Sugarcane ethanol: Contributions to climate change - BAFF

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Chapter 3 Another source of data on sugarcane planted area is the National Food Supply Company (CONAB). CONAB data, which are presented in a crop assessment reports, are not used in this study. Sugarcane planted area is also provided on the Canasat Project, coordinated by the National Institute for Space Research (INPE). Canasat Project monitors the most important producing states and estimates the planted area from remote sensing satellite images. A summary of data available in Brazil is presented in the Table 1. Table 1. Sugarcane area in Brazil (sources of data available). Source Data gathering Period available Data presented Aggregation level IBGE LSPA survey 1990-2006 planted area and harvested area state (Brazil) PAM survey 1990-2006 harvested area municipality (Brazil) CONAB crop assessment 2005, 2007-2008 planted area state (Brazil) INPE/Canasat satellite images 2003/2005 - 2008 planted area municipality (South- Centre) 3.1. Measuring the LUC using remote sensing images Remote sensing satellite images from the Earth surface are an important source of information to evaluate the fast land use changes observed by the dynamic agricultural activity. Brazil, through its National Institute for Space Research (INPE), acquires remote sensing images from both Landsat and CBERS satellites since 1973 and 1999, respectively. In 2003, INPE started the Canasat Project together with UNICA (Sugarcane Industry Association), CEPEA (Center for Advanced Studies on Applied Economics) and CTC (Center of Sugarcane Technology) to map the cultivated sugarcane area in the South-Central region of Brazil using remote sensing images (www.dsr.inpe.br/canasat/). �e mapping began in São Paulo State in 2003 and in 2005 it was extended to the states of Minas Gerais, Goiás, Mato Grosso do Sul and Mato Grosso where sugarcane production has been most intensi�ed over the last years. In order to obtain an accurate thematic map, multitemporal images were acquired at speci�c periods to correctly identify the sugarcane �elds and to clearly distinguishing them from other targets (Rudor� and Sugawara, 2007). �e mapping procedure was mainly performed through visual interpretation on the computer screen using the SPRING so�ware (www.dpi.inpe.br/spring/), which is a Geographic Information System (GIS) with digital image processing capabilities. 68 Sugarcane ethanol
Prospects of the sugarcane expansion in Brazil Results from the thematic maps of the Canasat Project presented in Section 4.1 refer to the total cultivated sugarcane area which includes the �elds being renewed with the 18 months sugarcane plant, the �elds planted in new areas (expansion) and the �elds of sugarcane ratoons (Sugawara et al., 2008). Prior land use identi�cation for the expanded sugarcane plantations in each year was carried out using remote sensing images acquired before the land use change to sugarcane. �is evaluation was accomplished in São Paulo for the years of 2005 to 2008, and for the years of 2007 and 2008 for the States of Minas Gerais, Goiás, Paraná, Mato Grosso do Sul and Mato Grosso. Four classes of land use and occupation were de�ned, as follows: Agriculture, for cultivated and bare soil �elds; Pasture, for natural and anthropogenic pasture land; Reforestation, for reforested areas with Pinus and Eucalyptus; and Forest, for riparian forests and other forests no matter the stage of succession. In São Paulo State the Citrus class was also considered due to its relevance in terms of land occupation and change to sugarcane. �ese �ve classes were responsible for almost all of the changes to sugarcane. Figure 3 illustrates each of these classes over some Landsat images acquired at two di�erent dates prior to the change and one date a�er the change to sugarcane. Figure 3a highlights a �eld classi�ed as Agriculture. On Date 1 (March of 2003), the �eld has the appearance of bare soil (medium-gray), and on Date 2 (May of 2003), it is covered with a winter crop - probably maize. On Date 3 (April of 2008), a well grown sugarcane �eld can be clearly identi�ed (light-gray with well de�ned pathways). An example for the Pasture class is illustrated in Figure 3b where it appears as a mixture of di�erent amounts of vegetation and soil (medium/light-gray). On Date 1, the vegetation amount is dominant (end of rain season) whereas on Date 2 the soil becomes dominant due to a reduction in the green vegetation amount in response to less available water to the plant (mid dry season). On Date 3, a sugarcane �eld can be observed in substitution to the pasture �eld. Figure 3c illustrates the Citrus class with its typical pattern on Date 1 and 2, and a sugarcane �eld on Date 3. Figure 3d presents a typical �eld for the Forest class (Dates 1 and 2) that was changed to sugarcane (Date 3). A �eld changed from the Reforestation class to sugarcane is illustrated in Figure 3e. It is worth to mention that Figure 3 only illustrates, in a simpli�ed way, part of the whole procedure used to identify the di�erent land use classes that were displaced to sugarcane in each year over the analyzed period. In several occasions a greater number of images were necessary to clearly identify the classes that were changed to sugarcane. �e SPRING so�ware allows coupling images acquired at di�erent dates to alternate views of the same area facilitating the visual interpretation resulting in a better extraction of the correct information registered in the coloured multispectral satellite images. Sugarcane ethanol 69
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Sugarcane ethanol Contributions to
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Table of contents Foreword 11 José
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Chapter 8 �e global impacts of US
Page 8 and 9:
Foreword �e use of biofuels as a
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Executive summary Do biofuels help
Page 13 and 14: Executive summary 12. Projections o
Page 15 and 16: Chapter 1 Introduction to sugarcane
Page 17 and 18: Introduction to sugarcane ethanol A
Page 19 and 20: Introduction to sugarcane ethanol C
Page 21 and 22: Introduction to sugarcane ethanol F
Page 23: Introduction to sugarcane ethanol H
Page 26 and 27: Chapter 2 Production (million tons)
Page 28 and 29: Chapter 2 Table 2. Sugarcane produc
Page 30 and 31: Chapter 2 Harvested area (million h
Page 32 and 33: Chapter 2 million hectares 8 7 6 5
Page 34 and 35: Chapter 2 Table 5. Global significa
Page 36 and 37: Chapter 2 (FAOSTAT, 2008). �e lan
Page 38 and 39: Chapter 2 indicating that sugarcane
Page 40 and 41: Chapter 2 and vinasse produced duri
Page 42 and 43: Chapter 2 • • • • Harvested
Page 44 and 45: Chapter 2 2.2. AEZ assessment of la
Page 46 and 47: Chapter 2 The quantified descriptio
Page 48 and 49: Chapter 2 Table 8 summarizes by reg
Page 50 and 51: Chapter 2 Table 9. Suitability of u
Page 52 and 53: Chapter 2 Table 10. Suitability of
Page 54 and 55: Chapter 2 of bio-diversity and land
Page 56 and 57: Chapter 2 FAO, 1987. 1948-1985 Worl
Page 58 and 59: Chapter 2 Smeets, E., M. Junginger,
Page 60 and 61: Chapter 3 Considering that this deb
Page 62 and 63: Chapter 3 1,000 ha 9,000 8,000 7,00
Page 66 and 67: Chapter 3 Figure 3. Different land
Page 68 and 69: Chapter 3 1-Pastureand crops expans
Page 70 and 71: Chapter 3 Given that the model is u
Page 72 and 73: Chapter 3 to the case studies; (d)
Page 74 and 75: Chapter 3 Mato Grosso do Sul in 200
Page 76 and 77: Chapter 3 the Pasture class increas
Page 78 and 79: Chapter 3 Table 5. Number of sugarc
Page 80 and 81: Chapter 3 of 12.6% from 2001 to 200
Page 82 and 83: Chapter 3 Table 6. South-Centre: ex
Page 84 and 85: Chapter 3 4.5. Options for approach
Page 86 and 87: Chapter 3 states that have lost pas
Page 88 and 89: Chapter 3 It is important to contex
Page 91 and 92: Chapter 4 Mitigation of GHG emissio
Page 93 and 94: Table 1. Basic data: sugarcane prod
Page 95 and 96: Mitigation of GHG emissions using s
Page 97 and 98: GHG emission (kg CO 2 eq/m 3 etOH)
Page 101 and 102: Table 7. Soil carbon content for di
Page 105 and 106: 5. Conclusions Mitigation of GHG em
Page 107: Mitigation of GHG emissions using s
Page 110 and 111: Chapter 5 Oil reserves - Gasoline/d
Page 112 and 113: Chapter 5 in research and developme
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Chapter 5 Table 3. Summary of main
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Chapter 5 3. Environmental indicato
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Chapter 5 Table 4. Carbon stock in
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Chapter 5 3.2. Water Total carbon i
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Chapter 5 3.3. Soil and fertilizers
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Chapter 5 3.4. Management of diseas
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Chapter 5 Table 10. Sugarcane and v
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Chapter 5 in the implementation of
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Chapter 5 Table 11. Continued. Crit
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Chapter 5 Brazilian Government. Lei
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Chapter 5 São Paulo State Governme
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Chapter 6 2. Development of the eth
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Chapter 6 blends by car manufacture
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Chapter 6 Brazil have been roughly
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Chapter 6 Overall there are few di
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Chapter 6 3.2.1. The impact of exis
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Chapter 6 �e use of ethanol in he
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Chapter 6 cropping systems that pro
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Chapter 6 certainty, the supply of
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Chapter 6 De Vries, B.J.M., D.P. va
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Chapter 7 Biofuel conversion techno
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Biofuel conversion technologies �
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Biofuel conversion technologies In
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Biofuel conversion technologies the
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Biofuel conversion technologies Imp
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Biofuel conversion technologies Pro
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Investment costs (Euro/kWth input c
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4.2. Greenhouse gas balances Biofue
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Reduction in CO equivalent emission
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Biofuel conversion technologies �
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Biofuel conversion technologies pot
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Chapter 8 The global impacts of US
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The global impacts of US and EU bio
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Page 185 and 186:
Page 187 and 188:
Table 1. Change in output due to EU
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Forest cover Pasture cover USEU 201
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Chapter 9 also risks and serious tr
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Chapter 9 Box 2. Bioethanol stoves
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Chapter 9 �e highest impact on po
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Chapter 9 the increased generation
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Chapter 9 o�ers to support the MD
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Chapter 9 Price variation (%) 14 12
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Chapter 9 Dufey et al., 2007a). Leg
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Chapter 9 manufactured directly fro
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Chapter 9 in mechanical aid for the
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Chapter 9 Table 1. Import tariffs o
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Chapter 9 3.8. Improving efficiency
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Chapter 9 some minimum transport in
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Chapter 9 IEA, 2004. Biofuels for T
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Chapter 10 Why are current food pri
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Why are current food prices so high
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2.4 Long-term drivers of supply Why
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3.1. Effects on the supply side Why
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3.3 Policy responses to rising food
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3.4. Other effects • Why are curr
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- - - - Why are current food prices
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18 16 14 12 10 8 6 4 2 0 5. The fut
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• • • Why are current food pr
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6.1. Policy implications Why are cu
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Why are current food prices so high
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Authors Dr. Marcos Adami, senior re
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Keyword index A Africa 204, 205, 20
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M maize 20, 83, 104, 173, 184, 187,
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Sugarcane ethanol: Contributions to climate change - BAFF

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