Indirect land use change for biofuels: Testing predictions and improving analytical methodologies

b i o m a s s a n d b i o e n e r g y 3 5 ( 2 0 1 1 ) 3 2 3 5 e3 2 4 0

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Indirect land use change for biofuels: Testing predictions and improving analytical methodologies Seungdo Kim, Bruce E. Dale* Department of Chemical Engineering and Materials Science, DOE Great Lakes Bioenergy Research Center, Michigan State University, 3900 Collins Road, Lansing, MI 48910, USA

article info

abstract

Article history:

Current practices for estimating indirect land use change (iLUC) due to United States bio-

Received 27 January 2011

fuel production rely on assumption-heavy, global economic modeling approaches. Prior

Received in revised form

iLUC studies have failed to compare their predictions to past global historical data. An

21 April 2011

empirical approach is used to detect evidence for iLUC that might be catalyzed by United

Accepted 25 April 2011

States biofuel production through a “bottom-up”, data-driven, statistical approach. Results

Available online 13 May 2011

show that biofuel production in the United States from 2002 to 2007 is not significantly correlated with changes in croplands for corn (coarse grain) plus soybean in regions of the

Keywords:

world which are corn (coarse grain) and soybean trading partners of the United States. The

Corn

results may be interpreted in at least two different ways: 1) biofuel production in the United

Biofuel

States through 2007 (the last date for which information is available) probably has not

Historical data

induced any indirect land use change, and 2) this empirical approach may not be sensitive

Indirect land use change

enough to detect indirect land use change from the historical data. It seems clear that

Renewable energy policy

additional effort may be required to develop methodologies to observe indirect land use

Soybean

change from the historical data. Such efforts might reduce uncertainties in indirect land use change estimates or perhaps form the basis for better policies or standards for biofuels. ª 2011 Elsevier Ltd. All rights reserved.

1.

Introduction

Indirect land use change (iLUC) due to biofuel production has become a key issue in the ongoing ‘Food-versus-Energy’ debates. The basic concept of iLUC is that natural ecosystems elsewhere might be converted to croplands to replace crops (either ‘animal feed’ or ‘food’) that are lost due to biofuel production. For example, changes in the United States (US) corn supply caused by ethanol fuel production could eventually lead to increases in: 1) corn (coarse grain) areas in other countries due to decline in US corn export or 2) croplands for soybean in other countries due to a decline in US soybean exports, resulting from conversion of soybean fields to cornfields in the United States.

Several studies show that iLUC has the potential to be one of the primary potential greenhouse gas (GHG) sources in wellto-fuel GHG emissions of biofuels [1e5]. Crop management practices in newly converted croplands can also play an important role in GHG estimates due to iLUC [6]. Even though there is a general consensus within the biofuel community that the iLUC effects are potentially important, a major concern for iLUC is ‘uncertainty’, particularly due to the amount and type of forest and grassland converted. These are critical factors in determining GHG emissions associated with iLUC. For example, Searchinger et al. [1] shows that about 9.1 ha of natural ecosystems are converted to croplands (52% from forest, 46% from grassland and 2% from desert) due to one TJ of ethanol fuel production, while the United States

* Corresponding author. Tel.: þ1 517 353 6777; fax: þ1 517 337 7904. E-mail addresses: [email protected] (S. Kim), [email protected] (B.E. Dale). 0961-9534/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.biombioe.2011.04.039

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Environmental Protection Agency (US EPA) [2] estimates about 4.4 ha TJ1 for land conversion as iLUC (66% from forest and 34% from grassland). The California Air Resources Board (CARB) [3] and the GREET model [4,5] estimate the iLUC effects due to corn based ethanol via the Global Trade Analysis Project (GTAP) model to determine the sizes and the locations of natural ecosystems converted due to biofuels. Results from CARB show the conversion of 5.1 ha TJ1 of natural ecosystems to croplands (26% from forest and 74% from grassland), while GREET shows the conversion of 1.6 ha TJ1 of natural ecosystems to croplands (33% from forest and 67% from grassland). These results show that the sizes, the types and the locations of the natural ecosystems converted differ greatly between the economic models chosen and their assumptions. CARB [3] uses an ethanol production increase of 50 hm3 in its economic modeling, while the US EPA [2] uses a 10 hm3 ethanol production increase. CARB’s assumption of a 50 hm3 ethanol production increase in its analysis implies that an ethanol production increase dating from 2002 would begin to trigger iLUC. If biofuel production in the United States has indeed triggered land conversion elsewhere, evidence for iLUC effects should be observed in the historical data. The historical data would include changes in the area of forest lands, total arable lands and croplands for animal feed and food associated with biofuel production in the United States. Few studies have attempted to find evidence for iLUC from the historical data. This study attempts to detect evidence for iLUC from the historical data through a set of tests for a better understanding of iLUC in policy or standard developments. The tests developed in this study identify regions of the world in which iLUC due to US biofuel production might be observed from the historical data. This analysis uses 19 geographical regions as in Tyner et al.’s study [5] e Brazil (BRA), Canada (CAN), China and Hong Kong (CHK), India (IND), Japan (JAP), United States (USA), Central and Caribbean Americas (CCA), East Asia (EAS), European Union 27(EUN), Malaysia and Indonesia (MAI), Middle Eastern and North Africa (MEA), Oceania countries (OCC), Other East Europe and Rest of Former Soviet Union (OES), Rest of European Countries (REC), Rest of South Asia (RSA), Rest of South East Asia (SEA), Russia (RUS), South and Other Americas (SOA), and Sub Saharan Africa (SSA). The details of these regions are given elsewhere [5]. The historical data, including land use patterns and commodity grain imports, associated with those 19 regions are investigated to determine regions where iLUC has occurred.

2.

Material and methods

The analysis proposed in this study is an empirical approach to detect indirect land use change due to US biofuel production, particularly corn based ethanol and soybean biodiesel. This is a “bottom-up”, data-driven, statistical approach based on individual regions’ land use patterns and commodity grain imports. This approach relies on very few assumptions and tries neither to quantify nor to predict iLUC effects. According to iLUC predictions [1e3], production of corn ethanol or soybean biodiesel in the United States will lead to reduced

exports which will increase crop commodity prices which in turn will catalyze land use change with potentially large accompanying GHG releases. Thus biofuel production leads to reduced exports which in turn lead to land use change. It is this mechanism that we test here. Predictions of iLUC are empirically tested using historical data on the United States croplands, commodity grain exports to specific regions and land use trends in those geographical regions. We use the 1990s as a baseline when the United States biofuel industry was very small and measure changes against that baseline. In order for iLUC to occur in a specific geographical region, it is postulated that the following five conditions must be met simultaneously. 1. For a specific region, average areas of croplands used for corn plus soybean production in the 2000s must increase compared to the 1990s. 2. For a specific region, average areas of arable lands in the 2000s must also increase compared to the 1990s. Otherwise, corn plus soybean areas are probably converted from other cropland areas in that region. Biofuel production in the United States is unlikely to be involved in those intercropland conversion processes. Economic or other factors such as domestic agricultural policies are more likely to be responsible for those inter-cropland conversions than is US biofuel production [7]. Since there is always some cost associated with bringing new lands into agricultural production, we assume that there would be no conversion of natural ecosystems in regions where average areas of arable lands in the 2000s are less than those in the 1990s. 3. For a particular geographical region, average areas of natural ecosystem lands in the 2000s decline compared to the 1990s. The natural ecosystem lands include forest, and permanent meadows and pastures. Otherwise, no natural ecosystems are converted to croplands to produce animal feed or food that is lost due to US biofuel production. 4. For a particular region, average corn plus soybean imports from United States in the 2000s decline significantly compared to imports during the 1990s. Annual changes that occur within average annual variation are taken to mean ‘no significant decline’. iLUC due to biofuel production is not likely to occur in regions within which mean corn plus soybean imports from United States increase. Up to this point, conditions 1e4 seem to be straightforward. Indirect land use changes due to US biofuel production would not happen in regions that do not meet conditions 1e4. The last condition (condition 5) determines whether iLUC would be observed in the regions that meet conditions 1e4 using correlation tests. Changes in harvested areas for corn and soybean for US biofuel production will lead to increases or decreases in croplands for corn plus soybean in a specific region if US biofuel production influences land use changes. The correlation test between biofuel production and land use changes can provide some clues about the relationships between these factors. 5. Annual percentage changes in croplands for corn plus soybean in a specific region are positively correlated with

b i o m a s s a n d b i o e n e r g y 3 5 ( 2 0 1 1 ) 3 2 3 5 e3 2 4 0

annual percentage changes in harvested areas for corn and soybean for biofuel production (corn ethanol and soybean biodiesel) in the United States. A negative correlation suggests that annual percentage changes in croplands for corn plus soybean decline with annual percentage changes in harvested areas for corn and soybean for biofuel production in the United States. Even though ‘correlation’ does not mean ‘causation’, ‘no correlation’ strongly suggests ‘not related to each other’. Year 2007 biofuel production in the United States is tested using the above conditions to determine regions within which iLUC effects are detected in the historical data. The 1990 baseline values are average values from 1992 to 1999. Average values in the 2000s are taken from 2000 to 2008. Global information on country-level croplands and natural ecosystems is available at the Food and Agriculture Organization of the United Nations e FAOSTAT [8]. The United States corn and soybean trade data are obtained from the United States Foreign Agricultural Service [9]. Even though crop price is a primary factor driving iLUC effects in the global economic models, the amount of cropland involved is used in the correlation tests. The historical data show that annual percentage changes in corn prices in the United States are moderately correlated with annual percentage changes in croplands for US biofuel production. This is illustrated in Fig. 1. Due to moderate correlations, cropland would be more appropriate for the correlation tests than crop price. Furthermore, corn price is also influenced by fossil energy costs (crude oil price), weather conditions and other factors [10,11]. Annual percentage change is defined as (AiAi1)/(Ai1) %, where Ai is area at year i. The reasons for using the annual percentage changes instead of total cropland areas in this analysis are that a) biofuel production accounts for only 1.5% of global croplands for grain and oil seeds [12], b) the biofuel industry is relatively young, and c) many other factors rather than biofuel production (e.g., population pressure, food consumption, economics, crop yields, commodity speculation, etc.) are involved in the system. Therefore, correlation tests with the total cropland areas would not be appropriate. The annual change in croplands, not just percentage change, is another feasible parameter for the correlation tests. Both

parameters (i.e., annual percentage changes and annual changes) are investigated in our correlation tests. For the correlation tests, corn area used for corn based ethanol production is estimated by Eq. (1).     Di ð1  Di Þ Di ð1  Di Þ EtOHi $ þ EtOHi $ þ YDi YDi YWi YWi $b þ $ð100  bÞ Ci1 Ci (1) Where EtOHi is corn based ethanol production at year i. Di is the dry milling share of total ethanol production. YDi is ethanol yield in dry milling, while YWi is ethanol yield in wet milling. Ci is corn yield at year i. b is a percentage of feedstock harvested in the previous year involved in producing corn based ethanol at year i. (100b) is a percentage of feedstock harvested at year i. Soybean area used for soybean diesel follows the same algorithm. Biofuel yield (e.g., ethanol and soybean diesel) and other data are obtained from literature [4,13e15]. We assume that essentially 100% of US biofuel production in a given year is derived from feedstock harvested in the previous year (b ¼ 1). This implies that there is at least a twoyear lag between diversion of corn and soybean production to biofuels and any possible iLUC effects. The time lag between biofuel production and its possible iLUC effects is one year, in order to account for planting and harvesting decisions. For example, the iLUC effects due to corn and soybeans produced in 2005 and converted to biofuels in the US would occur in 2007 in the regions of interest, while the iLUC effects due to the 2006 diversion of corn and soybean production to the 2007 biofuel production increase in the United States would occur in 2008. In a sensitivity analysis, we investigate the effect of the fraction of feedstock harvested in the previous year involved in biofuel production in a given year. The possibility that coarse grains other than corn alone are planted in the newly converted croplands is also investigated in a sensitivity analysis (referred to as ‘coarse grain case’). This coarse grain case in the sensitivity analysis affects conditions 1, 4 and 5. Croplands for coarse grain plus soybean production in condition 1 and coarse grain plus soybean imports from United States in condition 4 are investigated. In condition 5, the correlations between croplands for coarse grain plus soybean in a specific region and harvested areas for corn and soybean for biofuel production are tested.

3.

Fig. 1 e Annual percentage changes in corn price versus annual percentage changes in croplands for biofuel production in the United States (from 1997 to 2009).

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Results

First, the correlations between domestic cropland and cropland used for biofuel production in the United States are investigated to determine whether iLUC has occurred in the United States e annual percentage changes in planted areas for crops versus annual percentage change in croplands for biofuel production. Results show that the annual percentage change in planted areas for cotton, corn plus soybean and oats are significantly correlated with the annual percentage change in croplands for biofuel production in the United States at p < 0.05. However, negative correlations are observed only for cotton, which is not a major food source. This implies that US biofuel production can reduce croplands for cotton,

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which is consistent with findings in the United States Government Accountability Office’s report [11]. This result also intensifies the rationale for condition 5. Pearson productmoment correlation coefficients for individual crops up to year 2009 are summarized in the supplementary material. Even though year 2007 US biofuel production is tested for conditions in other regions, the data availability in the United States allows investigating year 2009 biofuel production to determine the iLUC effects in the United States. No arable land increases from the 1990s are observed in the United States. Furthermore, no declines in natural ecosystem lands in the United States have been observed since 1998. Therefore, the US historical data do not indicate that iLUC occurred within the 48 contiguous states as a result of US biofuel production. The regional information on 18 regions (except for the USA) is tested for conditions 1e4 first to identify any regions that meet all four conditions. Results show that only three regions satisfy conditions 1e4: Brazil, Oceania countries, and Sub Saharan Africa (Brazil and Oceania countries in the coarse grain case). These three regions (two regions in the coarse grain case) would thus have potential for iLUC due to US biofuel production and are tested through the correlation analysis. Results from testing conditions 1e4 are listed in Table 1. Results in parentheses reflect the coarse grain case. The MAI region (Malaysia and Indonesia) is eliminated from consideration because it shows no increases in croplands for corn (coarse grain) plus soybean despite satisfying conditions 2e4. About 44% of natural ecosystems converted in the MAI region from 1992 to 2008 become croplands, while the rest of the natural ecosystems converted (about 56%) become urban or other lands. Palm oil, but not corn (coarse grain) or soybeans, is the major crop planted in the converted natural ecosystem followed by coffee, coca bean and natural rubber from 1992 to 2008. On the contrary, average croplands for soybean in the 2000s in the MAI region were reduced by 55% compared to those in the 1990s. It is thus obvious that US biofuel production does not play a great role in land use changes in the MAI region. Although the SEA (Rest of South East Asia) and the SOA regions (South and Other Americas) meet conditions 1e3, these two regions do not meet condition 4. The average corn (coarse grain) plus soybean imports from United States in the 2000s in the SEA and the SOA region increased by 22% (20%) and 30% (29%) compared to corn (coarse grain) plus soybean imports during the 1990s, respectively. Rice and beans are the major crops grown on increased arable lands in the SEA region, while soybean is the major crop grown on increased arable lands in the SOA region, particularly in Argentina. Large increases in soybean areas in Argentina occurred from 2001 to 2005 because of its floating currency policy, biotechnology and double cropping [7]. Therefore, US biofuel production does not play a role in increasing soybean area in the SOA regions. If the correlation is significant, three regions (two regions in the coarse grain case) identified from conditions 1e4 (i.e., BRA, OCC and SSA) might potentially have converted natural ecosystems to croplands to produce corn (coarse grain) and soybean that were once imported from the United States. Both Pearson product-moment and Spearman’s rank correlation coefficients are used to determine whether the correlation between the percentage changes of croplands for corn (coarse

Table 1 e Results from testing conditions 1e4 [O: satisfying, X: not satisfying, n.a.: not applicable]. Condition Condition Condition Condition 1 2 3 4 Brazil Canada China and Hong Kong India Japan United States Central and Caribbean Americas East Asia European Union 27 Malaysia and Indonesia Middle Eastern and North Africa Oceania Countries Other East Europe and Rest of Former Soviet Union Rest of European Countries Rest of South Asia Rest of South East Asia Russia South and Other Americas Sub Saharan Africa

O (O) O (X) O (X)

O X X

O O X

O (O) X (X) X (X)

O (O) O (X) O (X) X (X)

X X X O

X O X O

O (O) X (O) n.a. X (X)

X (X) X (X)

X X

O X

O (O) O (O)

X (X)

O

O

X (X)

X (X)

O

X

X (X)

O (O)

O

O

O (O)

O (X)

X

X

X (X)

X (X)

X

X

O (O)

O (O)

X

O

X (X)

O (O)

O

O

X (X)

O (X) O (O)

X O

X O

O (O) X (X)

O (O)

O

O

O (X)

grain) plus soybean in those regions and the percentage changes in corn and soybean production dedicated to US biofuel production is significant at p < 0.05. Results show that there are no regions that have a significant correlation (at p < 0.05), implying that the percentage change in cropland for corn (coarse grain) plus soybean in regions which are corn (coarse grain) and soybean trading partners of the United States may not be significantly correlated with the percentage change in croplands for biofuel production in the United States up to the 2007 biofuel production increase. These results provide strong evidence that either: 1) no iLUC has occurred due to US biofuel production up through the end of 2007 or 2) that the empirical data cannot detect iLUC due to US biofuel production. Increased soybean production plays a major role in increased croplands in Brazil. Policies, biotechnology and growing soybean demand from China expanded soybean area in Brazil [16,17]. Wheat accounts for about 57% of the arable land increases from the 1990s in the OCC region (Oceania Countries), followed by barley and rapeseed accounting for

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27% and 14%, respectively. It is possible that declines in soybean imports from the United States would lead to increases in croplands for rapeseed in the OCC region, particularly Australia. However, rapeseed production in Australia began to increase in early 1990s. Better varieties, improved agronomy, crop monitoring program (Canola Check) and good prices are the primary reasons for the expansion of croplands for rapeseed in Australia [18]. Most increases in croplands for rapeseed in Australia occurred during 1998 and 1999 [19]. Therefore, the expansion of rapeseed production in the OCC region is not related to US biofuel production. Population pressure is a major driver for agricultural expansion in the SSA region (Sub Saharan Africa) [20,21], where extensification is a dominant trend [22]. Results for the correlation tests are summarized in the supplementary material. Even though the RSA region (Rest of South Asia) has a significant correlation with the annual percentage change in croplands for US biofuel production at p < 0.05, the RSA region fails to meet other conditions 1e4. Sensitivity analyses also show that using the annual changes as parameters (instead of the annual percentage changes) does not affect the findings. The coarse grain case also shows the similar results, which are summarized in the supplementary material.

4.

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This interpretation implies that iLUC has occurred, but that our analysis may not be adequate to detect it from the historical data. Thus more sophisticated empirical approaches should be developed to detect indirect land use change from the historical data. Another concern with our conclusions is based on the completeness of the FAO statistics. Unfortunately, there are no global data currently available that are as complete as the FAO statistics. In fact, some global economic models used to estimate iLUC also use the data from the FAO statistics. Thus, this concern does not appear to be relevant. Either these data are adequate for analysis or they are not, and if they are not, then iLUC estimates based on them are invalid also, and the discussion cannot proceed further. Very few previous studies have attempted to find empirical evidence for or against indirect land use change from the historical data. Most previous studies [1e5] have relied on global economic simulations. Therefore, we encourage additional effort to determine a potential threshold point for indirect land use change, to develop methodologies to observe indirect land use change in the historical data, or to determine that no measurable iLUC is taking place in the United States. Such efforts might reduce uncertainties in indirect land use change estimates or form the basis for better policies or standards for biofuels.

Discussion and conclusions

Results from these empirical tests are likely to be controversial. Our results can be interpreted in two different ways: 1. Biofuel production in the United States up through the end of 2007 in all probability has not induced indirect land use change. There are two feasible dependent conclusions to that might be drawn from this interpretation: 1) crop intensification may have absorbed the effects of expanding US biofuel production or 2) the effects of US biofuel production expansion may be simply negligible, and not resolvable within the accuracy of the data. Note that these results do not apply to increased biofuels production after 2008. Data do not yet exist to make comparable tests after 2008. It therefore appears that not every unit of biofuel production implemented to date has triggered indirect land use change. For example, the 1999 biofuel production increase (biofuel produced in a biofuel production facility built in 1999) has apparently not triggered indirect land use change, while we do not have information to say one way or the other whether the 2010 biofuel production increase triggered iLUC. In other words, the characteristics of indirect land use changes due to 2010 biofuel production increases may be quite different from those due to a 2015 biofuel production increase. This conclusion also suggests that ongoing agricultural intensification can continue to absorb the effects of biofuel production expansion without inducing indirect land use change, at least up to some level. 2. A contrary interpretation is that this empirical test simply fails to detect ongoing indirect land use change from the historical data.

Acknowledgments This work was funded by DOE Great Lakes Bioenergy Research Center (www.greatlakesbioenergy.org) supported by the US Department of Energy, Office of Science, Office of Biological and Environmental Research, through Cooperative Agreement DEFC02-07ER64494. Support was also provided by the Michigan Agricultural Experiment Station.

Appendix. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.biombioe.2011.04.039.

references

[1] Searchinger T, Heimlich R, Houghton RA, Dong F, Elobeid A, Fabiosa J, et al. Use of U.S. croplands for biofuels increases greenhouse gases through emissions from land-use change. Science 2000;319:1238e40. [2] US EPA. Renewable fuel standard program (RFS2) regulatory impact analysis. 1109 p. EPA-420-R-10-006. Washington DC: US Environmental Protection Agency. Available from: http:// www.epa.gov/otaq/renewablefuels/420r10006.pdf; 2010 February [accessed 11.04.11]. [3] California Air Resources Board. Proposed regulation to implement the low carbon fuel standard. 374 p. In: Staff report: initial statement of reasons, vol. I. Sacramento, CA: California Environmental Protection Agency. Available from: http://www.arb.ca.gov/fuels/lcfs/030409lcfs_isor_vol1.pdf; 2009 March 5 [accessed 11.04.11].

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[4] Argonne National Laboratory. The greenhouse gases, regulated emissions, and energy use in transportation (GREET) Computer model 1.8d. Argonne, IL: Argonne National Laboratory. Available from: http://greet.es.anl.gov/; 2010 [accessed 11.04.11]. [5] Tyner WE, Taheripour F, Zhuang O, Birur D, Baldos U. Land use changes and consequent CO2 emissions due to US corn ethanol production: a comprehensive analysis. Purdue University: GTAP; 2010 July. 90 p. (unpublished). Available from: http://www.transportation.anl.gov/pdfs/MC/625.PDF [accessed 11.04.11]. [6] Kim H, Kim S, Dale BE. Biofuels, land use change, and greenhouse gas emissions: some unexplored variables. Environ Sci Technol 2009;43:961e7. [7] Foreign Agricultural Service. International trade report: Argentina’s soybean complex competitiveness. 6 p. Washington DC: US Department of Agriculture. Available from: http://www. fas.usda.gov/oilseeds/highlights/06-04/ArgentinaCom petitiveness.pdf; 2006 April 14 [accessed 11.04.11]. [8] Food and Agriculture Organization of the United Nations. FAOSTAT. Available from: http://faostat.fao.org/ [accessed 11.04.11]. [9] United States Foreign Agricultural Service. Market and trade data. US Department of Agriculture. Available from: http:// www.fas.usda.gov/psdonline/psdQuery.aspx [accessed 11. 04.11]. [10] Tyner WE. The integration of energy and agricultural markets. Agr Econ 2010;41:193e201. [11] GAO. Biofuels. Potential effects and challenges of required increases in production and USE. Washington DC: US Government Accountability Office; 2009 August 25. 184 p. GAO-09-446. [12] Baffes J, Haniotis T. Placing the 2006/08 commodity price boom into perspective. Washington DC: The World

[13]

[14]

[15]

[16] [17]

[18]

[19]

[20]

[21] [22]

Bank; 2010 July. 42 p. Policy Research Working Paper 5371. National Agricultural Statistics Service. Washington DC: US Department of Agriculture. Available from: http://www.nass. usda.gov/ [accessed 11.04.11]. Renewable Fuels Association. Ethanol industry statistics. Washington DC: Renewable Fuels Association. Available from: http://www.ethanolrfa.org/pages/statistics [accessed 11.04.11]. Energy Information Administration. Short-term energy outlook e custom table builder. Washington DC: US Department of Energy. Available from: http://www.eia.gov/ emeu/steo/pub/cf_query/index.cfm [accessed 11.04.11]. Valdes C. Brazil’s booming agriculture faces obstacles. Amber Waves 2006;4(5):28e35. Barona E, Ramankutty N, Hyman G, Coomes OT. The role of pasture and soybean in deforestation of the Brazilian Amazon. Environ Res Lett 2010;5:024002. doi:10.1088/17489326/5/2/024002. Canola Association of Australia. Canola in Australia: the first thirty years. Australia. Available from: http://www.regional. org.au/au/gcirc/canola/; 1999 [accessed 11.04.11]. Foreign Agricultural Service. Oilseeds: world market and trade: 10e98. Washington DC: US Department of Agriculture; 1998 October. FOP 10e98. Maitima JM, Olson JM, Mugatha SM, Mugisha S, Mutie IT. Land use changes, impacts and options for sustaining productivity and livelihoods in the basin of lake Victoria. J Sustainable Dev AFr 2010;12:189e90. Scrieciu SS. Can economic causes of tropical deforestation be identified at a global level? Ecol Econ 2007;62:603e12. The World Bank. World development report 2008: agriculture for development. Washington DC: The World Bank; 2007. 386 p.