asia...... 11 Characteristics of Climate Conditions in Thailand ...... 13 Agricultural Production: An Exposure Unit...... 14 Root Crops and Their Importance ...... 17 Cassava: Characteristics and Production...... 19 Impacts on Agriculture ...... 20 Methods of Impact Assessment...... 23 EPIC Crop Model...... 24 Research Questions...... 26

Chapter 3 Study Areas ...... 28

Characteristics of the Study Areas...... 28 Soil Characteristics...... 32 Climate Patterns...... 33 Climate Variability ...... 39 Agriculture in the Study Areas ...... 39 Past Impacts of Climate on Agriculture...... 43

Chapter 4 Method and Data ...... 45

Study Site Selection...... 45 Methodology...... 47 Erosion Productivity Impact Calculator (EPIC) Model ...... 49 Input Data Required for the Model...... 52 Weather data...... 52 Soil Data ...... 53 Management Data...... 54 vi Model Validation...... 55

Chapter 5 Climate and Crop Scenarios...... 59

Climate Change Scenarios...... 60 Climate Ccenarios ...... 60 CO2 Scenarios...... 66 Crop Scenarios...... 67

Chapter 6 Crop Yield and Responses for Various Scenarios ...... 71

Effects of Climate Change and CO2 on Cassava Yield ...... 72 Effect of Climate Variables on Crop Yield ...... 73 Effects of CO2 on Crop Yield with Current Agricultural Technology/Practice...... 74 Effects of Climate Variables, CO2 with Future Agricultural Technology/Practice...... 75 Effects of Climate Change, CO2, with Future Agricultural Technology/Practice...... 77 Effect of CO2 Fertilization on Crop’s Water Relations...... 80 Relations between Climate Change and CO2 Affect Crop Yield...... 84

Chapter 7 Conclusion...... 88

Bibliography ...... 92

Appendix Adjustment of the CSIRO-Mk3 Climate Data...... 102

vii LIST OF FIGURES

Figure 2-1: Basic conceptual model of climate impacts...... 9

Figure 2-2: Geographical features of Thailand...... 15

Figure 3-1: Map of the study areas––Sakon Nakhon and Udon Thani...... 30

Figure 3-2: Sakon Nakhon and Khorat basins in the northeast region...... 31

Figure 3-3: Map of the Dan Sai soil series in the Sakon Nakhon province...... 34

Figure 3-4: Map of the Dan Sai soil series in the Udon Thani province ...... 35

Figure 3-5: Climate patterns in Northeast Thailand ...... 37

Figure 3-6: Climate patterns in the Sakon Nakhon province...... 38

Figure 3-7: Climate patterns in the Udon Thani province ...... 38

Figure 3-8: Variability in air temperature in 1978–2007...... 40

Figure 3-9: Variability in precipitation in 1978–2007...... 41

Figure 3-10: Trend in cassava planting in the study areas...... 43

Figure 3-11: Cassava production trends in the study areas...... 44

Figure 4-1: Weather stations in Northeastern Thailand...... 48

Figure 4-2: Diagram of methodology for this project...... 51

Figure 4-3: Actual cassava yield compared to EPIC simulated yield for Udon Thani...... 57

Figure 4-4: Actual cassava yield compared to EPIC simulated yield for Sakon Nakhon...... 57

Figure 5-1: CSIRO-Mk3 projected annual mean climate data for UDN (upper) and Sakon Nakhon (lower) ...... 64

Figure 5-2: CSIRO-Mk3 projected annual means for monthly climate data for Udon Thani (upper) and Sakon Nakhon (lower) ...... 65

Figure 6-1: Changes in cassava yields from baseline, simulated based on scenario 1 (baseline period using CO2 at 330 ppm)...... 74 viii Figure 6-2: Percentage changes in cassava yields from baseline, with and without CO2 effect scenarios (baseline period CO2 = 330 ppm)...... 75

Figure 6-3: Percentage change in cassava yields from baseline, with current and future agricultural technology/practice...... 77

Figure 6-4: Percentage changes in cassava yields from baseline, future agricultural technology/practice, and with and without CO2 effect scenarios (baseline period CO2 = 330 ppm) ...... 78

Figure 6-5: Changes in cassava yields as simulated via four scenarios...... 79

Figure 6-6: Percentage changes in crop-water relation impacts in response to CO2 effects...... 82

Figure 1-1: Comparison of the CSIRO-Mk3 projected maximum temperature and the observed historical temperature before data adjustment...... 102

Figure 1-2: Comparison of observed maximum temperature and projected maximum temperature after adjustment ...... 104

Figure 1-3: Comparison of observed minimum temperature and projected minimum temperature after adjustment...... 105

ix LIST OF TABLES

Table 3-1: Dan Sai (Ds) soil characteristics on cassava plantings in the study area ..33

Table 4-1: Result of paired t-test for actual yield and EPIC simulated yield ...... 58

Table 5-1: Climate and crop scenarios...... 59

Table 5-2: EPIC crop parameters adjusted for agricultural technology/practice scenarios ...... 69

Table 6-1: Correlation between cassava yield and climate/ crop-water relations variables...... 85

Table 6-2: Multiple regression analysis results ...... 86

x ACKNOWLEDGEMENTS

I wish to thank Prof. Amy Glasmeier, Prof. William E. Easterling, and Dr. Jimmy

R. Williams for their guidance in my effort to conduct this research. I am grateful to the

Agricultural Research Development Agency (Public Organization) and the Department of

Geography and Faculty for their generous support and encouragement in the pursuit of my study. Finally, I wish to thanks to my family and colleagues in the Department for their support and encouragement throughout this work.

Chapter 1

Introduction

In this study, an attempt was made to assess the impact of climate change stemming from increased atmospheric CO2 on cassava crop production in Northeastern

Thailand. Udon Thani and Sakon Nakhon provinces were selected as the case study areas. According to the Intergovernmental Panel on Climate Change (IPCC), Southeast

Asia will begin to experience increases in precipitation and temperature as a result of elevated CO2 (IPCC, 2007). The projected change in climate variables may influence crop production (Grigg, 1995). While many studies have focused on the impact of climate on cereal crops, fewer have examined the likely impact of climate change on root crops such as cassava. In order to bridge this research gap, an assessment was conducted to determine whether changes in the climate variable were the reason for a decrease in cassava yield.

Among several root crop varieties, cassava (Manihot sp.) is one of the main root crops growing in many developing countries located in a low latitude region (Itharattana,

2003). Its importance lies in its diverse uses as food, fiber, and energy sources. The cassava root can be transformed into several end products both by food industries (e.g., pellets, chips, flour, sweetener, etc.) and non-food industries (e.g., bio-ethanol)

(Ratanawaraha, 2001; Wattananonta, 2002; Sriroth et al., 2003). Its many applications in

Thailand and other tropical countries have increased demand for cassava production

(Office of Agricultural Economic-Thailand, 2006). Rising demand has precipitated 2 research and development in agricultural technologies that may lead to increased yields.

Despite these investments, cassava production in Thailand remains low relative to demand (Hershey et al., 2001; Office of Agricultural Economics, 2004; 2005; 2006).

Climate variation and extreme events are among the factors influencing cassava production in Thailand (Office of Agricultural Economics, 2004; 2005; 2006). Therefore, in order to assess the impact of climate change on crop production, it is necessary to understand how a crop’s physiology responds to climate variables stemming from changes in future climate conditions.

Recent literature on climate change impacts has noted the consequences of climate change for cereal crop production. Affected crops include rice, wheat, and maize.

According to study findings, changes in temperature and precipitation may have different positive and negative impacts on agriculture in each world region. In mid- to high- latitude regions, the projected warming is likely to increase cereal crop and pasture yields, whereas only a slight warming is likely to decrease the yield in low-latitude regions (IPCC, 2007). The impacts can be observed in shifts in crop type, crop location, crop calendar, total crop yields, and crop prices (e.g., Hu and Buyanovsky, 2003; Luers,

2003; Adejuwon, 2005; Bezuidenhout and Schulze, 2006; Torriani et al., 2007). Cereal crop responses to climate changes have long been studied. However, root crops’ responses to future climate conditions are not well understood (Easterling et al., 2007).

Further study of root crops’ relationship to climate change variables is crucial because these food crops are vital to the rural poor and are a cash crop in several countries.

In addition to climate variability (temperature and rainfall), several studies have reported that CO2 concentration is another variable affecting plants’ physiological and 3 biological processes. As the CO2 effect increases with rising temperatures, it simulates photosynthesis and determines crop productivity. The degree of the CO2 effects is uncertain based on crop types and environment. Under optimal climate conditions, evidence of physiological and biological changes shows positive impacts on rain-fed rice production in Thailand (Dominique et al., 1992), winter wheat in China (Allison et al.,

2006), and maize, sorghum, and cassava in Nigeria, but negative impacts on maize and wheat in Bulgaria and in the U.S. (Alexandrov and Hoogenboom, 2000; Brown and

Rosenburg, 1999; Wassenaar et al., 1999; Allison et al., 2005). According to Kimball et al. (2002), elevated CO2 has an indirect impact on plant-water relations rather than photosynthesis under heat stress conditions. During heat stress, soil has little moisture and water available for plants; elevated CO2 simulates stomatal conductance which reduces evapotranspiration and improves water use efficiency. Despite unfavorable climate conditions, crop productivity may increase with CO2 enrichment.

In the case study region selected for this research, cassava is mostly grown in marginal dryland that receives less rainfall and is of lower quality compared to land used in cereal crop production in other regions (Ratanawaraha et al., 2001). Thus, differences in the physical properties of cassava root crops and cereal crops mean that findings from studies of climate change impacts on the latter are inappropriate when applied to such examinations of the former. This research study contributes to the correlation of future change in climate variables (temperature and rainfall), increased CO2 concentration, and potential cassava yield by taking into account CO2 effects on plants’ physiological and biological growth factors. The objective was to determine how future climate changes impact cassava production and assess whether cassava production in Northeastern 4 Thailand can cope with future climate variable changes. In order to conduct this type of research, this thesis followed the framework of climate impact assessment.

Climate impact assessment is used to explore the dynamic relationships linking biophysical systems of plants to climate (Kates, 1985; Parry and Carter, 1998).

Application of this basic concept facilitates analysis of potentially negative crop responses by employing two different models—the empirical-statistical model and the process-based model (Hamm, 1995). The empirical-statistical model relies on statistical methods and is used to explain the relationship between climate and crop yields.

However, in order to assess impact, the values of future climate conditions must be within the range of climate variables used in a generated model. With this limitation, the model does not seem like a good choice for examinations of the dynamic relationships between crop yields and the uncertainty of future climate variables.

Unlike the statistical model, the process-based model is considered to be more reliable in assessing climate change effects on agricultural productivity (Tan and

Shibasaki, 2003). This model applies established physical theories to explain the dynamic interactions between crop and climate conditions and so may be applied to wide ranges of plants and environments. It provides more comprehensive explanations of a causal mechanism between climate variables and crop growth factors than do statistical models

(Tan and Shibasaki, 2003). This may be demonstrated by using a simulation crop model.

EPIC (Erosion Productivity Impact Calculator), for example, is a tool in the biophysical process-based model that simulates potential yields by calculating daily weather data, physical soil properties, and crop management parameters. Easterling et al. (1996),

Thomson et al. (2005), Adejuwon (2005; 2006), and Williams et al. (2006) showed that 5

EPIC can be used to simulate crop yield and CO2 fertilization effects on plants under diverse regional environment conditions, climate conditions, and dynamic atmospheric

CO2 concentrations which cover the basic output studied for this thesis research.

The input data applied to the EPIC model is important in assessments of crop yield impact. Similar research on climate impact assessment has assigned fixed CO2 levels to monthly climate data to simulate output yields for an entire period (Brown and

Rosenberg, 1999; Alexandrov and Hoogenboom, 2000; Adejuwon, 2005). In reality, CO2 levels gradually increase year after year until a particular concentration level has been attained. Therefore, using a stabilized CO2 level in predictions is likely to overestimate potential annual crop yield in this study. For this research, EPIC was modified to generate yield from dynamic CO2. Moreover, crop and crop management parameters were the main input in projecting yield. However, this input was adjusted from current parameters because improvements in agronomic technology, and farm practices over the next 30 years are likely to maximize yield. Simulated yields may be underestimated if this fact is ignored. Nonetheless, most research does not clearly show whether this factor has been adjusted before projecting yield. In order to project future cassava yield at levels close to reality, a crop management scenario was created, and demonstrated here in addition to the climate, and CO2 impact scenarios.

The contribution of this thesis is that three main fundamental factors which determine crop yields are taken into account. This study conducts three crossing scenarios—climate, dynamic elevated CO2 level, and crop technology/practice-- to demonstrate responses of cassava production and the potential yield due to the future changes in climate variables, and CO2 enrichment. First, the climate scenario simulation 6 employs baseline climate input data for the period 1978-2008 obtained from the Thai

Meteorological department, and the projected climate data for the period 2009–2038 from the Australian CSIRO-MK3 model (A2 scenario). Second, the simulated yield is generated in a combination with two CO2 concentration scenarios -- the current constant

CO2 level at 380 ppm, and the elevated 1% increase of CO2 concentration per year. Third, the study contributes future agricultural practice scenarios – the upper bound level assumes advances in agricultural technology and practice that are likely to occur irrespective of climate change in the next 30 years, and the lower bound level assumes no change in agricultural technology and practice from the current period. The correlation and regression techniques are used for analyzing simulated output and for prediction. The result of this thesis elaborates the correlations between cassava yield and those three main factors. This finding helps visualize the potential of cassava production and the alternative practices necessary in order to prepare a necessary agricultural plan to handle with the future climate change in the region.

This thesis is divided into seven chapters. Chapter 1 offers an introduction to the study; the second chapter is a literature review on climate change and its relevance to agricultural production, climate conditions and cassava production in Thailand. Drawing on theories identified during the literature review, the research questions are then discussed as they frame this study. Climate conditions and physical characteristics of the study areas are described in chapter 3. Chapter 4 contains descriptions of the methods, tools, and data employed in the research. The explanation of the study area selection process is also included in chapter 4. Chapter 5 provides details of selected scenarios— crop managements and projected climate scenarios––used in this study. Empirical 7 analysis of the interaction among crop yields, crop responses to climate variables, and subsequent results are discussed in chapter 6. Last, in chapter 7, conclusions are provided.

Chapter 2

Relevant Research

Approach to Climate Impact Assessment

A climate impact assessment is a study that focuses on the impact of interactions between nature and society. According to Kates (1985) and Beniston (2005), a climate impact assessment encompasses elements from science, physical, biological, and social- behavioral and uses them to determine the interaction between climate impacts and possible consequences. The impact assessment model shown in figure 2-1 is a simple representation of a single cause-and-effect relationship in which climate events have an impact on the exposed units. In order to assess climate impacts, it is necessary to identify the elements involved in the processes and their interrelations (Hamm, 1995). Referring to figure 2-1, the impact model is comprised of three main elements: climate event or climate change, exposure units, and impacts (Hamm, 1995; Parry and Martens, 1999).

Climate events are sets of geophysical states and processes within a boundary condition. Examples of geophysical states used in several studies are future climate conditions and extreme weather events, such as increasing concentrations of atmospheric

CO2, drought, and flooding (Kates, 1985; Hamm, 1995). The climate events are generally distinguished by temporal scales: between-year (Tan and Shibasaki, 2003), persistent long periods (Adejuwon, 2006; Lobell and Field, 2007), and a century or multi-century- long periods (Alexandrov and Hoogenboom, 2000).

9

Climate Events Exposure Units Impacts

Figure 2-1: Basic conceptual model of climate impacts (Kates, 1985)

Exposure units are the impacted groups of people or living systems, activities or areas that are influenced by the particular climate events (Kates, 1985). There are several scales of exposure units: individual scales (e.g., people, households, plants, animals), population/community scales (e.g., economic/industrial sectors, agricultural sector), and regional scales based on climatic boundaries (e.g., floodplains, political boundaries)

(Kates, 1985; Hamm, 1995).

The final element in the impact approach is identifying the consequences of the interaction between climate events and the exposure unit. These impacts can be separated into a sequential order of actions (Hamm, 1995). Generally, the first-order impact will be in the biophysical activity sectors, such as food and fiber, water, crop yield, and energy.

The second-order impact generally focuses on economic and social effects, such as nutrition, human health, energy production, or socio-economic sectors (Kates, 1985).

The impact approach has been widely adopted in the study of climate affects on biophysical systems. For example, the impact studies of natural ecosystems (Parry and

Martens, 2003), water resources (Beniston, 2004), food security (Lal, 2005), agricultural practices (Luers, 2003; Hu and Buyanovsky, 2003; Alcamo et al., 2007), and crop production (Mearn and Rosenzweig, 1997). For this thesis, I assess the climate impact on the cassava agricultural production system in Northeast Thailand, focusing on plant biophysical responses and its production, which is the first-order impact.

10 Justification for Research

Climate-crop relationships under changing climate conditions should be investigated because climate affects resources—solar radiation, temperature, and precipitation, etc.—vital for plant growth and production systems. Potential climate change may have beneficial or deleterious impacts on agricultural production due to drought, flood, or increased CO2 fertilization, which influence society. The anticipation of how future climate change variability will affect crop productivity and its biophysical systems is an important issue to consider.

Climate Impacts

Three major concerns that drive global climate changes include increasing concentrations of atmospheric CO2 and other greenhouse gases (GHGs) associated with warmer temperatures, and increasing precipitation variability. Analyses conducted for the

IPCC Fourth Assessment Report (AR4) indicate the increase in greenhouse gas (GHG), including CO2, in the Earth’s atmosphere. It is projected that by the end of the next century, rising CO2 will be double the current level, which will lead to warmer air temperatures and increases in percentage change in seasonal precipitation (IPCC, 2007).

Based on these projections, temperatures in the 21st century are predicted to increase from 2–5.4°C under continuously increasing CO2 emissions. The projections highlight the fact that natural physical and biological systems, such as plant and hydrology, are likely to respond to the increasing CO2 emissions (IPCC, 2007). For example, warmer temperatures are associated with the elevated CO2 increases in sea

11 surface temperatures, resulting in more frequent rainfall. This means that more water is available for plants. Meanwhile, plants alter their responsive function due to elevated

CO2 concentrations. Plants could gain either an advantage or disadvantage from changes in climate conditions.

According to Sirinanda (1997), Snidvongs (2006), and Cruz et al. (2007), the distribution patterns for temperature and precipitation are relatively distinguished based on the geographical location of each region. In the next section, I describe the projected climate changes, focusing on regional scales, for Southeast Asia and Thailand.

Climate Changes in Southeast Asia

Based on the IPCC’s AR4 report, Southeast Asia will experience an increase in mean temperature of 1-2°C, which is similar to the global mean temperature, as well as an increase in rainfall variability in the southern part of the region (Manton et al., 2001;

IPCC, 2007). These changes will be relatively small compared to other parts of Asia

(e.g., Central Asia, Eastern Asia, and South Asia) and other regions in higher and lower latitudes (e.g., North America, Africa, and Europe) (Christensen et al., 2007). However, as Southeast Asia is considered one of the regions with complex physiographical features and altitudinal differences, the impact of climate variable changes (temperature and precipitation) is varied across both space and time (Sirinanda, 1997; Lal et al., 2001).

Consequently, the quantitative study of climate impacts is more difficult in this region, and a more detailed geographic-level study is required (Cruz et al., 2006). Before investigating future climate change impacts, it is necessary to understand the nature and

12 causes of variability in climate in Southeast Asia and Thailand. These details are provided below.

Southeast Asia is located in approximately 10-25° N latitudinal zone and 90-140°

E longitudinal zones. Physiographically, Southeast Asia is considered a fragmented region forming peninsulas and island arcs with rugged relief of mountain ranges standing in the north (Sirinanda, 1997). It includes large rivers, such as the Irrawady, Chao Praya,

Mekong and Red, located on the mainland that form dense drainage networks across the region. This peculiar characteristic lends diversity to the biophysical environment which influences the climate at the regional scale.

With areas penetrated by both land and sea, the Southeast Asian climate is influenced by two ocean masses (Indian and Pacific Oceans) and the land masses between Asia and Australia. These ocean and land masses contribute key elements to the

Asian Monsoon, which is an important source of climate variation in Southeast Asia

(Chuan, 2005). The Asian monsoon is the wind that develops from the changing pattern in atmospheric circulation caused by the transmission of heating and cooling between land and oceans. The movement of monsoonal wind systems along with longitudinal and altitudinal differences lead to spatial and temporal variation in rainfall in large areas of

Southeast Asia (Chuan, 2005).

There is evidence that climate change can influence monsoons and subsequently alter the intensity of both temperature and precipitation in various areas (Kripalani et al.,

1995; Mitchell and Hulme, 1999; IPCC, 2007). The significant increase in extreme climate events could occur in the form of high temperatures, heavy rainfall, and/or flooding projected in the Philippines, Northern Indonesia, Vietnam, and Thailand

13 (University of Arizona, 2003; Chinvanno et al., 2006; Cruz et al., 2007). Manton et al.

(2001) reported a decrease in the number of rainy days (with at least 2 mm of rain) in

Malaysia and drought risk in Southern Indonesia (Christensen et al., 2007). This evidence supports a need for climate impact studies at the regional scale because the occurrences of climatic patterns and impacts of climate events are not uniform throughout the

Southeast Asia regions.

Characteristics of Climate Conditions in Thailand

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 6° to 20° north latitude and 97° to 106° east longitude and can be divided into six physiographic regions—northern, northeastern, western, central, southern, and east coast and west coast peninsulas (Yoshino, 1984). Climate conditions over Thailand are affected by two major air streams—the northeast monsoon and the southwest monsoon. The climate components dominating the distribution of temperature and precipitation over Thailand are the monsoon, combined with the movement of the Inter

Tropical Convergence Zone (ITCZ) (Kripalani, 1995). The ITCZ moves northward in

May and southward in September.

Generally, the average temperature in Thailand varies from 24.4–29.3° C (76–85°

F) with annual precipitation ranging from 998–4,603 mm. From the beginning of

November to February (dry season), except for the lower south, the northeast monsoon brings cool and dry air from the Siberian anti-cyclone and the South China Sea to all

14 parts of Thailand. The southwest monsoon, the main source of precipitation in Thailand, brings humidity from the Indian Ocean to the eastern region for a rainy season that lasts from May to October (Ratanopad and Kainz, 2006).

Because of the geographical features within the region (figure 2-2), the distribution patterns for temperature and precipitation are spatially and temporally different, which in turn governs agricultural activity.

Agricultural Production: An Exposure Unit

Currently, some scientific studies have assessed the subsequent impacts of future climate change on agriculture (e.g., Murayama et al., 2003; Clark et al., 2003; Binbol et al., 2006; Alcamo et al., 2007). The reports indicate that potential changes in climate variables, CO2 concentration, temperature, and precipitation, are likely to influence plants’ physiological systems and agricultural production. Therefore, the alterations of plants’ growth functions and yield are used as indicators to measure the magnitude of future climate change impacts.

15

Figure 2-2: Geographical features of Thailand (Source: Gupta, 2005)

16

Nonetheless, the accuracy and consistency of the same effect are greatly uncertain due to the spatial distribution of the crop-climate environment at a regional scale. Plant responses to climate change depend on the limitations in the crop environment and other relevant factors, such as water availability, soil characteristics, and crop varieties.

Therefore, most impact analyses are likely to limit the study of small-scale changes in single crops or field production or plant physiology to a limited crop environment— setting the climate or soil characteristics as constant (Thomson et al., 2005). Evidence demonstrates the effects of climate changes on single grain crops such as rice, wheat, corn, and soybean (Lal et al., 2001; Adejuwon, 2005; Thomson, 2005; Motha and Baier,

2005).

Similar to previous research, this thesis also involved an impact assessment on single crop production in Thailand and limited the crop environment by controlling for soil characteristics in selected study areas. However, in comparison to other findings regarding the impact of climate factors on grain and cereal crops, little evidence has been found about impacts on root crops (Easterling et al., 2007), including cassava. The extrapolation of climate impacts on cereal crop yields to root crops would result in misunderstandings and inaccurate assumptions due to differences in the physical properties of root crops and cereal crops (Rijks, 2003; Lutaladio, 2003). Therefore, the research conducted for this thesis contributes to impact assessments of cassava root crop production in order to enhance understanding of climatic effects on agricultural production. In the next section, I provide background on root crops, specifically cassava root crop production and its importance for the study.

17 Root Crops and Their Importance

Root or tuber crops play a crucial role in the agricultural sectors of many countries. These crops provide an important source of animal feed, and a raw material for industrial products. The most important role of root crops is as a substantial actor in the world’s food supplies (FAO, 1997). Unlike cereal crops, the root that is the edible part of the plant can be kept underground until needed. With this characteristic, root crops may serve as substitute foods when other food or cereal crops are in short supply. Root crops have provided household-level food security for more than a decade (Rijks, 2003).

Examples of root and tuber crops are cassava, potato, sweet potato, yam, and edible aroids.

Among several root crop varieties, cassava (Manihot sp.) is one of the main root crops growing in many developing countries. Fresh cassava root contains starch that can be transformed into several end products--pellets, chips, flour, sweetener, etc.—used in food industries (Ratanawaraha, 2001). Its stems and leaves can be used as raw material for bio-ethanol in non-food industries (Wattananonta, 2002; Sriroth et al., 2003). Because it has broad uses in many industries, cassava has become an economic crop in increasing demand worldwide (Howeler and Tan, 2000; Rijks, 2003). This worldwide demand has encouraged new developments in agricultural and research practices and technologies.

Among these improvements are soil improvement, land management, and breeding technology that may lead to increased production of cassava to meet worldwide demand

(Howeler and Tan, 2000; Ratanawaraha, 2001; FAO and IFAD, 2004).

18 Cassava is a raw material for non-food industries, too, including bio-ethanol.

Thus, its importance should not be underestimated since some countries depend heavily on it (Itharattana, 2003; GIEWS, 2007). In Thailand, cassava constitutes an important source of export revenue. In fact, Thailand ranks first among world cassava exporters and third among world cassava producers. Thailand’s export of cassava products accounts for approximately 90% of total cassava export in the world market. The principal markets for cassava products (in the form of chips, pellets, and flour) are Europe, Japan, China, and

Taiwan (GIEWS, 2007).

Most research has focused on improving cassava’s adaptability to wide ranges of growing conditions, such as under drought conditions, at various levels of soil moisture, and in various soil types and qualities (Watananonta, 2002; Boonseng et al. year; Sriroth et al., 2003). Some research has paid particular attention to agricultural management in the location of suitable crop zones for cassava plantations (Sorawat and Rattanasriwong,

2007). In addition to this research, many agricultural technologies for improving soil quality, irrigation management, and harvesting have been invented (Ratanawaraha et al.,

2000; Watananonta, 2002).

Despite advances in agricultural technology, cassava production remains uncertain and average crop yield is still low (Henry and Westby, 2001). Climate is claimed to be a factor in yield variations (Hershey et al., 2001; Office of Agricultural

Economics, 2004; 2005; 2006).

19 Cassava: Characteristics and Production

Based on the plants’ physical characteristics, agricultural practices between cereal crops and root crops are also different. Cassava root is developed underground; thus, the flowering and seed production periods are less important than those of the cereal crops.

Cropping periods that considerably impacted on cassava production are the planting period and the harvesting period (Rijks, 2003). In Thailand, for instance, the suitable planting period generally starts in the early rainy season and harvest begins at the late rainy season (Sriroth et al., 2003). The reason for this is that the average amount of rainfall in the early planting period determines the quality of starch content as well as the development of fresh roots. Excess rainfall during the planting period could rot cassava roots. During the harvesting period, optimum rainfall and moisture are also required. Too little moisture in the soil will obstruct the harvesting process as the soil is too hard to dig into; whereas, high moisture in the soil will reduce the percentage of starch content in fresh roots leading to a decline in market price (Office of Agricultural Economics, 2004;

2005).

Another factor that makes cassava different from cereal crops is how its post- harvest constraint affects the production. Because cassava roots have no natural dormancy, they are highly susceptible to physiological deterioration, such as discoloration, smell alteration, and microbial contamination. This results in the short storage life (less than 2-4 days) of fresh roots (Wenham, 1995). After harvesting, sunny days and open dry areas are needed to expose the cassava roots to natural drying on the ground. In case of unsuitable climate conditions, cassava can be left in the ground

20 without serious damage to the root until favorable conditions are met. Thus, the harvesting time of root crops is more flexible than that of cereal crops (Ratanawaraha et al., 2001; Rijks, 2003). The linkages among climate condition, growth period, and crop yield of root crops are merely mentioned in agricultural reports of the Office of

Agricultural Economics, yet quantitative analysis of climate impacts on cassava has not been attempted.

Despite developments in crop varieties and the introduction and use of advance technologies and agricultural practices, cassava production is still variable (Hershey,

2001). In order to understand the impacts of climate changes on cassava root crop yields in the present and future, it is necessary to examine the response of the crop to historic climate change. This study will examine the influence of climate on cassava yields in

Udon Thani and Sakon Nakhon, the study areas in Northeastern Thailand, for the period

1998-2007. The results are expected to increase our understanding of climatic changes and their effect on agriculture and complement research on cereal crops. (Wenham, 1995;

Nareerat, 2005).

Impacts on Agriculture

Agricultural impact is the final element of the impact study. As mentioned in previous section, the alterations of three climatic variables due to climate change including temperature, precipitation, and CO2 concentration are considered the climatic components that influence crop production and crop physiological response. This section

21 will explore the previous finding of the potential interactions among climatic variables and plants that may drive the findings of this thesis.

Air temperature and precipitation are considered two major climatic variables that are important resources for all crop production. Riha et al. (1996), Hu and Buyanovsky

(2002), Murayama et al. (2003), Motha and Baier (2005), and Adejuwon (2006) dedicate their work on the interaction of individual climate variables on crop productivity. The finding results suggest that higher temperature and fluctuated rainfall are generally decrease crop yield. For example, heat-stressed condition in Vietnam is expected to decrease rice production (UNEP, 1993; Lal et al., 2001), and the excessive rainfall is expected to decrease rice production in Thailand and Malaysia (Sahaschai). Nonetheless, there are some regions that may experience positive impacts from the warmer temperature. For instance, corn and sugarcane production in the Northern region of

Thailand may benefit from the increase mean temperature and mean precipitation

(Sahaschai).

In addition to the effect of changes in temperature and precipitation on plant, the rising atmospheric carbon dioxide concentration is another factor of concern that directly affects crop development and productivity. Nowadays, impact studies are likely to combine the effect of climate change with rising CO2 concentration. According to

Kimball et al. 2002, the rising CO2 ‘fertilization effect’ which is associated with higher temperature is known to stimulate photosynthetic systems. Plants respond to increased

CO2 concentration by reducing stomatal conductance and transpiration. Hence, they conserve water and decreases water stress with subsequent crop productivity increases

(Smith et al. 2005). There is evidence that under optimum climate conditions crops

22 including rice, wheat, and sorghum gain benefit from the CO2 fertilization effect (Parry et al. 2004; Motha and Baier, 2005; Sahaschai).

Most impact studies mentioned above mostly focus on the grain crop production cultivated in well-irrigated system areas; the detrimental impact can be mitigated using irrigation system. Unlike grain crops, drought tolerant crops such as glass crops and root crops are grown in marginal dry land which depends heavily on an optimal amount of rainfall without the irrigation system. As a consequence, the plants’ biological responses to CO2 fertilization and the subsequent impacts on crops growing with and without an irrigation system are probably different (Motha and Baier, 2005).

There is evidence supporting that CO2 fertilization effect decreases with the occurrences of water-stressed conditions during the growing season (Easterling et al.,

2007). Under stress conditions, the elevated CO2 concentration has an indirect impact on plant water relations compared to the direct impact on plant photosynthesis (Kimball et al. 2002). The CO2 –induced reduction in stomatal conductance lowers transpiration (ET) and water loss and leads to an enhancement of plant water-use efficiency (WUE) and may alleviate drought stressed conditions (Morison, 1987; Triggs et al., 2004). All this in mind, the impact study on a drought tolerant crop or crop cultivation in marginal areas should consider the water relations-- evapotranspiration (ET) and plant water potential-- in addition to crop yields, as the additional impact indicator.

23 Methods of Impact Assessment

Over the past 30 years, methods of impact assessment approaches have been developed. A variety of analytical methods have been adopted for impact assessment such as model projections, empirical studies, expert judgment, and experimentation

(Kates, 1985). In this paper I will focus on the first method of model projection.

The model projection that is widely used to evaluate the physical interactions between climate factors and exposure units is the biophysical impact model (Smith et al.,

2005). This model is well suited to the agricultural sector, the hydrological aspects, the natural resources, and ecosystems (Parry and Martens, 1998; 2003). The biophysical impact models can be classified into two main types 1) empirical-statistical models, and

2) process-based models (Parry and Martens, 1998; Tan and Shibasaki ,2003).

Empirical-statistical models are used to analyze correlations between climate and the exposure unit. The statistical methods range from simple statistical analysis through regression models to complex multivariate models. Hu and Buyanovsky, (2003),

Adejuwon (2005), and Binbol et al.(2006) employ statistical methods to assess the impact of climate conditions on crop yields. However, this model has a limitation when predicting the impact of future climate conditions because the values of the future climate conditions must lie inside the range of climate variables used in a generated model.

Additionally, the statistical relationship does not provide a comprehensive explanation of a casual mechanism between factors (Parry and Martens, 2003; Tan and Shibasaki,

2003).

24 Process-based models apply the established physical laws and theories to explain the interaction between climate and the exposure unit (Kates, 1985) and are used in most impact studies. This model requires higher demands on input data than the empirical- statistical model; but, the process-based model can represent processes that can be applied globally to similar systems in a wide range of the climatic conditions (Parry and

Martens, 2003). For example, crop models can be applied to wide ranges of plants and environments (Chen and Srinivasan, 1999). Recently several crop models, which are process-based, have been developed as research tools to assess the potential yield under the impacts of climate change at different scales, such as local, regional, national, and global, such as EPIC crop model (Alexandrov and Hoogenboom, 2000).

EPIC Crop Model

EPIC (Erosion Productivity Impact Calculator) is a crop model that simulates crop yields at the field-based level (Williams, 1999; Tan and Shibasaki, 2003). This model was developed by USDA-ARS, SCS, and ERS (Agricultural Research, Soil

Conservation and Economic Research Services of the U.S. department of Agriculture) at the Grassland Soil Water Research Laboratory in Temple, Texas (Easterling et al., 1993).

Being a physical-process model, EPIC’s major processes are integrated and related to the variables of soil, atmosphere, and the management system. In addition to soil data, crop, and crop management input data, the key climatic inputs required for operating the model are daily weather data including maximum and minimum air temperature, precipitation, and solar radiation (Easterling et al., 1996; Hoogenboom,

25 2000). EPIC is capable of simulating the climate impact on crops, such as maize, wheat, legumes, rice, potato, and cassava (Alexandrov and Hoogenboom, 2000; Adejuwon,

2005). The validation of the EPIC has been examined by Rosenberg et al. (1992),

Easterling et al. (1996), and Adejuwon (2006). These studies find that EPIC is efficient in determining long-term crop response to the various climate change scenarios.

EPIC can be used to simulate crop yields due to future climate change conditions.

According to Easterling et al. (1996), EPIC operates on a daily climatic input; it calculates photosynthetic rate from the light-use efficiency. Then it converts this photosynthetically-active radiation into biomass (both above-ground and below-ground) which determines crop productivity. The amount of biomass or yield depends on crop environment factors such as temperature, moisture, and nutrients. The biomass can be decreased if stress environment condition is applied to the input of the model. The optimal input factors of the EPIC model are timing and application of irrigation

(Easterling et al., 1992; 1996). The procedure of EPIC model is described by Williams et al. (1981).

According to Easterling et al. (1993); Thomson et al., (2005); and Adejuwon

(2006), EPIC can demonstrate the mechanisms of crop physiological responses such as evapotranspiration in response to increasing CO2 concentration. Using Penman-Monteith model in EPIC, the model is made sensitive to changes in CO2 concentration as well as moisture deficit of air. The simulated evapotranspirational variable of EPIC can therefore be used to assess the crop responses on changing CO2 effects which is supported by the objective of this thesis.

26

However, previous impact studies applied static values of CO2 level to the model input. For example, the approximated CO2 levels between 550 and 720 parts per million

(ppm) compared to the current CO2 levels at 380 ppm, are used in most studies (Brown and Rosenberg, 1999; Alexandrov and Hoogenboom, 2000; Adejuwon, 2005). Using static values represent rapid increases in atmospheric CO2 level, as a consequence the model may over estimate the potential crop yield. Assuming the gradual and dynamic rising of CO2 levels, the EPIC model used in this thesis is modified by Williams, J.R.

(personal contact) to simulate crop yields and crop physical responses based on the dynamic CO2 concentration at 1% increase per year.

Research Questions

This thesis aims to fill the gap in the research on the climatic impacts on agricultural crops through an impact assessment of cassava root crops in Northeast

Thailand. The overall objective is to understand how climate change and its variability can influence cassava production within a certain climatic condition in the study area for the period 1998-2008. The trend of climate impacts on cassava production in the period

2009-2038 will then be estimated. Two specific research questions are:

1. What are the impacts of climate change on cassava productivity and its

biophysical responses in Northeast Thailand?

2. How will the expected trend of the climate change influence cassava

production in Northeast Thailand in the next 30 years?

27 This study adopts the EPIC crop model for climate change and its impact on biophysical systems. This project will focus on changes in climate variability

(precipitation and temperature under the rising CO2 concentration) and its first order impact on agriculture, particularly for the cassava crop yield and crop biophysiological systems. The impact assessment will be analyzed by using historical climate data (1978-

2008) collected by the Meteorological department of Thailand and the projected climatic data (2009-2038) from the simulation prepared by CSIRO for the IPCC (AR4).

Chapter 3

Study Areas

This chapter presents a description of the major biophysical characteristics of the study area in Northeastern Thailand. The geographical and physical features of the study areas are described. Information on climate conditions and their impacts on agriculture across the region are also included.

Characteristics of the Study Areas

Northeastern Thailand is situated from 14° to 20° north latitude and 101° to 106° east longitude. It occupies more than 17 million hectares, or one third of the total area in the country (Rathnawaraha et al., 2001). It has 19 provinces. The study area will be limited to two provinces––Udon Thani and Sakon Nakhon––selected as the representative sites for cassava production in the northeastern region (Figure 3-1).

The Udon Thani province is located in 17° 25′ north latitude and 102° 45′ east longitude. It occupies approximately 1.17 million hectares. Udon Thani is located at the center of Northeast Thailand, between its neighbors––Khon Kaen province in the South and Nong Khai province in the North. The Sakon Nakhon province is located in 17° 10' north latitude and 104° 9' east longitude and occupies 0.9 million hectares. The neighboring provinces are Nong Khai, Nakhon Phahom, Mukdahan, Kalasin and Udon

29 Thani. The elevations of Udon Thani and Sakon Nakhon are about 187 feet and 172 feet, respectively.

Geographically, the study areas are located in the northeastern region, known as the Khorat Plateau. The plateau contains the high flat terrains lying between undulating terrains. With the mountain range at the middle of the plateau area in the northeast direction, the main landform characteristics of the Khorat plateau are roughly elongated by hills and mountains alternating with rolling topography and lowland alluvial plains

(Wongsomsak, 1986). The Khorat plateau is comprised of two large basins: the Udon-

Sakon Nakhon basin in the upper area and the Khorat basin in the lower area (Figure 3-

1). The Khorat basin is drained by the Mun and Chi Rivers, while the Sakon Nakhon basin is drained by the Loei and Songkhram Rivers. The two basins are divided by the tectonically undulating Phu Phan mountain range. Among 19 provinces, only four in the study area are situated in the Udon-Sakon Nakhon basin (Archwichai et al., 2005).

The climate in the northeastern region is influenced by a tropical monsoon. The rainy season begins in May and lasts until October, when the dry seasons begins in

November and lasts until February. The summer season lasts from March to May. An average temperature in this region is about 26–27° degree Celsius with average annual rainfall at 1,200 millimeters (Ratanopad and Kainz, 2006).

30

Figure 3-1: Map of the study areas––Sakon Nakhon (SKN) and Udon Thani (UDN).

31

However, Troung Son Cordilliera, which lies in the middle of the region in the northeast direction, is in a position to trap rainfall. The intensity of the rainfall is lowered through large areas on the leeward side of Trong Song Cordilliera. As a consequence, the leeward (Khorat basin area) receives inadequate rainfall––900–1,200 mm annually–– whereas the opposite side covering the study areas; Sakon Nakhon and Udon Thani, receives over 1,300 mm of the annual rainfall (Bell and Seng, 2004).

Figure 3-2: Sakon Nakhon and Khorat basins in the northeast region. (Source: Wongsomsak, 1986)

32 Soil Characteristics

Sandy loam soil (>60% sand) is the typical surface soil of Northeastern Thailand.

This soil is acidic with a low inherent fertility (Bell and Seng, 2004). The soil in the study areas is of several types. Ultisols is the soil covering about 50% of the study areas. This soil order is generally sandy loam and contains more than 50 soil series, such as Borabu

(Bb), Chum Phuang (Cpg), Chakkarat (Ckr), Dan Sai (Ds), Huai Thalang (Ht), Korat

(Kt), Phen (Pn), Phon Ngam (Png), Phon Phisai (Pp), Roi Et (Re), Satuk (Suk), etc.

The Dan Sai (Ds) soil series is the selected soil in the study areas. According to the Land and Development Department of Thailand, the Dan Sai series are a member of the fine-loamy, kaolinitic, isohyperthermic Typic Kandiustults. These are very deep soils characterized by a dark brown or reddish brown sandy loam. This soil is well drained, and has rapid permeable and runoff. The organic carbon ranges from 0.40–1.29%. The soil surface has medium acid (soil pH 5.0–6.0). The characteristics of the Dan Sai series are shown in Table 3-1. Based on the LDD (2007), the Dan Sai series is well suited to cassava cultivation. It is found in the cassava planting areas in the Sakon Nakhon and

Udon Thani provinces, respectively (Figures 3-3 and 3-4).

Therefore, in this study, the Dan Sai soil series is considered the dominant soil series for cassava plantings in both the Sakon Nakhon and Udon Thani provinces.

Moreover, it is a soil parameter in the EPIC crop model scenarios.

33

Table 3-1: Dan Sai (Ds) soil characteristics on cassava plantings in the study area Soil depth (cm) pH USDA grading C% CEC (100g Clay) Sand Silt Clay 0-22 6.0 60.0 22.5 17.5 0.95 40.0 22-42 5.5 56.0 21.0 23.0 1.29 27.8 42-75 5.0 56.7 18.4 24.9 1.07 22.1 75-97 5.0 55.5 20.0 24.5 0.59 18.4 97-145+ 5.0 54.0 20.5 25.5 0.40 14.5

Climate Patterns

Northeastern Thailand is located in a tropical monsoon climate zone. The rainfall and seasons in this region are influenced by the directions of monsoons and winds that move in a circular fashion during the year. In winter (November–February), the northeast monsoon moves southward from China to Thailand. It brings cool and dry weather to the entire region, resulting in a decrease in average minimum temperatures to 10° C. The summer season (March–May) has average temperatures that can rise to 40° C. The highest temperature is in mid-April. In summer, thunder storms can be expected when cool air from the Northeast confronts the warm south wind. The rainy season, which lasts from the end of May to October, starts when the West and Southwest monsoons bring moisture from the South China Sea to the area. The annual rainfall ranges from 800–

2,900 mm. The patterns for average temperatures and precipitation from January–

December are shown in Figure 3-5.

34

Figure 3-3: Map of the Dan Sai soil series in the Sakon Nakhon province

35

Figure 3-4: Map of the Dan Sai soil series in the Udon Thani province

36 However, total rainfall and rainfall patterns vary across the regions. The study areas, which are located in the upper part of the Dan Sai plateau or in the Sakon-Nakhon basin, receive higher amounts of rainfall than the lower part of the plateau. According to the Meteorological Department of Thailand, during 1978–2007 the annual rainfall for areas in the Sakon-Nakhon basin, including the study areas, ranges from 900–2,900 mm.

The average temperature is from 12.9–39.4° C. For the Khorat basin, the annual rainfall ranges from 600–2,400 mm. with the average temperature from 10.7–40.2° C.

Monthly temperature and average rainfall are shown in climographs for each province (Figures 3-5 and 3-6). The average minimum and maximum temperatures in the

Udon Thani province for 1978–2007 range from 14.1–27.3° C. For Sakon Nakhon, the average minimum and maximum temperature for the same period is 14.2–26.9° C. The highest temperature occurs in April. Annual rainfall in both study areas ranges from 900–

2000 mm, which is higher than other provinces in the Khorat basin.

As shown in Figures 3-6 and 3-7, temperature and rainfall trends for Udon Thani and Sakon Nakhon are similar. However, precipitation patterns differ between the two provinces. The average precipitation for Udon Thani is lower than that of Sakon Nakhon.

Thai meteorological department records show that average precipitation in 1978–2007 for

Udon Thani and Sakon Nakhon was 117.31. There was a more drastic drop in the latter area in September than in the former area.

37

300 35

Avg Rainfall Avg Temp

30 250

25 200

20

150

Rainfall (mm) 15 Temperature (deg.C) 100 10

50 5

0 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Months

Figure 3-5: Climate patterns in Northeast Thailand (Data: Meteorological Department, Thailand, 2008)

38

400 35

Avg Rain Avg Temp 350 30

300 25

250 20

200

15 Rainfall (mm) Temperature (deg.C) Temperature 150

10 100

5 50

0 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Months

Figure 3-6: Climate patterns in the Sakon Nakhon province. (Data: Meteorological Department, Thailand, 2008)

350 35

Avg Temp Avg Rain

300 30

250 25

200 20

150 15 (deg.C) Rainfall (mm) Temperature

100 10

50 5

0 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Months Figure 3-7: Climate patterns in the Udon Thani province (Data: Meteorological Department, Thailand, 2008)

39 Climate Variability

In this part, aspects of climate variability such as temperature and precipitation in the study areas are investigated. From 1978–2008, the temperature and precipitation trends for Udon Thani and Sakon Nakhon were similar. Therefore, mean annual temperature as well as precipitation for both areas are grouped together for the 1978–

2008 period.

As seen in Figure 3-8, the temperature during 1978–1997 was generally below

26.89° C, which is the average temperature in the study areas. After 1997, the temperature often rose above the average. An increase in precipitation occurred after

1994.

In 1998, the temperature exceeded the mean while precipitation decreased much below the mean, leading to drought throughout the region. Flooding occurred in 2000–

2002 when both precipitation and temperature trends exceeded their annual means. The patterns for temperature and precipitation affected the agricultural activities in the study areas. The details are provided later in this chapter.

Agriculture in the Study Areas

The substantial salt deposits found at the subsoil level in Northeastern Thailand have had detrimental effects on soil quality. Large areas have become semi-arid plateaus with low soil fertility (Settle, 2002). In addition to the soil situation, drought conditions also lessen agricultural production. These types of constraints mean that a drought- resistant crop such as cassava is one suitable crop for the area, since it needs low

40 maintenance and no irrigation systems, and can be grown in sandy loam soil (Crews-

Meyer, 2003).

29

28

Mean = 26.89 mm

27 Temperature (deg.C)

26

25 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 Years Figure 3-8: Variability in air temperature: Departed from its mean at 26.89 in 1978– 2007. (Source: Meteorological Department, Thailand, 2008)

41

180

160

Mean = 117.23 mm 140

120

100

80 Rainfall (mm)Rainfall

60

40

20

0 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 Years Figure 3-9: Variability in precipitation: Departed from its mean at 117.23 mm in 1978– 2007. (Source: Meteorological Department, Thailand, 2008)

42 Cassava has long been one of the most important economic crops in Thailand. It is produced both for domestic use and export. About 80% of the cassava produce is exported to the European Union (EU) as the major market. Due to rising prices and high demand, cassava acreages were expanded from marginal areas to areas intended for other crops. This expansion led to declines in soil fertility, soil erosion, and the loss of the genetic diversity of the crop nationwide (Ratanawaraha et al., 2001). Therefore, in 1993 the Thai government established an agricultural strategy to limit cassava production within the marginal area. As a result, the number of acres used for cassava planting in

Thailand decreased continuously during 1993–2002. The areas for cassava planting in the

Sakon Nakhon and Udon Thani provinces are shown in Figure 3-10.

The Office of Agricultural Economics in Thailand recorded in 2007 that about

660 thousand hectares or 7% of the total cropped area in the Northeast was dedicated to cassava. The 14+ million tons of cassava harvested in the Northeast region were approximately 50% of cassava production in the entire country.

In the study areas, Udon Thani and Sakon Nakhon, rainfed rice is the major crop.

It is grown in a flood plain area for household consumption. Approximately 57,000 hectares of marginal upland are used for the cash crop, cassava. Cassava production in

Udon Thani is third highest in the Northeast region, whereas Sakon Nakhon ranks fifteenth (Office of Agricultural Economics, 2007). Figure 3-11 shows the cassava production trends in the study areas.

43

100,000

Sakon Nakorn Udon Thani

80,000

60,000

Planted Area (ha) Area Planted 40,000

20,000

0 1983 1986 1989 1992 1995 1998 2001 2004 2007 Year

Figure 3-10: Trend in cassava planting in the study areas (Source: Office of Agricultural Economics, Thailand, 2007)

Past Impacts of Climate on Agriculture

Despite the limitation on cassava acreage, there has been a gradual increase in cassava production. This increase is due to advances in agricultural technology. However, fluctuations in yields have occurred, with climate cited as a main factor in those fluctuations (Office of Agricultural Economics, 2005, 2006, 2007).

In 1998, the average temperature was significantly above the annual mean temperature while rainfall amounts decreased. This shift led to drought across

44 Northeastern Thailand and correspondingly reduced cassava harvests. The excessive rainfall during 2000–2002 was a factor in yield decreases during that period.

25.00

Sakon Nakorn Udon Thani

20.00

15.00

10.00 Harvested Yield (t/ha)

5.00

0.00 1983 1986 1989 1992 1995 1998 2001 2004 2007 Year

Figure 3-11: Cassava production trends in the study areas (Source: Office of Agricultural Economics, Thailand, 2007)

Chapter 4

Method and Data

In order to assess the impact of variations in climate change on agricultural production, a biophysical process-based model or crop model is used. In this chapter, I first show how the study areas––the Sakon Nakhon and Udon Thani provinces––were selected. Then I provide a description of the methodology used to direct the analysis and the required input data used to simulate the results for this study. The testing of the EPIC model is also demonstrated in this chapter.

Study Site Selection

Initially, this study estimated changes in potential cassava yields and physiological responses due to various climate change scenarios. Physical characteristics such as the features of the soil in which cassava is grown, were controlled in the study areas. The two provinces in the Northeast region with cassava acreage have similar soil characteristics, making them good choices for this type of study. Following is a discussion of the reasons for selecting these two provinces.

The Sakon Nakhon and Udon Thani provinces were the study areas. They were selected because they are representative of the 19 cassava production provinces in the

Northeastern region. The study areas are located between 17°22´–17°25´ N latitude and

102°25´–103°43´ E longitude and cover approximately 56,000 hectares. The criteria for

46 site selection were based on similarities in soil series in the cassava-growing portions of the two provinces and the completeness of the weather data collected by the Thai

Meteorological Department for the period 1978–2008. The latter data are important input for the EPIC simulation model used to generate an output result for this thesis.

The land use data set of 19 provinces in Northeast Thailand obtained from the

Land Development Department of Thailand (LDD) were used to locate the cassava plantation areas. In each case, the cassava area map updated in 2004 was then overlaid with a soil series map updated by LDD in 2002 to create specific soil maps on cassava cropland. By overlaying the cassava land use map on the soil series map, several different soil series located in cassava plantation area were revealed, such as Phon Pisai, Chakarat,

Ban Pai, Korat, etc. Then the cassava production area soil series was calculated and ranked.

Even though cassava production is dispersed throughout the entire area under consideration, not all the dominant soil is suitable for cassava cultivation. In selecting the soil series for the site, three main criteria were used: first, the prominent soil series had to be suitable for cassava production as listed in the LDD study report (2007). Second, the selected soil series had to be located in at least two provinces of the Northeast region. As this project heavily relied on daily weather data, the third important factor was that the study sites had to have complete daily weather data from 1978 to 2008.

Based on these three criteria, only two provinces––Sakon Nakhon and Udon

Thani––matched the requirements. The selected soil series for cassava plantings for both sites was Dan Sai (Ds), which is part of the Ultisols soil order (LDD, 2007). Dan Sai (Ds) is characterized by fine-loamy, kaolinitic, and isohyperthermic typic kandiustults and is

47 found in large areas of the cassava plantings in the Sakon Nakhon and Udon Thani provinces, respectively. Therefore these provinces were selected as representative of cassava plantings in Northeastern Thailand. A map of the study areas and weather stations is shown in Figure 4-1.

Methodology

The methodology for this study followed the seven steps of the IPCC impact assessment model. These steps involved: 1) defining the problem, 2) selecting the method, 3) testing the method, 4) selecting scenarios, 5) assessing impacts, 6) assessing autonomous adjustment, and 7) evaluating the strategies for the adaptation. As the objective of this project is to basically assess climate impacts on cassava production, only the first five steps were adopted in the study.

The basic assumption about climate impact assessment is that changes in climatic variations have a direct impact on cassava production and its physiology, which is the first biophysical exposure unit of the impact model. In order to evaluate this assumption, crop production is likely to be an appropriate indicator and is widely used in climate impact studies. Here, the EPIC crop simulation model was used to estimate cassava yield for the two study sites.

48

Figure 4-1: Weather stations in Northeastern Thailand (Source: The Meteorological Department, Thailand, 2007)

49 Using the model for the climate impact study, two sets of climatic data were required—the observed or historical climatic data and the projected climatic data.

Basically, the EPIC model requires information on temperature and precipitation as the main inputs. In this project two time scales of climatic data sets––the projected 30-year from 2009–2038 and the past 30-year from 1978–2008––were of interest. The projected climate change data sets from 2009–2038 as conducted by the CSIRO Division of Marine and Atmospheric Research, Australia and prepared for the IPCC-SRES report were used.

Next, cassava yield according to projected climate change was simulated to study possible future impacts. The 1978–2008 climate data set collected by the Thai

Meteorological Department was used to establish the baseline for ‘no climate change’ input for the EPIC model, and to validate simulated cassava yield against observed yield.

Results for simulated yield in the two study areas––Sakon Nakhon and Udon Thani provinces––were then used to extrapolate total cassava production in the northeastern region. Two sets of simulated yields due to present and future climate conditions were compared to assess the effect of climate change and its variations using statistical analysis. A diagram of the thesis methodology may be found in Figure 4-2.

Erosion Productivity Impact Calculator (EPIC) Model

The EPIC model (version 0509) is a biophysical process-based model that can simulate crop yield in response to changes in climate conditions. The model is used to estimate various kinds of crops––rice, maize, soybean, wheat, potato and so on. The model––downloaded from the Blacklands Research Centre in Temple, Texas––provides

50 sets of input data, such as soil, crop, crop management, and historical climate data, necessary for yield simulations for all U.S. farms. For this research, the data sets for any regions in Thailand were not available and thus were created for input from the EPIC model.

Since the EPIC model generates output on the real world, it requires a lot of input data. This intensive requirement is a limitation of this project with regard to using the

EPIC model––the data necessary to create the input files are not easily accessible.

Moreover, the data from the data sources must be converted into the required format for model input. The input data sets are comprised of program control codes, daily climatic data, weather station data, study site data, physical and chemical soil data, crop data, crop management data, operation schedule data, and operational codes. In order to operate the model, three main input files––daily weather and soil data and the operations schedule for each study site were required (see the detail of EPIC model in chapter 2).

51

Observed Projected climatic data climatic data from climate model

Projected changes in Observed daily statistics monthly statistics

Stochastic Weather Generator

Generated daily Generated daily weather for present weather for future climate changed climate

Impact Models

Climate changes/ Exposure units Impacts Climate variability

Figure 4-2: Diagram of methodology for this project (Adapted from Kates, 1991)

52 Input Data Required for the Model

Weather data

The EPIC crop model requires climatic parameters, such as temperature and precipitation, as the main inputs. Since EPIC can simulate crop yield response to climate conditions outside the historical time period, in this project two time scales for climatic datasets––the projected 30-year from 2009–2038 and the past 30-year from 1978–2008–– were of interest.

For the observed weather data, the baseline weather data for 1978–2008 were obtained from the Meteorological Department of Thailand. Only two weather stations located in the study areas were selected. The Sakon Nakhon weather station is located at

17°22´ N latitude and 103°43´ E longitude with the elevation of 172 ft. in the Sakon

Nakhon province. In the Udon Thani province, the weather station is located at 17°25´ N latitude and 102°59´ E longitude with the elevation of 187 ft. These two weather stations collect the daily weather data required by the EPIC model, such as maximum and minimum temperature, total precipitation, rainy days, relative humidity, and wind speed.

However, solar radiation data were not available from the two weather stations, so the climate generator (WXGEN) was used to simulate these missing data for the model input.

WXGEN is the EPIC’s built-in stochastic weather generator developed to generate a sequence of daily weather variables such as precipitation, maximum and minimum temperatures, and solar radiation for a period at a given location. In the absence of measured weather variables, WXGEN is used as an alternative means to satisfy the input requirement of the EPIC model. According to Wallis and Griffiths (1995), WXGEN

53 incorporates a first-order Markov chain technique (occurrences of wet and dry days) that requires monthly weather records to generate the daily weather variables. Because the monthly statistics of wet and dry days have a major impact on temperature and solar radiation, the daily solar radiation variables for 1978-2008 of this project were calculated from the observed rainfall data obtained from weather stations located within the study areas. The procedure for this model is described by Richardson (1981, 1984).

The projected weather data for the A2 scenario were taken from the CSIRO-Mk3 model developed by the British Climate Research Centre for the IPCC-AR4 report in

2007. The A2 and storyline for the CSIRO-Mk3 model demonstrated the plausibility of projecting the effects of greenhouse gas emissions based on different changes in population and economics at the regional scale. The development of the A2 scenario focused on significant increases in population, strengthening cultural identities, and slow economic development. The available records for monthly temperature and precipitation for the A2 scenario––downloaded from the IPCC Data Distribution Center (DDC)––were used to assess impact on cassava yield from 2009–2038. As the projected data were available at a monthly time scale, they were converted into daily weather data by using

WXGEN. The WXGEN first generated sequences of precipitation (wet or dry state) occurring for a one day period independent of the other variables. Daily maximum temperature, minimum temperature, and solar radiation variables were then calculated based on the previously generated wet or dry states (Richardson, 1984). Then the daily projected climate input data for 2009–2038 were applied to the baseline climate data

(1978–2008) to operate the EPIC model.

54 Soil Data

The EPIC requires at least seven parameters of soil data, such as soil texture

(percentage of sand, silt, and clay), bulk density, pH, field capacity, wilting point, water- holding capacity, and organic matters. Other soil parameters can be estimated by EPIC itself. Additionally, EPIC can accept up to 20 parameters for 10 soil layers. In this study,

Dan Sai (Ds), defined as the reference profiles for soil series in the cassava planting areas in the Sakon Nakhon and Udon Thani provinces, was applied as an input in the EPIC model. According to the Land Development Department of Thailand, the soil depth intervals in the Dan Sai soil series are 0-22, 22-42, 42-75, 75-97, and 97–145+ cm. The characteristics of this soil surface are fine-loamy soil with about 60% sand content and

17% clay, as well as low soil pH at 6.0. This soil has low fertility, is well drained, and has rapid permeability and surface runoff. The soil surface is deeply dry during the hot season.

Management Data

Management practices input data such as planting, harvesting, fertilization, tillage, and irrigation are required for the EPIC model. These management data must specify the timing of individual operations either by date or by fraction of the growth period. Based on these data, EPIC can simulate complex crop rotations with a variety of irrigation, fertilizer, pesticide, and tillage controls. Cassava requires optimal rainfall at

100–150 cm and optimal temperature at 25–29° C. The growth period for cassava is approximately 9–24 months.

55 The information on cassava management from planting to harvest was taken from both literature reviews and official documents of the Agricultural Department of

Thailand. For this project, the planting date was set for the beginning of May and harvest on the first day of March (eight months after planting). Before planting, the land is prepared with tractors or bullocks, and cassava growth space is about 1x1 m. Fertilizer is applied twice––once at 1 month and once at 3 months of age at a volume of 50–100 kg. per rai. The formulas for fertilization suggested by the Department of Agricultural

Extension, Thailand (N:P2O5:K2O = 2:1:2) are 16-8-16 or 15-15-15.

Model Validation

As this impact study relied heavily on simulation model results, the EPIC model must be validated in order to assess its performance before simulating the results.

Simulated cassava yields were compared with historical yield data recorded by the Office of the Agricultural Economic Department, Thailand during 1983–2007 using baseline data. The baseline data were the historical climatic data recorded by the Thai

Meteorological Department; crop management parameters––tillage and fertilizer—were based on work conducted by the Thai Department of Agriculture. Using this baseline data, the simulated yields for both study sites, Udon Thani and Sakon Nakhon, were calculated. Then the model was calibrated specifically to match each study site’s conditions in order to simulate yields close to those found in the historical data. The simulated yields are shown in Figures 4-3 and 4-4.

56 According to these figures, average actual cassava yields reported for 1983–2008 were 6.02 tons per hectare for Udon Thani and 5.53 tons per hectare for Sakon Nakhon.

However, EPIC simulated average yields for Udon Thani of 6.38 tons per hectare and for

Sakon Nakhon of 5.56 tons per hectare between 1983 and 2003. The simulated annual yields overestimated cassava for Udon Thani by as much as 0.5–1.9 tons per hectare between 1983 and 2003. In contrast, the figure was underestimated by as much as 2.5 tons per hectare after 2003. For Sakon Nakhon, the cassava yield was overestimated by

0.5–3 tons per hectare between 1983 and 2000, but after 2001, it was underestimated by about 0.13–0.99 tons per hectare. The underestimation of the simulated yields stemmed from increased actual yield, which may have been influenced by improvements in agricultural technology or changes in cassava demand over the last baseline period

(2003–2007).

57

10

9

8

7

6

5

4

3 1983 1986 1989 1992 1995 1998 2001 2004 2007

UDN Actual Yield UDN Simulated Yield

Figure 4-3: Actual cassava yield compared to EPIC simulated yield for Udon Thani

9

8

7

6

5

4

3 1983 1986 1989 1992 1995 1998 2001 2004 2007

SNK Actual Yield SNK Simulated Yield

Figure 4-4: Actual cassava yield compared to EPIC simulated yield for Sakon Nakhon

58 A paired t-test analysis was employed to evaluate differences between the mean of a simulated yield and the mean of an actual yield. The purpose was to show that the model was appropriate for reproducing historical cassava production and thus could be used to estimate yield over the next 30 years. The data used in this study met the requirements of the paired t-test method—the variables were normally distributed and homogenous as indicated by the Levene test of equal variances.

Table 4-1: Result of paired t-test for actual yield and EPIC simulated yield

Paired Differences Sig 95% T Df Pair (Actual – Mean Std SE (2-tailed) Confidence Simulated yield) Lower Upper Udon Thani 0.365 1.268 0.249 -0.147 0.877 1.47 25 0.155 Sakon Nakhon 0.031 0.992 0.194 -0.369 0.431 0.16 25 0.875

Even though the model generally overestimated crop yield, the difference between the means for simulated and actual yields was fairly small. Using a two-tailed test with a significance level of 0.05, both study areas had calculated t values lower than the tabulated t of alpha = 0.025, df = 25. As shown in Table 4-1, the calculated t of 1.47 for Udon Thani and 0.16 for Sakon Nakhon are small and lie within the 95% confidence interval; therefore, the null hypothesis cannot be rejected. In other words, the mean simulated yield and the mean actual yield did not differ. The performance of the EPIC model as well as its parameter setting were satisfactory for simulating cassava yields for the representative production sites in this study.

Chapter 5

Climate and Crop Scenarios

In order to estimate the impacts of climate change on cassava production in 2009–

2038, possible input scenarios were established. The two major scenarios emphasized in this thesis are: 1) CO2 scenarios, and 2) agricultural technology/practice scenarios. Each scenario includes two estimates. The first major scenario is concerned with a no-effect, or an effect of rising CO2 at the rate of 1% per year to double concentration; the second major scenario is concerned with current, or future agricultural technology and practice to maximize yields. The future agricultural technology/practice scenario was based on the assumption of advances in agricultural technology and practice that were likely to occur irrespective of climate change. These advances in agricultural technology and practice were not prompted by climate change per se, but rather by the broader demand for higher production levels.

Combinations of these estimates led to four scenarios that may cover the possible impacts of climate change on cassava production in this study. The four scenarios are listed in Table 5-1.

60

Table 5-1: Climate and crop scenarios Scenario Description Climate change with no CO effect (380 ppm), current agricultural 1 2 technology/practice (lower bound)

2 Climate change with CO2 effect (1% per year increase CO2), current agricultural technology/practice (lower bound)

3 Climate change with no CO2 effect (380 ppm), future agricultural technology/practice (upper bound)

4 Climate change with CO2 effect (1% per year increase CO2), future agricultural technology/practice (upper bound)

The details of the two scenarios––climate change and CO2 levels, and agricultural technology/practice––are described separately below.

Climate Change Scenarios

Climate scenarios

The baseline for the future climate change data set for the next 30-year period

(2009–2038) was taken from the CSIRO-Mk3 model. This climate model was developed by Australia, and contributed to the results for some international climate analysis reports, including some studies in the Southeast Asia region (e.g., IPCC, 2007). The

CSIRO climate change scenario contributed four emission scenarios––including A1, A2,

B1 and B2––that depend on different assumptions about future economic and political activities in the region, which would influence crop processes differently.

These scenarios (A1,B1,A2,B2) were developed in IPCC (2007) for the Fourth

Assessment Report (Special Report on Emissions Scenarios - SRES). The A1 scenario

61 incorporates “a future world of very rapid economic growth, global population that peaks in mid-century and declines thereafter, and rapid introduction of new and more efficient technologies”. The B1 scenario incorporates “a convergent world with the same global population as in the A1 storyline but with rapid changes in economic structures toward a service and information economy, with reductions in material intensity, and the introduction of clean and resource-efficient technologies” (IPCC, 2007). The population is expected to significantly increase by the year 2050 and subsequently decline. While the

B1 scenario incorporates a convergent world with the same global population as in the

A1 scenario but with rapid changes in economic structures toward a service and information economy, with reductions in material intensity, and the introduction of clean and resource-efficient technologies (IPCC, 2007).

B1 is more ecologically friendly and emphasizes global solutions to economic, and social as well as environmental stability, as compared to A1, which focuses on the technological aspects associated with energy sources. The A2 and B2 scenarios are characterized by a heterogeneous world with an underlying strengthening of regional cultural identities, more increasing focus on family values, more local economic development, continuously rising population growth, less rapid technological changes, and less improvement per capita income than in both A1 and B1 scenarios. The B2 scenario is more ecologically friendly than the A2 scenario (IPCC, 2007). Among these scenarios, the characteristics of the A2 scenario resemble the background of the study areas; I selected the A2 climate change scenario to represent the future baseline of climate conditions based on the similarity of the study areas.

62 The A2 scenario’s number of physical parameters was taken as the input for the

EPIC crop model and includes air temperature and total precipitation, and relative humidity according to a monthly time scale. These data could be downloaded from the data distribution center on the IPCC website. The CSIRO-Mk3 climate change data were collected for the entire world, employing grids of 1.875 degree latitude x 1.875 degree longitude. For the climate projection required for this thesis, the cell covering the study areas was selected. However, one grid cell may cover more than one local climatic region; as a consequence, the climate model not represent the local climate well enough

(Collier et al., 2004; Parry et al., 2004). In this case, an analysis and adjustment process for the local projected climate data was required.

The CSIRO-Mk3 model projected an increase in temperature and precipitation in the study areas, Udon thani and Sakon Nakhon, during 2009–2038. However this climate projection was noticeably lower than the observed historical climate during 1978–2008.

Therefore, the projected temperature data were adjusted by adding a fixed value computed from the 30-year monthly mean differences between simulated and observed climate data. By increasing the magnitude of the projected temperature, the local climate conditions should become more realistic. The predicted changes in annual mean temperature after the adjustment and annual precipitation are presented in Figure 5-1.

Under the A2 scenario, the mean precipitation for both study sites was projected not to change significantly from the baseline period (1979–2008). The mean annual precipitation was predicted to be of about 128.97 mm. The precipitation was predicted to have the greatest increase in the year 2006 and to have the lowest decrease in 2036. The

CSIRO-Mk3 model projected an increase in average mean, maximum, and minimum

63 temperatures of about 26.38, 33.59, and 26.38 °C, respectively. The increase in these temperatures ranged from 1 to 2 °C above the baseline period for Sakon Nakhon and

Udon Thani. The mean temperature was projected to have increase most––by about

28.5 ° C––in 2025 and the least––about 24.48 °C––during 2006–2027, when the greatest precipitation was projected (Figure 5-1).

Despite the similarity in the overall mean temperature and precipitation for both study areas, the magnitudes of the monthly precipitation and temperature changes differed over the two periods for Sakon Nakhon and Udon Thani. Figure 5-2 shows that the temperature in Sakon Nakhon from February–May was projected to gradually increase by 1.5–3 °C; the hottest month was supposed to be April, which was about 3.5°C above the baseline. The projected temperature increase in Udon Thani from January to

May was predicted to be about 1–3.5 °C higher than the mean temperature during the baseline period. The precipitation in Sakon Nakhon in May was projected decrease by 80 mm; however, Sakon Nakhon was predicted to receive more rain during the rainy season

(June–August). In Udon Thani, a greater decrease in precipitation in May and greater precipitation during the rainy season were projected.

Since the CSIRO climate data were provided on a monthly time scale for the selected 30-year period (2009–2038), the climate generator included in the EPIC crop model was used to develop a daily time scale for climate data input. This daily climate input was later used both as an independent scenario and in combination with the CO2 scenario to enable meaningful comparisons for each study area.

64

Figure 5-1: CSIRO-Mk3 projected annual mean climate data for Udon Thani (upper) and Sakon Nakhon (lower) (Source: CSIRO-Mk3 model: A2 scenarios, Australia).

65

Figure 5-2: CSIRO-Mk3 projected annual means for monthly climate data for Udon Thani (upper) and Sakon Nakhon (lower) (Source: CSIRO-Mk3 model: A2 scenario, Australia)

66

CO2 scenarios

CO2 fertilization effects are well known for their direct and indirect impacts on plants’ physiological processes and crop productivity (Kimball, 1983; Wolfe and

Erickson, 1993). A number of researchers suggest that elevated CO2 induces the net rate of photosynthesis by increasing the CO2 concentration gradient from air to leaf and by reducing the loss of CO2 through photorespiration which results in the increase in carbon uptake for plant photosynthesis. The elevated CO2 also simulates the reductions in stomatal conductance of plants. The reduction limits the rate of photosynthesis, but it increases plant water-use efficiency (WUE) of the leaf. This impact is considered an indirect benefit of elevated CO2 that either increases or maintains plant photosynthesis in water-stressed conditions (Ainsworth and Long, 2005; Ziska and Bunce, 2006). However, the magnitude of the plant responses to elevated CO2 concentrations and biomass production are significantly greater in C3 plants (the majority of tropical plant species including cassava) than C4 plants (e.g. glasses, maize, sugarcane, sorghum) that are better adapted to cope with the drought or CO2 limited environment. Full details of C3 and C4 plants are described by Ziska and Bunce (2006).

This study employed two different CO2 scenarios that were combined with the

CSIRO-Mk3 climate change projection to assess potential CO2 impacts and demonstrate plant responses to elevated CO2. The first CO2 scenario used the current level of CO2 at

380 ppm for the baseline CO2 level, assuming no CO2 effects on crop production. The other CO2 scenario followed the CSIRO-Mk3 projection that simulated future climate data based on 1% per year to double CO2 levels from this baseline value. Under this

67 circumstance, the levels of CO2 concentrations during the 30 year period for this study did not exhibit the same level in each year.

For the first CO2 scenario, CO2 = 380 ppm was used as a future baseline scenario which indicated a no CO2 effect. The 1% per year increase in CO2 from baseline levels indicated climate change with CO2 effects. Since the focus here is on the 30-year climate change (2009–2038), the model showed a final CO2 increase from 380 ppm in 2009 to

517.30 ppm in 2038, which is lower than the fixed CO2 level included in several impact studies that used fixed CO2 levels higher than 520 ppm. However, it should be noted that an actual CO2 level associated with future climate change may reflect gradual increases each year; the level of CO2 in the year 2038, under the 1% per year CO2 scenario, may be less than 520 ppm. Using the fixed level of CO2 in the simulation for the entire 30 years may lead to an overestimation of overall yields. Thus, the application of dynamic CO2 levels rather than fixed CO2 levels is reasonable.

For these reasons listed previously, CSIRO-Mk3 climatic data with a fixed

CO2value of 380 ppm and dynamic rising CO2 were applied to climate and CO2 parameters in the EPIC simulation model to simulate yield output and crop processes under two CO2 scenarios––climate change with, and without a CO2 effect, respectively.

Crop Scenarios

Assuming that the higher demand for cassava production is likely to occur in the future, this may lead to the advancement in agricultural technology and practice. Several studies have suggested that the advances in agricultural technology and practices could be

68 in the forms of: technical innovations, plant cultivars, irrigation system management, cropland shifting, etc. However, information on future technology/practice is now unavailable, the reasons for which are outside the scope of this thesis research, which is an examination of trends in climate change impacts on cassava production. Therefore, for this study I adjusted crop parameters, simulating the adjustment under two agricultural management scenarios––one with current, and one with ideal or future cassava technology/practice. The ideal cassava scenario takes into account the expected advances in future agricultural technology and practice that maximize cassava production. As indicated in the introduction of this chapter, these agricultural changes represent the advancements in technology and practice likely to occur irrespective of climate change.

The detail of parameter adjustments for both current and future agricultural technology/practice scenarios are described in the following section.

Current crop technology/practice is referred to as the lower bound scenario and the future crop technology/practice scenario for cassava will be referred to as the upper- bound scenario. Each includes different sets of crop parameters for the EPIC simulation model. The lower bound baseline scenario that represents current agricultural technology/practice in the study areas included sets of cassava crop parameters initially established in the EPIC model. These parameters were derived from a rigorous experiment and could be used to identify the basis of plant functions. However, minimal changes to these parameters, and other operating values have been made to calibrate the model, and to simulate baseline yields that are similar to actual yields from 1978–2008.

The EPIC crop parameters for the lower-bound scenario are shown in Table 5.2. For the upper bound scenario, the adjustment parameters for cassava that would lead to increased

69 yield were derived from the literature (Cock et al., 1979; Hillocks et al., 2001), and conversations with experts. According to Cock et al. (1979), the ideal cassava plant is pest-tolerant, has a high root distribution factor, and has a high leaf area index (LAI) up to 4. Thus, the plant growth rate parameters in the upper-bound scenario were set at a high level. Moreover, the parameters for crop harvesting and pest damage can be adjusted to increase simulated yields. The lists of possible changes due to advances in technology

/practice that may be anticipated over the next 30 years are provided in Table 5-2.

Table 5-2: EPIC crop parameters adjusted for agricultural technology/practice scenarios Agricultural scenarios Variables Definitions Units Lower bound Upper bound WA Biomass-Energy ratio - 15 15 HI Harvest index Kg kg -1 0.95 0.99 DMLA Maximum potential leaf area - 2.7 3.0 index RWPC1 Fraction of root weight at - 0.4 0.6 emergence RWPC2 Fraction of root weight at - 0.6 0.8 maturity HE Harvest efficiency - 0.9 0.9 ORHI Overrides simulated harvest - 0.9 0.99 index (HI) PARM (3) Water stress harvest index 0.5 0.7 PARM (10) Pest damage cover threshold, t/ha 1 99 crop residue above ground biomass

In this simulation, cassava was planted on the 1st May each year, which was marked as the onset of the rainy season when most farmers were likely to start planting, and harvested on the 7th March, eight months after the planting date. Cassava crop management parameters such as fertilization, weeding, and harvesting practices were used as suggested by the Department of Agricultural Extension of Thailand. Since this

70 thesis research focused mainly on potential changes in crop yield due to future climate conditions, several parameters for soil series, and land condition were held constant, assuming: no change in land characteristics, no cropland shift, and no land erosion from the baseline period. Moreover, the cassava crop management parameters, such as plant schedule, fertilization, and weeding, were also held constant. In other words, in addition to crop parameters these sets of parameters for the lower bound and upper bound scenarios were the same.

Chapter 6

Crop Yield and Responses for Various Scenarios

In examining cassava crop responses to climate variables and the impact of future climate change on cassava production in Northeastern Thailand, the elements of a climate-crop impact assessment study were used in this study. As discussed in chapter 2, these elements included: a climate event, exposure unit, and analysis of the biophysical impact on the plant in question. In this study, the climate event was represented by the

CSIRO-Mk3 projection changes in climate variables during 2009–2038. The exposure unit was the cassava production in two study areas of Northeastern Thailand, and the biophysical impact focused on changes in cassava plants’ biophysical responses to climate change and yield changes due to interactions among temperature, precipitation, and elevated CO2 concentration effect.

The two major scenarios employed as the model inputs for this impact study included climate variable change scenarios and agricultural technology/practice scenarios. For each scenario, there were two estimates: the first scenario contained one with, and one without the CO2 fertilization effect, and the second scenario contained one with current crop technology/practice and one with future crop technology/practice scenarios for maximum cassava production.

This analysis was based on the future 30-year (2009–2038) climatic change period as predicted using the CSIRO-Mk3 climate model from Australia. The projection was predicted based on 1% per year increases in CO2 concentration. The ideal cassava

72 scenario established in this study followed the study of Hillocks et al. (1979), and served as the upper bound. The current crop technology/practice scenario was used as the lower bound. Yield changes determined via the EPIC crop model resulted from the integration of climate scenarios and crop technology/practice scenarios—the upper bound and lower bound practice scenarios and the fixed-changed CO2 concentration scenarios (CO2=380 ppm for without CO2 effect, and the increased 1% per year for CO2 effect to about 517 ppm) for the two study areas. The difference between baseline yield (actual yield) during

1983–2008, and simulated yield during 2009–2038 was then computed, and compared to assess climate change impacts on cassava yield. This chapter explores the integration, and results of climate impact on cassava yield.

Effects of Climate Change and CO2 on Cassava Yield

The effect of climate change, and CO2 fertilization on cassava yield was analyzed with current agricultural technology/practice based on scenarios 1 and 2. CO2 fertilization was considered here because the CSIRO-Mk3 climate model was projected based on 1% per year to a double CO2 level. This projection was used to assess the influence of a change in CO2 concentration on cassava’s photosynthesis, and on cassava as a C3 photosynthesis pathway crop sensitive to CO2 concentration. Using the EPIC crop model, the interaction between climate variables, including CO2 fertilization and the cassava crop, was demonstrated as follows.

73 Effect of climate variables on crop yield

The EPIC model was used to estimate the average cassava yield under scenario 1

(no CO2 effect with current agricultural technology/practice) during 2009–2038––the result was about 4.69-4.73 ton/ha. Under this scenario the EPIC simulated yield ranged from a decline of 28% for Udon Thani to a decline of 16% for Sakon Nakhon. The differences between average yields for both study areas may be related to differences in projections from the CSIRO-Mk3 climate models described in chapter 5. The least negative effect was estimated for Sakon Nakhon, where lower maximum temperature during the growth period compared to the baseline period was projected to increase.

The result for crop yield response to rising temperature without beneficial CO2 effects agrees with several climate impact studies in which that a small increases in temperature can be detrimental to crop yields (Matthew et al., 1995; Parry et al., 2004).

The explanation of this impact is that higher temperatures shorten crop growth periods

(e.g., filling, flowering, and harvesting). As the growth period becomes shorter, the plant reaches a mature stage before adequately receiving nutrients needed to produce the yield.

The consequence is a reduction in plant production. Maximum temperature, therefore, plays a crucial role in determining cassava yield as shown in Figure 6-1.

74

Figure 6-1: Changes in cassava yields from baseline, simulated based on scenario 1 (baseline period using CO2 at 330 ppm)

Effects of CO2 on Crop Yield with Current Agricultural Technology/Practice

According to the IPCC (2007), increases in atmospheric CO2 concentrations can mitigate crop production losses due to warming temperatures. This study analyzed cassava yields in response to CO2 effects under the projected 1% per year increase in CO2 levels (scenario 2). The results show that cassava yields in the Udon Thani and Sakon

Nakhon provinces were estimated to be about 5.58 and 5.61 tones/ha, respectively. For the period of 2009–2038, the EPIC model predicted a slight decrease in yield from the baseline yield of about 7% for Udon Thani, but a minimal increase in yield of about 1.5% for Sakon Nakhon (Figure 6-2). Again, the different changes were anticipated based on differences in projected changes in climate conditions between the two study areas.

75

Figure 6-2: Percentage changes in cassava yields from baseline, with and without CO2 effect scenarios (baseline period CO2 = 330 ppm)

Similar to the result for scenario 1, the effect on cassava yields under scenario 2 was negative. However, the negative impact was less severe under the 1% per year increase in CO2 level. Nonetheless, the CO2 effects on cassava yield in this scenario did not fully overcome the losses and did not indicate the substantial increases indicated in several research studies on grain yield and rising CO2 levels. These studies have indicated likely increases in most grain yields of at least 10% (Matthew et al., 1995; Parry et al.,

2004). For this study, using the 1% per year CO2 increase scenario, cassava yield was predicted to only slowly increase from the baseline. This result indicates that CO2 fertilization effects could mitigate the detrimental impact of climate change on yield, but this factor alone may not substantially improve cassava yields in a short period of time.

However, it is important to note that most cassava production in Northeastern

Thailand occurs in water-limited areas. Cassava is generally grown in dry marginal areas

76 that receive little precipitation relative to other agricultural regions of Thailand. Thus, the combination of poor conditions for land cultivation, and unfavorable projected climate conditions––heat or water-stressed conditions—determine the extent of the crop yield. As these events dominate the direct CO2 fertilization effects on plants, negative or constant yield can be expected despite increases in CO2 concentration (Ziska, 2003; Sahaschai,

2001). According to study results, CO2 effects alone may not overcome the negative impacts of climate change without an additional factor; I developed two other scenarios to assess yields by adjusting for changing agricultural technology/practice likely to occur regardless of climate change. This factor was used to simulate yield with two CO2 scenarios, one with, and one without CO2 effects. The results are reported in the following sections.

Effects of Climate Variables, CO2 with Future Agricultural Technology/Practice

It is assumed that appropriate improvement in agricultural technology/practice will maximize productivity in the future. The EPIC model was re-run with a different set of crop growth, and management parameters assuming advances in agricultural technology/practice that are likely to occur irrespective of climate change. This section provides the results of crop yield changes derived from scenario 3,––climate variables with no CO2 effect, and the upper-bound agricultural technology/practice scenario, combined.

For this scenario, cassava production were originally estimated to be about 4.6–

4.7 ton/ha during the 2009–2038. However, after considering the advances in agricultural

77 technology/practice, the simulated yields were projected to be about 7.7–7.9 ton/ha.

These changes increased from the baseline yields in Udon Thani and Sakon Nakhon by approximately 22% and 30%, respectively, indicating that the advances in agricultural technology and practice may compensate for the negative impacts of climate change on cassava yield. The comparison of crop yields simulated with current and future agricultural technology/practice is shown in Figure 6-3.

40 30 20 yield 10 in

0 ‐10 changes

% ‐20 ‐30 ‐40 Udon Thani Sakon Nakhon

Current agricultural scenario Future agricultural scenario

Figure 6-3: Percentage change in cassava yields from baseline, current and future agricultural technology/practice scenarios

Effects of Climate Change, CO2, with Future Agricultural Technology/Practice

In this section, the EPIC model was used to simulate cassava yield by integrating all possible impacts on crops, including climate change, the CO2 fertilization scenario, and future agricultural technology/practice (scenario 4). Figure 6-4 shows that the CO2

78 fertilization effect associated with advances in agricultural technology/practice may boost cassava production in both study areas.

Figure 6-4: Percentage changes in cassava yields from baseline, future agricultural technology/practice with and without CO2 effect scenarios (baseline period CO2 = 330 ppm)

Under scenario 4, cassava yields for both study areas were estimated to be about

8.49 and 8.53 ton/ha. The amount of the yield under future crop technology/practice with elevated CO2 scenario was greater than that for the future crop technology/practice with no CO2 enrichment scenario. Moreover, when comparing this result to baseline yield in

1983–2008, the yields relatively increased by 29% in Udon Thani and by 35% in Sakon

Nakhon in 2009–2038 under the 1% per year rising CO2 concentration and future crop technology/practice, as expected.

The result generated under scenario 4, indicates that future crop technology and practices may improve cassava production whether there is a gain from CO2 fertilization or not. The improvement of agricultural technology/practice, even without specifically dealing with the climate change, is needed to overcome small yields in the future.

79

Figure 6-5: Changes in cassava yields as simulated via four scenarios

80 The comparison of yields simulated from four scenarios is shown in Figure 6-5.

As exhibited in the figure, the highest positive yield was simulated under the combination of rising CO2 concentration with future agricultural technology/practice. The lowest yield was simulated from the scenario that took no account of CO2 effect with current agricultural technology/practice. The combination of using current agricultural technology/practice, and the absence of CO2 effects may reduce yield losses; however, the overall yields may either increase, or remain similar to the baseline yield. Therefore, even with current or future agricultural technology/practice, cassava production under future climate change conditions could still occur.

Effect of CO2 Fertilization on Crop’s Water Relations

Most crops respond to elevated atmospheric CO2 by adjusting the photosynthesis rate which determines overall crop yield (Kimball et al., 2002). Under multiple stresses such as heat stress or limited availability of water resources, crops become sensitive to climate changes and elevated CO2 concentration. Crops respond to these changed circumstances by adjusting evapotranspiration rates or water use relations, which indirectly determine crop yields (Ainsworth and Long, 2005; Morrison et al., 2006). This study employed the EPIC model to simulate crop water use variables, including crop available water (CAW), evapotranspiration (ET), and water use efficiency (WUEF) to demonstrate the interactions between plants’ responses to climate changes and elevated

CO2 concentrations during the growth period. The EPIC result was simulated from the combination of climate change with two CO2 concentration levels and the agricultural

81 technology/practice scenarios. For the remainder of the discussion, only the upper-bound scenario is considered here with an assumption that agricultural technology/practice will certainly be improved to broaden the demand of cassava in the future. Table 6-2 shows the EPIC’s simulated values for crop water use variables under CO2 concentrations at the current level (without CO2 effect) and 1% per year CO2 increase (with CO2 effect) scenarios.

As shown in Figure 6-5, the EPIC simulation revealed that the available water

(CAW) for crops in 2009–2038 is likely to decrease from the baseline period by 21–30% in both study areas. Taking into consideration the projection of the CSIRO-MK3 model, both warmer temperature and the decrease in plants’ available water indicate water- stressed conditions in the areas. The water-stressed conditions may be detrimental to yield by increasing the plants’ water demands and decreasing direct CO2 fertilization effects on plants’ photosynthesis (Kimball et al., 2002). Under this condition, the indirect effects of CO2 play a crucial role in plant water relations. The interaction is demonstrated in the following paragraph.

In the no CO2 effect scenario, percentages of evapotranspiration (GSET) in the

Udon Thani and Sakon Nakhon provinces decline from the baseline by 19% and 24%, respectively. Taking into account the rising CO2 effects, declining percentages of GSET in Udon Thani and Sakon Nakhon of about 20% and 25% were predicted. The decreased evapotranspiration was due to plants’ responses to higher CO2 levels associated with warmer temperatures. The response involves conducting stomatal conductance to mitigate heat stress, reducing the transpiration rate of plants (Kimball et al. 2002).

82

Figure 6-6: Percentage changes in crop-water relation impacts in response to CO2 effects

83 The water use efficiency (WUEF) of cassava is another plant water use variable that is estimated to increase from the baseline period by 12% for Udon Thani and by 25% for Sakon Nakhon. The 1% increase in CO2 scenario demonstrated an increase in plant water use efficiency over the no CO2 effect scenario in both study areas. The result indicates that under heat stress, the cassava plant benefits from elevated CO2 fertilization by improving plant functions relating to evapotranspiration and water use efficiency.

Plants’ physiological responses to elevated CO2 in this study agreed with results reported in the literature (Kimball et al., 2002; Ziska and Bounce, 2006; Easterling et al.,

2007). According to the literature, under stress conditions, the overall impact of an elevated CO2 effect relates to stomatal conductance and changes in plant water use.

Rising CO2 stimulates the reduction of stomatal aperture and conductance, which reduces the plant’s evapotranspiration (ET) rate and subsequently improves the plant’s water use efficiency (WUEF). The results for this study confirm this fact, as seen in the decreased evapotranspiration and increased water use efficiency when crops were experiencing decreases in water supplies in both study areas. Ziska and Bunce (2006) suggested that the effect of CO2 –induced improvement in WUEF could be expected to either increase or maintain photosynthesis (crop yield) under water-limited conditions. This fact may reveal the reason why the extent of stimulated cassava yield in scenario 2 did not improve as expected despite growing under elevated CO2 conditions. Besides the direct effects of climate and CO2 fertilization on crop yield, the study of CO2 indirect effects on plant- water relations is also necessary to examine in impact studies when focusing on a water- stressed region or marginal planting area.

84

Relations between Climate Change and CO2 Affect Crop Yield

This section explores the relationships among climate change and CO2 effect on plants’ response variables to gain a better understanding of how future climate changes may impact cassava plants’ bio-physiology and thereby determine yields. The plants’ bio- physiology adjustments in response to climate variables and CO2 effects focused on plant-water relation factors simulated via the EPIC model, including evapotranspiration

(GSET), available water (CAW), and water use efficiency (WUEF) during the growing season. The descriptive statistical method employed in this section is correlation analysis.

A correlation analysis was conducted to study the effects of plant water relation factors and climate variables under elevated CO2 concentrations on crop yield. The plant-water variables’ responses to current CO2 concentrations in the 380 ppm and 1% per year increase CO2 scenarios were analyzed; the results are shown in Table 6-1.

Table 6.1 shows that cassava yield and maximum temperature under the 1% per year increase CO2 scenarios were negatively correlated at a significance level of 0.05 for

Udon Thani and Sakon Nakhon. This indicates that higher temperatures are associated with decreased yields. This result stands in contrast to the relationships between cassava yield and precipitation in which increased yields are associated with increased precipitation. However, the association between yield and climate variables (temperature and precipitation) was not directly established by a linear relationship, as the strength of the correlation is quite low (r < 0.5). Crop yield is poorly predicted by using temperature and precipitation variables if the crop is grown in water-stressed environments. Other

85 variables that determine crop yield, such as plant-water relations in response to elevated

CO2, have to be taken into account.

Table 6-1: Correlation between cassava yield and climate/ crop-water relations variables Udon Thani Sakon Nakhon Variables r p-value r p-value Rainfall 0.297 0.111 0.283 0.130 Maximum Temperature -0.368 **0.046 -0.388 **0.034 Evapotranspiration 0.547 **0.002 0.570 **0.001 Water Use Efficiency 0.912 **0.000 0.901 **0.000 Crop Available Water 0.237 0.208 -0.038 0.841

Note: * Significance at 95% confidence level ** Significance at 99% confidence level

There is more likely to be a relationship between crop yield and plant-water relations––evapotranspiration and water use efficiency––during the growing season

(approximate r > 0.5). The correlations between crop yields and evapotranspiration rate and water use efficiency in both study areas are positive at a significance level of 0.05.

Water use efficiency has the strongest correlation with cassava yield at approximate r =

0.9, and evapotranspiration has a moderate correlation with yield at approximately r =

0.5. The result indicates that increased cassava yields are associated with lower temperatures, but increased evapotranspiration rates and greater water use efficiency.

This phenomenon occurred in both study areas.

Subsequently, multiple regression analysis was conducted to determine how changes in several variables were influenced by increased CO2 concentration and associated with changes in crop yield. Two sets of variables, climatic variables (TMax, and Rainfall) and crop growth factors (GSET, and WUEF), that showed a strong

86 correlation to yield, were analyzed both within the climate variables only and across the two variable sets. In the regression analysis, cassava crop yield was set as a dependent variable, and the two sets of variables were set as independent variables. The results of the multiple regression analysis are shown in Table 6-2.

Table 6-2: Multiple regression analysis results (1% increase CO2 + upper-bound scenarios) Variable groups R2 F p Udon Thani 2 Climate variables (TMax + Rainfall) .152 2.42 0.108

TMax + GSET + WUEF .992 1133.86 ** 0.000

Sakon Nakhon 2 Climate variables (TMax + Rainfall) .162 2.58 0.094

TMax + GSET + WUEF .992 1070.46 ** 0.000

** Significance at 95% confidence level

Contrary to expectations, the set of climate variables had low R2 values in both study areas. The set of climate variables accounted for only about 15%––less than the set of crop water relation factors, which accounted for more than 95%. This indicates that linear regression may not be suitable for predicting the impact of climate variable changes on cassava yield. The changes in annual climate data do not simply result in changes in crop yield in all cases.

According to Table 6-2, the regression revealed quite a good fit in the combination sets of temperature and crop water relation variances. The combined

87 relationship between the climate variable, maximum temperature and plant-water

2 relations due to elevated CO2 effect showed a high coefficient for determination (R =

99.4, p=0.000). The set of temperature and plant-water relation factors had a linear relationship to cassava yield at a significance level of 0.05. Therefore, the possible regression equations for determining cassava yield are equation A ( for Udon Thani) and

B (for Sakon Nakhon). Using equation A, cassava yield in Udon Thani is predicted to increase when maximum temperature, water use efficiency, and evapotranspiration rate increase by about 0.002 °C, 0.639, and 0.0128, respectively. For Sakon Nakhon, cassava yield is predicted to increase when maximum temperature, water use efficiency, and evapotranspiration rate increase by about 0.01 °C, 0.639, and 0.0131, respectively.

UDN Yield = - 8.24 + 0.0028 Tmax + 0.639 WUEF + 0.0128 GSET --- A

SKN Yield = - 8.70 + 0.0111 Tmax + 0.639 WUEF + 0.0131 GSET --- B

In summary, in order to determine cassava crop yield for this study, plants’ water relation factors, such as evapotranspiration, water use efficiency, and available water, must be taken into account. As discussed in previous sections, CO2 affects plants’ water relations indirectly under unfavorable growth conditions. The climate variables— temperature and precipitation—cannot be used directly as the factors in predicting or assessing change in potential cassava yield.

Chapter 7

Conclusion

Simulations of future cassava production in Northeastern Thailand using the EPIC model are expected to be affected by the CSIRO-Mk3 projected change in climate. As projected, the 1-2 °C increase in temperature could decrease yields, especially in cassava production in marginal areas. For this impact study, the EPIC model simulated a reduction in yield for over 28% of the study areas within 30 years, under the CSIRO-Mk3 climate change scenario. The declines were lower when possible increases in CO2 fertilization effects in the atmosphere were taken into account. The CO2 effect may fully offset the negative impact on Sakon Nakhon but partially offset the loss for Udon Thani.

The future advances in agricultural technology and practices may actually offset climate impacts as yields are expected to increase by 30% from the baseline yield. The lowest decrease in yield occurred when future agricultural technology advanced and CO2 effect scenarios were combined; the simulation projected a greater than 35% increase in cassava yield than would be possible without the adjustment at both study areas under this combination of scenarios. In addition to the benefit of CO2 effects on yield, the EPIC model revealed that cassava grown in water-limited areas would gain indirect benefits from increases in CO2 fertilization effects. Cassava would likely respond to rising CO2 by decreasing its evapotranspiration rate and increasing the efficiency with which it used water when water availability was low.

89 The study findings offered an optimistic outlook for cassava production in the

Northeast region of Thailand over the next 30 years, both current and future agricultural technology/practice. It was shown that the cassava root crop could survive projected climate change and increasing CO2 concentrations and be more productive compared to the case during the baseline period, especially with advances in agricultural technology/practice that are anticipated to occur in the future despite no occurrence of climate change, but rather by the higher demand on cassava production. Therefore, this study made a good start towards assessing possible climate impacts on current and future cassava production as well as the scope of the potential maximizing of yield for the regions. Moreover, it is important to note that this study considered only the marginal areas of cassava production within a specific soil series; such a focus may limit the geographical distributions of climate variables and their impacts. In order to gain a better understanding of the impacts of climate on cassava production, future research should take into consideration the diversity of soil characteristics and regional plantation areas.

Although there is a favorable and optimistic assessment on the potential change of cassava yield due to the climate variable impacts, the results in this project are not meant to be predictions and can be suspected. The suspected cause of inaccuracy may be due to limitations from the model’s simplicity, hidden impact factors, and the uncertainty of the data implemented in this analysis. For the first limitation, in order to simulate crop yield, the number of source of data such as crop, soil conditions, weeds, diseases, pests, and crop schedule, were calibrated to create the yield as accurately as that which the current baseline yielded. However, for the projected yield production, these source data

90 were simplified in order to estimate the expected yields, under the assumption that there will be possible changes in agricultural technology and practices for the next 30 years.

The values of input parameters such as soil conditions, weeds and disease, etc. were to maximize the yield. In fact, the actual parameters of future agricultural technology/practices would happen are likely to be different from the parameters of agricultural technology/practice scenarios used in this project. Consequently, the simulation model might either over or under estimate the expected yield.

The second limitation is the structure of the cassava production system. The cassava in Thailand is mainly consumed for a non-food sector: its production is strongly influenced by the market demand and crop price system. During the low root price, the farmers consider leaving the cassava underground and postponing the harvesting until the next crop year, or converting the cassava acreages to grow a higher competitive cash crop such as sugarcane. This factor is a significant component that determines the degree of yield, and the judgment on the agricultural decision policy which may have to be taken into consideration for a future assessment study.

Therefore, I would include these issues in my future study in order to investigate some of the possible trends and responses to climate change that will represent the cassava production as it might happen in the next 30 years. The additional scenarios should be constructed for the regional agriculture, and manipulate EPIC input parameters that anticipate the possible changes in future agricultural technologies due to climate change. The extension of this project will beneficial not only for understanding the crop

91 responses and the possible adjustments to climate, but also for providing the adjustments/ adaptations and ideas related to climate change in the next 30 years.

92

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Appendix

Adjustment of the CSIRO-Mk3 Climate Data

This project used the projected climate data (2009-2038) which obtained from the

CSIRO-Mk3 model. Despite a projected increase in temperature at the study areas during

2009-2038, the climate projections from period 2009-2038 was noticeably lower than the observed historical climate during 1978-2008 (Figure 1-1). The CSIRO-Mk3 also projected the lower temperatures for the study areas during 1978-2008 compared to the observed historical climate during that time.

Figure 1-1: Comparison of the CSIRO-Mk3 projected maximum temperature and the observed historical temperature before data adjustment.

Therefore, the projected maximum and minimum temperatures were adjusted by adding a constant value computed from the 30-year monthly mean differences between the projected and observed historical climate data from 1978 to 2008. By adding the

103 constant value to increase the magnitude of the projected temperature from 2009 to 2038, the predicted local climate conditions should become more realistic.

In order to find a constant temperature value for each month, the differences between the observed historical maximum temperatures and the CSIRO-Mk3 projected temperatures of each month from 1978 to 2008 were calculated. Then, monthly mean differences were calculated by dividing total years with the sum of monthly difference values. This method was used for adjusting the surface temperature and maximum temperature.

Constant value for the monthly maximum temperature

= ∑ 1978-2008, y [observed max temp year x, month y – projected max temp year x, month y ] / 30

The monthly mean difference of each month was used as a constant value that was added to the projected maximum temperatures from 2009-2038.

New projected max temp year x, month y

= constant value month y + projected max temp year x, month y

However, the CSIRO-Mk3 does not provided the minimum temperature dataset, so a constant value was calculated based on the mean differences between the observed maximum temperature and the observed minimum temperature during that period.

104

Figure 1-2: Comparison of observed maximum temperature and projected maximum temperature after adjustment.

The constant value for adjusting the projected minimum temperatures was calculated based on the differences between the observed maximum temperature and the observed minimum temperature of each month from 1978-2008. Then, monthly mean differences for each month were calculated by dividing total years with the sum of monthly difference values.

Constant value for the monthly minimum temperature

= ∑ 1978-2008, y [observed max temp year x, month y – observed min temp year x, month y ] / 30

The monthly mean difference of each month was used as a constant value that was minus to the projected monthly minimum temperatures from 1978 to 2038 to arrive at the appropriate projected minimum temperatures.

New projected minimum temp year x, month y

= projected max temp year x, month y - constant value month y

105 The predicted changes in annual mean maximum and minimum temperatures after the adjustment are presented in Figure 1-3.

Figure 1-3: Comparison of observed minimum temperature and projected minimum temperature after adjustment.