In Thailand, the Alternative Energy Development Plan has set the target to increase the use of bioethanol to 9.00 million liters per day by 2021. To achieve this goal, both freshwater availability for energy crops and best practices in bioethanol production chain management are very important issues. Therefore, this study integrates water footprint technique with the linear programing approach in order to optimize the operations decision, focusing on water footprint of the bioethanol production chains from both tactical and operational levels. A cradle-to-grave approach is adopted to evaluate the water consumption and pollution in bioethanol production from sugarcane and cassava. The results show that the water footprint of bioethanol consumed in Thailand was about 3.23 × 109, 1.72 × 1010, and 2.49 × 1010 m3 per year in 2010, 2016, and 2021, respectively. The share of agriculture water consumption to the total water footprints of bioethanol was 99% and industrial water consumption was 1%. After applying the linear programing, it was found that the water footprint could be reduced by at least 53%, or 1.33 × 1010 m3, annually. The modeling approach and formulation presented could be used as a tool to reduce water consumption and provide the operation plan of bioethanol production chain.

INTRODUCTION

The Thai government launched a strategic plan of alternative energy in 2004 which boosted bioethanol production in Thailand from 0.37 million liters per day in 2006 to 1.84 million liters per day in 2012. ‘Alternative Energy Development Plan: AEDP 2012–2021’ was then launched and set a target to produce bioethanol 9.00 million liters per day by 2021 (Department of Alternative Energy Development and Efficiency, Ministry of Energy 2012). To achieve the AEDP target, one of the most important factors is freshwater availability. Freshwater is essential for crop cultivation and bioethanol production processes. Conversion of biofuel crops to bioethanol includes two major stages: first, the agricultural stage (biofuel crops production from field level) and second, the industrial stage (processing of biofuel crops to bioethanol). The water resource availability in Thailand is considered highly adequate in statistical terms. In reality, the water resource is unevenly distributed and water stress situations are always happening in some regions of Thailand, but the cause of this situation occurs because the runoff storage is not good enough. Thailand can store only 36% of the annual runoff. Besides, inefficient use of water by various sectors in Thailand, especially the agricultural sector which is the largest user and accounts for 61% of total available water, and deteriorating of water quality could create a serious problem (Office of the National Economic and Social Development Board 2012). Moreover, freshwater availability on earth is limited, it is important to know how it is allocated among the various purposes, such as water for nature versus food, water for food versus energy, or water for basic needs versus luxury goods (Hoekstra et al. 2009). The idea of considering water use along supply chains has gained interest after the introduction of the ‘water footprint’ concept by Hoekstra et al. (2011). The number of applications has rapidly increased in many countries (Hoekstra & Chapagain 2007; Chahed et al. 2008; Zhao et al. 2009; Bulsink et al. 2010) including Thailand (Pongpinyopap & Mungcharoen 2011; Chooyok et al. 2013; Gheewala et al. 2013, 2014). The water footprint is a volumetric measure, showing freshwater consumption and pollution in time and space. The spatial aspect is important because the potential environmental and social impacts of water use differ from one location to another. Although the water footprint technique provides a useful tool to quantify and identify hotspots for the bioethanol production chains, but it does not provide the optimal options to improve bioethanol production chains operation. In this study, integration of water footprint technique with the linear programing approach is used to optimize the operation decision of the bioethanol production chains in Thailand. The main objective is to propose a suitable planning tool based on the approaches commonly applied to bioethanol production chains strategic planning under different environmental criteria. The water footprint is performed as a post-optimization step to evaluate the water consumption and pollution in bioethanol production from cassava and from sugarcane. The system boundary is drawn to include water consumption of all steps from cassava/sugarcane cultivation, transport to cassava chips/sugar mill plant, cassava/sugarcane processing process, transport to bioethanol plant, bioethanol production process, transport to storage, distribute to fuel station and fuel combustion in vehicles. All of the three components of water footprint to be considered are green, blue and gray water footprint. The reference year of the data is 2010.

METHODS

Water footprint calculation

In order to assess the water footprint of the bioethanol production chain in Thailand, there is need to assess the domestic water resource required for domestic biofuel crop growth and processing into liquid fuel. A tool to do this assessment is the water footprint concept. This concept of the ‘water footprint’ has been proposed as an alternative indicator of water use, which focus on water consumption instead of water withdrawal (Aldaya et al. 2010). On the subject of consumption, evaporative water use is more relevant than water withdrawal because parts of the water withdrawal return to the water bodies where they were taken from, so these parts can be reused. According to Chapagain et al. (2006), the water footprint is the volume of water needed to produce the goods and services that measured at the place where the goods and services were actually produced. The water footprint of a product (m3/ton) is calculated as the ratio of the total volume of water used (m3/year) to the production quantity (ton/year). The water footprint has three components: the green water footprint (volume of water evaporated from the rainwater stored in the soil as soil moisture), blue water footprint (volume of water evaporated from surface and renewable ground water resources) and the gray water footprint (volume of freshwater required to dilute the wastewater from the production process to the agreed quality standards). In this study, the volume of freshwater consumed or polluted to produce all the goods and services as input of bioethanol production chains is also considered. The amount of water used to produce raw material in each stage of bioethanol production chain is obtained from Ecoinvent (2006) and converted to water consumption using the consumption factors (Flury et al. 2012). Details of each stage are described in the following sections.

Cassava/sugarcane cultivation

The green water footprint of the crop was estimated as the ratio of the effective rainfall to the crop yield, while the blue water footprint of the crop was taken as equal to the ratio of the volume of irrigation water required to the crop yield. The crop water requirement is calculated by multiplying the reference crop evapotranspiration (ETo) by the crop coefficient (Kc). The crop coefficients for the cassava and sugarcane plant were taken from Kwanyuen et al. (2010) and Royal Irrigation Department (2009), respectively. The reference crop ETo is calculated by multiplying the pan evaporation coefficient (Kp) by the pan evaporation (Ep). The pan evaporation coefficient was taken from Kwanyuen et al. (2010), while data on pan evaporation were obtained from the Thai Meteorological Department. All relevant computed data were taken from local data. The Daily Soil Water Balance method (balancing between the amount of water added to the root zone and the amount of water withdrawn from it) was used to distinguish between green (effective rainfall) and blue (irrigation requirement) water component according to Patwardhan et al. (1990) which reported that the best estimates of effective rainfall could be obtained by conducting soil–water balance computation. Pongpinyopap & Mungcharoen (2012) also found that the estimation of green water use derived from the daily soil–water balance method is appropriate for Thailand conditions. The gray water footprint of the crop was estimated as the ratio of the volume of water needed to dilute pollutants to the crop yield. The nitrogen was chosen as an indicator for the impact of fertilization application that entered the water system. The magnitude of nitrogen leaching depends on soil conditions (irrigation frequencies, rainfall pattern, soil texture, percolation rate, etc.) and methods of fertilization application (application rate, time, agronomical practices, etc.). The rate of nitrogen leaching from Roy's study is calculated (Roy et al. 2003). The permissible limit of 50 mg nitrate-NO3 per liter is used to estimate the volume of water necessary to dilute leached nitrogen to the permissible limit. The input data (clay content, nitrogen uptake, annual rainfall, etc.) to calculate the rate of nitrogen leaching are taken from the Thai Meteorological Department and the Land Development Department. In Thailand, cassava can be cultivated and harvested at any time of the year. In 2010, cassava plantations were located in 46 provinces from five regions. The main plantation area is in the Northeastern region that covers Nakhonratchasima, Chaiyaphum, Kalasin, Khonkaen, Udonthani, Buriram and Roiet provinces. In practice, most crops are typically cultivated before the raining period (during March–May) and some are cultivated after the raining period (during November–January). In this study, it is assumed that cassava cultivation starts in May and harvested in the 12th month. For sugarcane which is one of the major economic crops in Thailand, the total plantation area is the fifth largest in the world, behind Brazil, India, Cuba and China, respectively. In 2010, sugarcane plantations were located in 47 provinces from five regions. The main plantation area is in the Central region that covers Nakhonsawan, Kamphaengphet, Suphanburi, Lopburi, Phetchabun, Uthaithani and Sukhothai provinces. In practice, farmers plant sugarcane during October–November in the Northeastern region, during November–February in the Eastern Central Plains region, during December–April in the irrigated area and during May–June in the rain-fed area of the North region, during January–March in the irrigated area and during May–June in the rain-fed area of the Western Central Plains region. While the sugarcane cropping calendar varies by region, the plantation period is about 10–14 months depending on the types of sugarcane. In this study, it is assumed that plantation starts in November and harvesting starts from the 10th month. Detailed information on fertilizers, pesticides and fuel inputs in cassava/sugarcane cultivation area of the country is shown in Table 1. The biofuel crops yield data were obtained from the Office of Agricultural Economics, Ministry of Agriculture and Cooperatives (2012).

Table 1

Material and energy input in biofuel crop cultivation

ItemCassava (kg/ha)Sugarcane (kg/ha)
N-fertilizer 192.13 116.25 
P-fertilizer 96.06 60.00 
K-fertilizer 192.13 60.00 
Pesticide 0.19 7.06 
Diesel 1.86 20.40 
ItemCassava (kg/ha)Sugarcane (kg/ha)
N-fertilizer 192.13 116.25 
P-fertilizer 96.06 60.00 
K-fertilizer 192.13 60.00 
Pesticide 0.19 7.06 
Diesel 1.86 20.40 

Cassava chips/sugar mill plants

Cassava roots are harvested in the 12th month and converted to dried chips using a simple chopping machine. After chopping into small pieces, the chips are sun-dried on a cement floor. The conversion ratio of feedstock (ton) to dried chips is approximately 2.25:1 where water is not required in the process (Sriroth 2010). It is assumed that the conversion ratio does not vary with the processing efficiency. Approximately 10 months after new crop cultivation, sugarcane stalks are cut and ready for sugar milling, whereas the remaining parts, e.g. leaves and tops (termed cane trash) are either open burned or used for low-end applications. Sugar milling consists of a series of processing stages, e.g. crushing, clarification, boiling, seeding and centrifuging to extract sugar crystals, the main product, from the sugarcane. Two key co-products, molasses and bagasse, are also produced. Normally, approximately 104.00 kg of sugar and 36.31 kg of molasses are produced from 1 ton of sugarcane and 1.21 m3 of water is required in this process. However, the conversion ratio varies depending on processing efficiency so the background data were gathered from relevant research study in Thailand (Saibuatrong 2008; Office of the Cane and Sugar Board, Ministry of Industry 2012). The gray water footprint in this stage is assumed to be zero because the wastewater is normally stored in a pond or reused in the plant and is not directly discharged into the water system. This stage assumed that the intermediate production use feedstock from a crop field located near the plant and the transportation between biofuel crop fields and intermediate plants using 8.5-ton truck (six wheels). The distance data were derived from Department of Highways.

Cassava chips-based/molasses-based bioethanol plants

Four types of raw materials, cassava, cassava chips, sugarcane and molasses are the potential raw materials for bioethanol production in Thailand. However, cassava chips and molasses are the main raw materials. In cassava chips-based bioethanol production chain, from dried cassava chips to bioethanol, the processes in this stage consist of milling, mixing and liquefaction, saccharification, fermentation, distillation and dehydration. Approximately 3.06 tons of cassava chips and 3.78 m3 of water are required to produce 1 ton of bioethanol. The water is used for mixing and liquefaction steps and steam production. The molasses-based bioethanol conversion process consists of two main steps. First, molasses are fermented with yeast to obtain a dilute alcohol. Second, dilute alcohol is distillated and then dehydrated to produce bioethanol. In order to produce 1 ton of bioethanol, approximately 5.82 tons of molasses and 3.80 m3 of water are required. The water is used for fermentation and steam production. In the bioethanol production stage, the variation of conversion ratios was investigated from relevant research studies in Thailand (Nguyen 2007; Thailand Environment Institute 2007; Sriroth et al. 2010). It is assumed in this stage that the quality of the wastewater from bioethanol production remains above the water emission standards, so the gray water footprint is equal to zero. The bioethanol production uses feedstock from intermediate plants located near the plant. The transportation between intermediate plants and bioethanol plants uses 16-tons truck (10 wheels).

Gasohol E10 production

Gasohol E10 is a fuel mixture of 90% gasoline and 10% ethanol. About 87.35 kWh of electricity are required in mixing process. It is assumed in this stage that the Gasohol E10 production uses feedstock from bioethanol plant located near the storage tanks and the transportation between bioethanol plants and storage tanks uses 32-ton truck (20 wheels).

Life cycle optimization of bioethanol production chains

A life cycle optimization approach, which integrates a linear programing scheme and a water footprint concept, is used to minimize the water consumption of the entire bioethanol production chains from biofuel crops production to fuel production and to fuel end use. The superstructure of the bioethanol production chains is presented in Figure 1. It contains several stages dedicated to key activities: (1) ‘Harvesting and supply’ stage comprising the raw material supply; (2) ‘Collection and intermediate production’ stage comprising the pre-treatment of biofuel crops to obtain intermediates directly suitable for energy generation; (3) ‘Fuel production’ stage comprising the core processes for energy and other bio-products' generation; (4) ‘Distribution’ stage comprising the liquid fuel in storage tanks and distribution to service stations; and (5) ‘Use’ stage comprising the usages of the energy products (fuel combustion in vehicle). In this study, the border region of bioethanol production chain is separated into eight regions in order to avoid the impossible configuration (i.e. the intermediate production factory located in the West does not use biofuel crop from the East or the Northeast). However, some possible configuration would be across the border region (e.g. the bioethanol production plant located in the Central region would use feedstock from the North). Table 2 shows the number of nodes (i.e. number of biofuel crop cultivation provinces, number of intermediate production plants, number of bioethanol plants, etc.) in each stage of the bioethanol production chain. The water footprints of each node in each stage of the bioethanol production chain in Thailand classified by provinces are provided in Tables S1–S14 (available online at http://www.iwaponline.com/ws/015/129.pdf).

Table 2

Number of nodes in each stage of the bioethanol production chain

RegionBiofuel crop cultivation (province)Intermediate production (plant)Bioethanol production (plant)Gasohol E10 production (tank)Service station (province)
Cassava chips-based 
 North 
 Upper-Northeast 11 12 
 Lower-Northeast 
 Upper-Central 
 Middle-Central 
 Lower-Central 10 
 East 
 West 
Molasses-based 
 North 
 Upper-Northeast 11 10 12 
 Lower-Northeast 
 Upper-Central 
 Middle-Central 
 Lower-Central 10 
 East 
 West 10 
RegionBiofuel crop cultivation (province)Intermediate production (plant)Bioethanol production (plant)Gasohol E10 production (tank)Service station (province)
Cassava chips-based 
 North 
 Upper-Northeast 11 12 
 Lower-Northeast 
 Upper-Central 
 Middle-Central 
 Lower-Central 10 
 East 
 West 
Molasses-based 
 North 
 Upper-Northeast 11 10 12 
 Lower-Northeast 
 Upper-Central 
 Middle-Central 
 Lower-Central 10 
 East 
 West 10 
Figure 1

The superstructure of the bioethanol production chains.

Figure 1

The superstructure of the bioethanol production chains.

To fulfill the main objective mentioned, linear programing with Matlab is used to optimize (or minimize) the total water footprint (m3/year) from the operation of the bioethanol production chain. The model has been developed under a steady-state condition, assuming that all the parameters and variables do not change with time. The general water footprint minimization problem is formulated as follows: 
formula
1
 
formula
2
 
formula
3
 
formula
4
 
formula
5
 
formula
6
 
formula
7

where wfa,b is the water footprint in each stage of bioethanol production chain produced from biofuel crop type a in site b (m3/ton bioethanol), xa,b is the amount of bioethanol produced from biofuel crop type a in site b (ton bioethanol/year). BCAa,b is the available amount of biofuel crop type a in harvesting site b. IPCa,b is the production capacity of intermediate production plant from biofuel crop type a in processing site b. FPCa,b is the production capacity of fuel production plant from biofuel crop type a in processing site b. FSCa,b is the capacity of fuel station from biofuel crop type a in distributing site b. CDa,b is the country demand of Gasohol E10 from biofuel crop type a in using site b.

The decision variables of the model are actually the unknowns of the problems, i.e. those variables for which we are trying to find their optimal values. Normally, there are two decision variables: discrete (binary or integer) and continuous decision variables. The continuous variables are mostly associated with the design and operational characteristics (what is the amount of feedstock transported from Ni unit to Nj unit?). The following decision variables are included in the model: (1) quantity of biofuel crops distributed from each province to the intermediate production plants; (2) quantity of intermediate distributed from each plant to the fuel production plants; (3) quantity of bioethanol distributed from each fuel production plant to the storage tanks; (4) quantity of Gasohol E10 distributed from each storage tank to the fuel stations; and (5) quantity of Gasohol E10 distributed from each fuel station to the end users. The values of the decision variables must be greater than or equal to zero. The main constraints of the model are the mass balances that have to be satisfied between nodes (equality constraints) and the capacity constraints that have to be satisfied (‘less than’ constraints). There can be other technical constraints (e.g. energy balances) as well as policy constraints (e.g. the recycling rate of paper, or the amount of waste going to landfill). The following constraints are included in the model. (1) ‘Supply constraints’, which relate the biofuel crops production to the shipment quantities. Biofuel crops production quantity in each province must be equal to or less than the intermediate production capacity. (2) ‘Demand constraints’, which relate the Gasohol E10 production to meet the country demand. The sum of fuel production quantity from all sites must satisfy the demand.

RESULTS AND DISCUSSION

In 2010, 72% of bioethanol in Thailand was produced from molasses while 28% came from cassava chips. From the study, for bioethanol production chain, there were 16,414 possible configurations in which 11,124 configurations were for molasses and 5,290 were for cassava chips. The integrated LP model of provincial bioethanol production chain was consisted of around 164,140 continuous variables and 476,022 constraints. The model was solved in 25 minutes. The LP was performed using Matlab on a computer with 3.30 GHz Intel® Core™ i3-3220 processor with 16.00 GB of RAM. According to the Alternative Energy Development Plan, the Thai government has set the target to increase ethanol consumption to 6.20 million liters per day by 2016 and to 9.00 million liters per day by 2021, as shown in Table 3. It was found that the total water footprints of bioethanol consumed in Thailand were 3.23 × 109, 1.72 × 1010, and 2.49 × 1010 m3 per year in 2010, 2016, and 2021, respectively, based on the 2010 data. The minimum total water footprint of the cassava chips-based and the molasses-based bioethanol production chain are 3.70 × 103m3 and 1.16 × 103m3 per ton, respectively, both from Kanchanaburi province. The water consumption of biofuel crop that required in the cultivation stage is the highest contributor to the total water footprint. The share of agriculture water consumption to the total water footprints of bioethanol was 99% and industrial water consumption was 1%. In cassava chips-based bioethanol production chain, the share of green water was 26%, blue water was 0%, and gray water was 74% to the total water footprints. Likewise, molasses-based bioethanol production chain, the share of green water was 5%, blue water was 38%, and gray water was 57% to the total water footprints. This indicates that sugarcane needs more irrigation water to grow than cassava chips-based production (Phujaroen 2008; Damen 2010). Moreover, the results shown that the cassava cultivation can grow without irrigation water. The advantage of this will lead to decrease the opportunity costs of blue water uses. The efficient of allocation water for different water uses is important due to annually limited water resource. Thus, the selection of cassava should be done in order to minimize the impact of bioethanol production for people in Thailand on water resource. After performing optimization based on 2010 data, the total water footprint will be reduced considerably. It was found that the total water footprint will be decreased from 3.23 × 109 to 1.19 × 109 m3 per year in 2010 (63% saving). To meet the government plan for 2016 and 2021, the results show that the best solution, with the proper management, would reduce the total water footprint by 55% and 53%, respectively. The best solution was to select feedstock from crop fields with lower water footprint to operate bioethanol production chain. For example, in the East, using cassava from Sa-Kaeo province as feedstock for cassava chips plant and cassava chips-based bioethanol plant which located in Sa-Kaeo province was not suitable. Also, using of sugarcane from Sa-Kaeo province as feedstock for sugar mill plant and the molasses-based bioethanol plant which is located in Sa-Kaeo province was not suitable. The best solution shows that cassava and sugarcane from Prachinburi for operating bioethanol production chain should be selected. It is indicated that the selection of feedstock to operate bioethanol production chain from a crop field located near the plant was not optimal configurations. So, this approach can be used as a tool to reduce water consumption and provide the operation plan of bioethanol production chain. The optimal configurations might increase the operation cost of bioethanol production chain. However, another way to reduce the blue water consumption of bioethanol production chain is to increase the use of cassava chips as feedstock. Every 1% increase of cassava chips, the blue water consumption will be reduced by 2.97 × 106 m3 per year (0.002% of Thailand's runoff). On the contrary, previous study (Pongpinyopap & Mungcharoen 2013) indicated that increasing the use of molasses as feedstock would save greenhouse gas (GHG) emissions. Therefore, the multi-objective optimization should be investigated to find the optimum condition for both GHG emissions and water consumption.

Table 3

Bioethanol consumption target, total water footprint and total water footprint optimization output of the bioethanol production chain in Thailand

YearTarget (ML/day)Total water footprint (m3/year)Total water footprint optimization output (m3/year)
2010 1.16a 3.23 × 109 1.19 × 109 
2016 6.20 1.72 × 1010 7.80 × 109 
2021 9.00 2.49 × 1010 1.16 × 1010 
YearTarget (ML/day)Total water footprint (m3/year)Total water footprint optimization output (m3/year)
2010 1.16a 3.23 × 109 1.19 × 109 
2016 6.20 1.72 × 1010 7.80 × 109 
2021 9.00 2.49 × 1010 1.16 × 1010 

aActual bioethanol consumption in Thailand.

CONCLUSIONS

This study evaluated water footprint of bioethanol in the form of Gasohol E10 consumed in Thailand. Integration of water footprint technique with the linear programing approach is used for operation planning of the bioethanol production chain. The results show that the total water footprint of bioethanol in Thailand was about 3.23 × 109 m3 per year in 2010. Based on the 2010 data, the total water footprints of bioethanol consumed in Thailand would be 1.72 × 1010 and 2.49 × 1010 m3 per year in 2016 and 2021, respectively, according to the Alternative Energy Development Plan. After applying the linear programing, it was found that, with the proper management, the water footprint could be reduced by at least 53%, or 1.33 × 1010 m3, annually. It was also found that increasing the use of cassava chips can help reduce the blue water consumption of bioethanol production chain because cassava can grow without irrigation. The modeling approach and formulation presented provide a valuable analytical tool to reduce water consumption and provide the operation plan of bioethanol production chain. Further study on multi-objective optimization of GHG emissions and water consumption is required to find the optimum use of molasses and cassava chips for bioethanol production in Thailand. This approach could be used to provide consistent results in order to drive political decisions about energy policies for the future bioethanol production chain and also used in other countries having the same or different crops as in Thailand.

ACKNOWLEDGEMENTS

The authors gratefully acknowledge financial support from the Kasetsart University Research and Development Institute (KURDI), the Energy Policy and Planning Office under the Ministry of Energy, the Center for Petroleum Petrochemicals and Advanced Materials and the Center for Advanced Studies in Industrial Technology, Department of Chemical Engineering, Faculty of Engineering, Kasetsart University.

REFERENCES

REFERENCES
Aldaya
M. M.
Munoz
G.
Hoekstra
A. Y.
2010
Water Footprint of Cotton, Wheat and Rice Production in Central Asia. Value of Water Research Report Series No.41
,
UNESCO-IHE
,
Delft, The Netherlands
.
Bulsink
F.
Hoekstra
A. Y.
Booij
M. J.
2010
The water footprint of Indonesian provinces related to the consumption of crop products
.
Hydrology and Earth System Sciences
14
(
1
),
119
128
.
Chahed
J.
Hamdane
A.
Besbes
M.
2008
A comprehensive water balance of Tunisia: blue water, green water and virtual water
.
Water International
33
(
4
),
415
424
.
Damen
B.
2010
BEFS Thailand-Key Results and Policy Recommendations for Future Bioenergy Development
.
FAO United Nations
,
Rome, Italy
.
Department of Alternative Energy Development and Efficiency, Ministry of Energy
2012
Alternative Energy Development Plan (AEDP) 2012–2021
, .
Ecoinvent
2006
Swiss Center for Life Cycle Inventories
.
Swiss Centre for Life Cycle Inventories
,
Switzerland
.
Flury
K.
Jungbluth
N.
Frischknecht
R.
Muñoz
I.
2012
Recommendation for life cycle inventory analysis for water use and consumption. http://www.esu-services.ch/fileadmin/download/flury-2012-water-LCI-recommendations.pdf
(accessed 15 July 2013).
Gheewala
S. H.
Silalertruksa
T.
Nilsalab
P.
Mungkung
R.
Perret
S. R.
Chaiyawannakarn
N.
2013
Implications of the biofuels policy mandate in Thailand on water: the case of bioethanol
.
Bioresource Technology
150
,
457
465
.
Gheewala
S. H.
Silalertruksa
T.
Nilsalab
P.
Mungkung
R.
Perret
S. R.
Chaiyawannakarn
N.
2014
Water footprint and impact of water consumption for food, feed, fuel crops production in Thailand
.
Water
6
(
6
),
1698
1718
.
Hoekstra
A. Y.
Chapagain
A. K.
Aldaya
M. M.
Mekonnen
M. M.
2009
Water Footprint Manual State of the Art 2009
.
Enschede
,
The Netherlands
.
Hoekstra
A. Y.
Chapagain
A. K.
Aldaya
M. M.
Mekonnen
M. M.
2011
The Water Footprint Assessment Manual: Setting the Global Standard
.
Earthscan
,
London
.
Kwanyuen
B.
NumKhang
P.
Phuthongsook
W.
Tonwiboonsak
S.
2010
The study of cassava's crop coefficient (Kc)
. In:
The 11th Thai Society of Agricultural Engineering International Conference, 6–7 May 2010
,
Nakhonpathom, Thailand
.
Nguyen
T. L. T.
2007
Life-cycle Assessment of Bio-ethanol as an Alternative Transportation Fuel in Thailand
.
PhD Thesis
,
The Joint Graduate School of Energy and Environment, King Mongkut's University of Technology Thonburi
,
Bangkok, Thailand
.
Office of Agricultural Economics, Ministry of Agriculture and Cooperatives
2012
Agricultural Statistics of Thailand 2011
,
Bangkok, Thailand. http://www.oae.go.th/download/download_journal/yearbook54.pdf (accessed 16 September 2012)
.
Office of the Cane and Sugar Board, Ministry of Industry
2012
Sugar production report: year 2010/2011
. .
Office of the National Economic and Social Development Board
2012
The Eleventh National Economic and Social Development Plan (2012–2016)
,
Bangkok, Thailand. http://www.nesdb.go.th/Portals/0/news/plan/p11/Plan11_eng.pdf (accessed 9 April 2013)
.
Patwardhan
A. S.
Nieber
J. L.
Johns
E. L.
1990
Effective rainfall estimation methods
.
Journal of Irrigation Drainage Engineering
116
(
2
),
182
193
.
Phujaroen
S.
2008
Energy Efficiency in Ethanol Production using Cassava and Molasses as Raw Materials
.
Master Thesis
,
Energy Management Technology, School of Energy Environment and Materials, King Mongkut's University of Technology Thonburi
,
Bangkok, Thailand
.
Pongpinyopap
S.
Mungcharoen
T.
2011
Water footprint of bioethanol production from cassava in Thailand
.
Kasetsart Engineering Journal
75
(
24
),
61
74
.
Pongpinyopap
S.
Mungcharoen
T.
2012
Comparative study of green water footprint estimation methods for Thailand: A case study of cassava-based ethanol
.
Environment and Natural Resources Journal
10
(
2
),
66
72
.
Pongpinyopap
S.
Mungcharoen
T.
2013
Life cycle optimization of greenhouse gas emissions from bioethanol in Thailand
. In:
The 3rd International LCA Conference 2013, 4–5 November 2013
,
Lille, France
.
Roy
R. N.
Misra
R. V.
Lesschen
J. P.
Smaling
E. M.
2003
Assessment of Soil Nutrient Balance Approaches and Methodologies
.
FAO United Nations
,
Rome, Italy
.
Royal Irrigation Department
2009
Crop Coefficient (Kc)
,
Bangkok, Thailand. http://water.rid.go.th/hwm/cropwater/CWRdata/Kc/kc_th.pdf (accessed 3 July 2013)
.
Saibuatrong
W.
2008
A Comparative Study on Net Energy Gain and Life Cycle Environmental Impact of Raw Materials for Ethanol Production
.
Master Thesis
,
Chemical Engineering, Kasetsart University
,
Bangkok, Thailand
.
Sriroth
K.
2010
Overview of Potential of Cassava as a Food Crop and as a Feedstock for Biofuels
. .
Thailand Environment Institute
2007
Life Cycle Assessment of Ethanol from Cassava and Sugar Cane
.
Bureau of Energy Research
,
Bangkok, Thailand
.
Zhao
X.
Chen
B.
Yang
Z. F.
2009
National water footprint in an input-output framework: a case study of China 2002
.
Ecological Modelling
220
(
2
),
245
253
.

Supplementary data