Climate and biodiversity impacts of crop-based biofuels
Climate and biodiversity impacts of crop-based biofuels Pieter Elshout
Pieter Elshout
PIETER ELSHOUT
Climate and biodiversity impacts of crop-based biofuels Colofon
Climate and biodiversity impacts of crop-based biofuels
Design/Lay-out Proefschriftenbalie, Nijmegen
Print Ipskamp Printing, Nijmegen
ISBN 978-94-028-1513-9
© Pieter Elshout, 2019
Climate and biodiversity impacts of crop-based biofuels
Proefschrift
ter verkrijging van de graad van doctor aan de Radboud Universiteit Nijmegen op gezag van de rector magnificus prof. dr. J.H.J.M. van Krieken, volgens besluit van het college van decanen in het openbaar te verdedigen op dinsdag 11 juni 2019 om 14.30 uur precies
door
Petrus Marinus Franciscus Elshout geboren op 22 september 1987 te Waalwijk Promotor Prof. dr. M.A.J. Huijbregts
Copromotoren Dr. R. van Zelm Dr. M. van der Velde (European Commission, Joint Research Centre, Ispra, Italië)
Manuscriptcommissie Prof. dr. ir. A.J. Hendriks Prof. dr. R.S.E.W. Leuven Prof. dr. A.P.C. Faaij (RUG) Table of Contents
Chapter 1 General Introduction 7
Chapter 2 A spatially explicit greenhouse gas balance of biofuel production: case studies of corn bioethanol and soybean biodiesel produced in the United States 17
Chapter 3 Greenhouse gas payback times for crop-based biofuels 37
Chapter 4 Greenhouse gas payback times for first generation bioethanol and biodiesel based on recent crop production data 53
Chapter 5 A spatially explicit data-driven approach to assess the effect of agricultural land occupation on species groups 69
Chapter 6 Global relative species loss due to first generation biofuel production for the transport 87
Chapter 7 Synthesis 103
Appendices 117 Literature 183 Summary | Samenvatting 207 Acknowledgements 215 Curriculum Vitae 219 Publications 221
chapter 1
General Introduction
General introduction 9
1.1 | Background
Fossil fuels are the dominant energy source in today’s world. Coal, oil and natural gas are used to generate electricity, to fuel vehicles, and to produce products, such as plastics.
Use of fossil fuels causes emissions of greenhouse gases (GHGs) like carbon dioxide (CO2), which cause global warming through increased radiative forcing. Estimates from 2010 predicted that fossil fuel usage contributes 65% of all global GHG emissions (IPCC 2014), hence indicating that fossil energy should be replaced with cleaner alternatives in order to limit the extent of global warming and climate change. Fuelled (i.e. non-electric) traffic is a major consumer of fossil fuels, and accounts for one third of the global CO2 emissions (Chapman 2007, Davis et al. 2010). A renewable alternative to fossil petrol and diesel are biofuels, which are broadly defined as fuels derived from any type of biomass.
Biofuels can, in theory, be a cleaner option than fossil fuels. Oil and gas are formed by the natural decomposition of dead organisms, which typically takes millions of years. The rate at which fossil fuels are mined and combusted greatly exceeds the rate at which new fossil fuels are formed. By contrast, biofuels are made from living biomass or feedstock.
During growth, this feedstock has taken up CO2 from the atmosphere in order to produce new biomass. During the biofuel’s production process and combustion, the same amount of CO2 is emitted, resulting in a zero net emission. In the case of feedstocks with a long rotation time, the CO2 will reside in the atmosphere for quite some time before being taken up again, and will therefore contribute to climate change in the meantime (Cherubini et al. 2011). In the case of short-rotation feedstocks, like annual crops, there is a short residence time of CO2 in the atmosphere, so the contribution to climate change of the CO2 emitted during the biofuel combustion is negligible (Cherubini et al. 2011). In that case, the loss of carbon from the soil, the addition of chemical substances (e.g. fertilizer), as well as any fossil fuels burned in the production process, could still contribute to an increase of GHGs in the atmosphere (Hoefnagels et al. 2010).
The prospect of producing renewable fuels with little GHG emissions led to governmental incentives that gave the emerging biofuel production industry a boost. Various industrialized governments have set targets regarding the future share of biofuels within the total fuel market, in order to comply with national and international climate policymaking. For example, the USA created the Renewable Fuel Standard Program to raise the share of renewable fuels to almost 10% between 2005 and 2017 (US EPA 2005). Likewise, the Directive on the Promotion of Use of Biofuels encourages EU Member States to achieve at least a 10% share of biofuels in total transport-related fuel consumption by 2020 (Renewable Energy Directive 2009). Additionally, investing in biofuels is considered to be an important strategy to reach the 2016 Paris Agreement goals (Goldemberg 2017). 10 Chapter 1
1.2 | Biofuels
There are currently two basic biofuel types, bioethanol and biodiesel, which in turn can be classified in four generations, based on the type of feedstock that is used. The first generation of bioethanol is made from energy-rich and edible plant parts, like corncobs, wheat grain and sugarcane, and the first generation of biodiesel is made from vegetable oils obtained from crops like rapeseed, oil palm and soybean. However, there are major downsides in using these food/feed crops as biofuel feedstocks. First, an expansion of agricultural land may be required to produce first generation biofuels, either to produce biofuel crops directly, or to produce food/feed crops that were replaced by biofuel crops elsewhere (Melillo et al. 2009). Such clearing of natural land may threaten local biodiversity through habitat loss, and it can cause large carbon emissions by removing biomass stored in the natural ecosystem. Second, the demand for biofuel crops created a shortage of land for food-based agriculture and may have spiked food prices in the countries from which these crops were imported (Mitchell 2008, Tenenbaum 2008). For example, international corn prices more than doubled between 2007 and 2008 after the USA Renewable Fuel Standard mandates for biofuels were expanded in 2007, and at least 20% to 40% of this price increase is directly attributable to the expansion of ethanol production in the USA (Wise 2012).
Instead of using edible plant parts, the second generation of biofuels uses agricultural waste products such as corn stover and wheat straw, as well as specifically grown energy crops like Miscanthus, switchgrass and jatropha. The main advantage of second generation feedstocks is that they do not hamper global food production directly, and thus, do not necessarily require agricultural expansion or intensification. However, the energy content of lignocellulosic biomass, and accordingly, biofuel yield, is lower than that of first generation feedstocks. Furthermore, amounts of crop residues available for biofuel production are limited, considering that farmlands require stover or straw to replenish soil carbon and nutrient stocks after harvest. On the other hand, Miscanthus and switchgrass are emerging as more promising feedstocks, with minimal agricultural inputs and fairly high yields, which may be further increased through improvements in agronomy and genetics (Heaton et al. 2008). Still, despite several decades of breeding varieties and developing cost-effective crop production and harvest methods, the perennial grasses have yet to breakthrough as large-scale commercial energy crops (Lewandowski et al. 2003, Tubeileh et al. 2016).
Since several decades, scientists have been developing a third and fourth generation of biofuels. The third generation uses oil from algae as a feedstock. Biofuels from algal oils may become a promising option as algae are fast growing, require little nutrients, and do not compete for land with food crops (Daroch et al. 2013, Slade & Bauen 2013). The definition of fourth generation biofuels varies, but normally covers all biofuels that make use of genetically engineered (micro)organisms. For example, scientists may soon General introduction 11
be able to engineer photosynthetic microorganisms that can directly convert sun light, water and CO2 into a liquid fuel (Aro 2016). However, current investment costs are high, and it will likely take years to decades of research and development before third and fourth generation biofuels are ready to be produced commercially (Wijffels & Barbosa 2010, Carroll 2013, Dutta et al. 2014).
Despite the alternatives, most biofuels are currently still produced using the first generation of feedstocks. Annually approximately 88 billion litres of bioethanol are produced worldwide, of which 60% is made from corn and 34% from sugarcane (Sorda et al. 2010, The New Climate Economy 2014). Almost 20 billion litres of biodiesel are produced each year, where rapeseed and soybean account for about 30%, and palm oil for 12%. The remaining 28% is made up of sunflower, jatropha and various other crops from which vegetable oils can be obtained. Overall, about 90% of the global biofuel production is situated in Brazil, the USA, and Europe. The USA is the largest producer of corn-based bioethanol in the world. Their biodiesel production, which is based mostly on soybean, is limited. The European countries produce little bioethanol, but they are the world’s largest producer of biodiesel, for which they mostly use rapeseed and some sunflower. Brazil is a main producer of soybean-based biodiesel and sugarcane-based bioethanol. Biodiesel production from oil palm is mostly located in Malaysia and Indonesia (Sorda et al. 2010).
Specific countries show a clear preference for one or two feedstock types, often based on the growth characteristics of the feedstock in the regional climate. In order to optimize the feedstock yields, farmers may adopt a farm management strategy that varies in the amount of fertilizers and pesticides that is applied to the fields. Distinction can be made between conventional farming, which typically comprises farming with high input of fertilizers and pesticides, and organic farming, which is characterized by low inputs. Additional choices are made by farmers with regard to farming machinery, crop rotation regimes and whether or not the fields are irrigated. Different management strategies have different impacts on the environment (Gomiero et al. 2011, Tuomisto et al. 2012), as will be discussed in the next two sections.
1.3 | Climatic impacts
The scientific community has long criticized the perception that biofuels in general, and first generation biofuels in particular, reduce GHG emissions by definition. One may argue that the combustion of biofuels only releases the same amount of CO2 to the atmosphere that was previously stored during crop growth, resulting in a net emission of zero. However, the combustion is only one part of the biofuel life cycle. Two other phases should be included to come up with the total GHG emissions related to biofuel production. 12 Chapter 1
First, this line of thought assumes that fertile land to grow the feedstock is readily available. In reality, fertile land may be acquired through destruction of natural forests and grasslands. These natural ecosystems store large amounts of carbon in their biomass and both carbon and nitrogen in the soil. Upon destruction of the original vegetation, often referred to as land transformation or land use change, these compounds will be
released to the atmosphere as the biogenic CO2 and nitrous oxide (N2O). This mechanism is especially clear in the cases of South America and Southeast Asia, where rainforests are cut and burned down and peat lands are drained to make way for oil palm, soybean and sugarcane plantations. Whenever the land transformation took place in the location where the biofuel feedstock is cultivated, it is called direct land use change (dLUC; Panichelli & Gnansounou 2008). Indirect land use change (iLUC) occurs when a biofuel feedstock is grown on existing arable lands that used to produce crops for food or feed purposes. Displacing food or feed-producing croplands with biofuel feedstock-producing croplands has little to no influence on the carbon dynamics of the cropland itself. However, when assuming an equilibrium in the demand for food and feed, these products will have to be produced elsewhere if production is ended in a certain location. New croplands may emerge in the same country, or in developing countries, where labour and raw materials are cheaper (Solomon & Barnett 2017). Whenever carbon-rich ecosystems are removed in favour of food or feed production, the GHG emissions may be attributed to the biofuels that triggered the displacement of crop cultivation. Several studies have shown that the GHG emissions due to iLUC heavily influence the biofuel carbon balance (e.g. Melillo et al. 2009, Lapola et al. 2010).
Second, fertilizers, pesticides, irrigation water or other substances may be added to the field during feedstock cultivation, also known as land occupation or, simply, land use. GHG emissions may be caused during application of these substances (e.g. in the case
of nitrogen fertilizers that cause N2O emissions), or during their production process.
Additional GHG emissions (including methane; CH4) are the result of using fossil-fueled machinery on the field and during refining. In turn, the production of this machinery also led to GHG emissions, thus culminating in a rather complex network of production systems, each with its own inputs and outputs.
Various studies have derived GHG balances for specific (mostly first generation) biofuels produced in specific countries or regions (Von Blottnitz & Curran 2007). The impact of corn-based bioethanol production in the USA is particularly well studied, with special emphasis on variations in the biofuel production process (Kim & Dale 2005, 2008, 2009), as well as on the influence of methodological choices on the outcomes of the assessments (Kim & Dale 2002). Kim and Dale (2009) studied the regional variations in GHG emissions of corn-based bioethanol and soybean-based biodiesel in the USA. Using county-specific data from nine states, they found the regional variation due to farming location to be a factor 2 to 7. Cherubini et al. (2009), Hoefnagels et al. (2010) and Lange (2011) calculated the GHG emissions related to a variety of energy crops, including rapeseed, palm oil, soybean, General introduction 13
sugarcane, maize and wheat in a selection of countries. These studies report mostly reductions in GHG emissions when producing biofuels instead of fossil fuels, although exact outcomes vary greatly with the type of ecosystem that it sacrificed. Other impact assessments cover e.g. soybean in the USA (Kim & Dale 2009), switchgrass in the USA (Davis et al. 2012), wheat in Switzerland (Gnansounou et al. 2009) and oil palm in Brazil (De Souza et al. 2010). All found that replacing fossil fuels by the studied biofuel production system may potentially reduce GHG emissions but that methodological choices like allocation procedure greatly influence the actual GHG balance of the biofuel.
Another way to compare the GHG impacts of biofuels and those of the fossil fuels they replace is through calculation of carbon payback times (Gibbs et al. 2008) or GHG payback times (which more clearly indicates that besides CO2 other GHGs are also considered). This metric considers the initial GHG emissions from land transformation to be an “investment” that may be paid back if the polluting fossil fuel is replaced by a cleaner biofuel for a certain period (i.e. the payback time). The shorter the payback time, the sooner a net reduction in GHG emissions will occur following displacement of fossil fuels by biofuels and, hence, the more suitable a biofuel is as an alternative. Several studies have used this metric: Fargione et al. (2008) compared five biofuel types grown in various ecosystems, while Gibbs et al. (2008) focused on ten feedstocks that can be grown in the tropics, and Havlik et al. (2011) compared four first generation and two second generation biofuel types. Case studies include work on corn ethanol production in the USA (Searchinger et al. 2008, Kim et al. 2009), and on biodiesel from oil palm grown in Brazil (De Souza et al. 2010) and Southeast Asia (Danielsen et al. 2009).
Overall, various studies have quantified the GHG emissions associated with the production of different first generation biofuels through calculation of either GHG balances or GHG payback times. However, variations in data selection, methodological choices and modeling assumptions make it practically impossible to come up with comparable outcomes and consistent recommendations to policy makers. There is currently a lack of studies that quantify the impact of global biofuel production on GHG emissions, whilst addressing the variability that follows from these local conditions.
1.4 | Biodiversity impacts
Loss of natural habitat due to deforestation is one of the largest threats to tropical biodiversity, and the increasing need for soybean, sugarcane and oil palm has been a major driver of this deforestation in the 1990’s and early 2000’s (Fearnside 2001, Fitzherbert et al. 2008, Kim et al. 2015) and even today (Obidzinski et al. 2015). The rapid expansion of oil palm plantations in Southeast Asia has brought species like the orang-utan on the brink of extinction (Buckland 2005). In temperate regions the impact of biofuel production on biodiversity may be less visible, especially since the number of species present has 14 Chapter 1
already deteriorated during centuries of human activity - mostly land use change for food production (Henle et al. 2008). Even though transition from food crops to biofuel feedstocks will hardly have any additional impact on biodiversity if the farm management remains the same, one may argue that the loss of species since destruction of the original ecosystem could still be attributed to the biofuel production. After all, the field could have been returned to a (semi-)natural state had there been no need to grow the feedstock. Habitat loss leading to biodiversity loss may be the direct consequence of biofuel-related land use in that same location, or could be caused indirectly when food or feed production is replaced and relocated elsewhere i.e., iLUC (Hennenberg et al., 2010).
Biodiversity can also be affected indirectly through climate change caused by the (biogenic) GHG emissions related to biofuel production (see above). In the atmosphere, GHGs cause an increased radiative forcing, which subsequently results in a temperature rise (De Schryver et al. 2009). Such an increased temperature could directly exceed the temperature tolerance levels of plants and animals, or cause shifts in mating and migration cycles (Walther et al. 2002). Changing weather conditions, including an increased occurrence of droughts and floods, may further affect the flora and fauna. It takes decades to centuries for biological communities to adjust to new climates (Menéndez et al. 2006), so most species will be unable to cope with the rapid changes that occur nowadays. Urban (2015) estimated that on average 7.9% of the earth’s species is bound to go extinct due to climate change.
Quantification of biodiversity impacts related to the production of products, such as biofuels, comes with several difficulties. First, there are several biodiversity indices, including species abundance, species richness, Shannon’s diversity index, Simpson’s evenness, and Berger-Parker dominance, and the choice of index alters the interpretation of results (Morris et al. 2014). Second, biodiversity is affected through many impact pathways that are highly complex and uncertain. Recent efforts like the ReCiPe project (Huijbregts et al. 2016) do provide a framework to implement the main impact categories. However, connecting these impact categories to a production process requires detailed emission and land use data, which are often unknown. Impacts from land use are especially difficult to quantify, considering the spatial and temporal variation in land use practices, and the possibility of restoring land to a semi-natural state once it is abandoned.
Overall, land use and climate change rank as the two most important drivers of global biodiversity loss (Sala et al. 2000), which shows the relevance of studying the impact of biofuel production on biodiversity. Nevertheless, Cherubini and Strømman (2011) calculated that only about 9% of the studies in the field of Life Cycle Assessment included the direct impacts of land use in their assessments. Many studies on biofuels and biodiversity use a qualitative approach (e.g. Groom et al. 2008, Wiens et al. 2011, Laurance et al. 2014), which is valuable in summing up the impact pathways of biofuel production and policies that could possibly confine these impacts. Still, in order to allow for proper General introduction 15
comparison between the impacts of different biofuel types, using feedstocks grown in different regions and under different farm management strategies, quantification of the impacts through modeling is essential. As mentioned in the previous section, GHG emissions vary greatly between locations because of differences in the carbon and nitrogen stocks of the original ecosystem, and because of local differences in farm management. Also, different regions house different amounts of species, which may be more or less susceptible to land use and land use change. This pleads for the use of spatially explicit data in impact assessments of biofuels. Up to now, there has been a lack of studies that quantitatively assess the impact of biofuel-related land use and climate change on global biodiversity in the first place, let alone that they use spatially explicit data to do so.
1.5 | Goal and outline
The goal of this thesis was to assess the importance of location, crop type and management type on life cycle GHG emissions and biodiversity impacts of crop-based biofuels. A better understanding of the role of these factors is urgently required, as it allows for identification of the most suitable biofuel production processes in different regions of the world. The focus is on first generation biofuels because these are the most widely produced biofuels to date. Spatially explicit data were used in order to deal with local differences in e.g. crop production, carbon stocks, fertilizer application and species vulnerability.
Apart from the introduction, the PhD thesis consists of five individual chapters and a synthesis.
Chapter 2 provides a spatially explicit GHG balance of agricultural crop production with a focus on corn and soybean cultivated in the USA. Both chapters 3 and 4 use the concept of GHG payback times to quantify the impact on GHG emissions of replacing fossil fuels with biofuels. In chapter 3, potential biofuel production is simulated all over the world, while chapter 4 focuses on a selection of actual biofuel producing countries. These chapters show which biofuel production systems perform better than fossil fuels, and under which circumstances. Chapters 5 and 6 do both address the issue of habitat loss due to biofuel-related land transformation. In chapter 5, the impact of habitat loss on local species richness is derived using survey data. Chapter 6 quantifies the combined impacts of biofuel-related GHG emissions and habitat loss on global relative species loss. Finally, chapter 7 is the synthesis, which reviews the performance of the different biofuel production systems and discusses the influence of methodological choices.
chapter 2
A spatially explicit greenhouse gas balance of biofuel production: case studies of corn bioethanol and soybean biodiesel produced in the United States
P. M. F. Elshout, R. van Zelm, M. Hauck, and M. A. J. Huijbregts 18 Chapter 2
Abstract
Greenhouse gas (GHG) balances of biofuels are subject to spatial variation that results from differences in climate, soil type, farm management intensity, and the original vegetation that was removed for crop cultivation. In order to assess this variation, we calculated spatially explicit GHG balances of biofuel production in the United States, based on current corn and soybean cultivation.
The GHG emissions from corn- and soybean-based biofuel production were calculated per MJ of bioenergy. Five major sources of GHG emissions were covered, including (1) removal of natural biomass upon initial land transformation, (2) change of soil organic carbon stocks upon initial land transformation, (3) change of soil nitrogen stocks upon initial land transformation, (4) application of nitrogen fertilizers, and (5) energy use and material inputs during the agricultural phase and fuel refining. Spatially explicit models and comprehensive databases were used to estimate the GHG emissions from these sources. GHG balances are provided grid-specifically, and as state averages.
Assuming a 30-year crop cultivation period and allocating emissions amongst byproducts based on mass, we found that the state-average GHG emissions for corn-based bioethanol
production range between -8.1∙10-4 to 1.2∙10-1 kg CO2-eq per MJ. Allocating emissions
based on energy content resulted in a range between 6.8∙10-3 and 1.6∙10-1 kg CO2-eq per MJ. For soybean-based biodiesel, the state-averages ranged between -8.5∙10-2 and 3.1∙10-1
kg CO2-eq per MJ. Biogenic CO2 emissions due to initial removal of the natural biomass contributed heavily to the GHG balance of soybean-based biodiesel, covering on average 55% of the total GHG emissions. For corn-based bioethanol, all five sources of GHG emissions contribute similarly at on average 15% to 25%. The outcomes for both biofuels depended heavily on the total period of crop cultivation that was assumed (30 or 100 years). The present study underlines the importance of accounting for land transformation in the assessment of GHG emissions of crop-based biofuels, and the added value of using spatially explicit models. Greenhouse gas balance of biofuel production in the United States 19
2.1 | Introduction
Growing population and transportation demand have put agricultural soils under increasing pressure to produce food, animal feed, fiber, and biofuels. Across the world, natural systems such as forests and grasslands have been altered to acquire fertile soils. As a result, over 40% of the land surface was occupied by croplands and pastures by the beginning of this century (Foley et al. 2005) and this percentage will increase further in the coming decades (Hertel 2011). However, natural forests and grasslands sequester large amounts of carbon in their biomass and soil, varying from approximately 110 Mg carbon per ha of temperate grassland to almost 700 Mg carbon per ha of wetland (IPCC 2001). In contrast, the carbon density of a generic cropland is estimated at approximately 80 Mg carbon per hectare (IPCC 2001), indicating that land transformation from natural vegetation to croplands has led to net emissions of carbon to the atmosphere. Emissions of carbon dioxide (CO2) and other greenhouse gases (GHGs) due to biomass destruction and soil disturbance upon land transformation heavily influence the GHG balance of agricultural products (Fargione et al. 2008, Borjesson & Tufvesson 2011), including crop-based biofuels. Replacing fossil fuel with biofuels is often advocated as a means to reduce GHG emissions and comply with climate goals (US EPA 2005, Renewable Energy Directive 2009), but various assessments on the GHG balance of biofuels have shown that the type of biofuel and location of crop cultivation (and, subsequently, the type of natural vegetation that is removed) strongly determines whether or not replacing fossil fuels with biofuels will truly effectuate a net decrease of GHGs (Cherubini et al. 2009, Hoefnagels et al. 2010, Creutzig et al. 2014).
Besides GHG emissions due to land transformation, other major sources of GHGs related to biofuel production are nitrogen emissions from the soil, GHGs from fertilizer production and application, and GHGs from fossil fuel combustion during agricultural machinery use, irrigation, transportation and fuel refining (Cherubini et al. 2009). Different regions may require different intensities of farm management (especially fertilizer application and irrigation) due to local variation in climate and soil type. Additionally, climate, soil type, and farm management intensity influence the crop yield, which in turn influences the GHG balance of the biofuel. Location thus influences the environmental performance of a biofuel production system in multiple ways, underlining the importance of assessing the impact of crop-based biofuels in a spatially explicit manner.
For the last few decades, increasing effort has been put into composing a solid methodology to quantify the impacts from land use (i.e. occupation) and land use change (i.e. transformation) for biofuels and other agricultural products in environmental impact assessments and life cycle assessments (LCAs; e.g. Lindeijer 2000, Köllner & Scholz 2007, Milà i Canals et al. 2007, Bare 2011). The amount of GHG emissions is commonly included in such assessments (e.g. Kim & Dale 2005, Reijnders & Huijbregts 2008, Gnansounou et al. 2009, Kendall & Chang 2009, Lange 2011, Castanheira & Freire 2013), but outcomes vary greatly with different methodological choices, use of different models, and differences 20 Chapter 2
in data quality (Warner et al. 2014, Goglio et al. 2015). Also, the spatial coverage of land use dynamics in such studies is mostly limited to the use of country-specific or regional averages (e.g. Reijnders & Huijbregts 2008, Gnansounou et al. 2009, Lange 2011). Two exceptions are the work from Gibbs et al. (2008) and Elshout et al. (2015), who used the concept of carbon payback times to spatially explicitly quantify the GHG emissions related to a variety of biofuel feedstocks cultivated throughout the tropics and throughout the total potential cultivation area, respectively. Studying a more confined area, such as a single biofuel-producing country like the USA, has the advantage of local models and census data. For example, Kim & Dale (2009) and Kim et al. (2009) used the DAYCENT model to assess the GHG emissions related to biofuel production from corn and soybean for 40 counties of the USA Corn Belt. However, they did not account for the removal of the original, natural vegetation in areas of current agriculture but, instead, quantified the potential emissions of forest vegetation removal in a scenario analysis. Hence, studies that use high resolution spatial data to derive local GHG balances of biofuels while also fully accounting for the impact of land transformation and occupation, have been sparse, and for the USA, a major biofuel producer, they are absent to our knowledge.
In this study, we derived spatially explicit GHG balances of potential corn-based bioethanol and soybean-based biodiesel produced in the Midwest and Southern USA, based on recent crop cultivation data. Corn and soybean were selected because these crops cover most acreages in the USA and both are vital for the USA biofuel industry, accounting for approximately 38% and 25% of the national biofuel production in 2015, respectively (USDA ERS 2018a,b). The present study is the first to provide grid-specific GHG balances of these two biofuels and assess the variation thereof at this level of detail. Additionally, we analysed which sources of GHG emissions contribute most to the GHG balances, in order to identify which sources are essential in GHG impact assessments of biofuels.
2.2 | Material & Methods
2.2.1 | Functional unit
In the present study, the GHG balance of pure (i.e. not blended) corn-based bioethanol and soybean-based biodiesel was calculated per MJ of bioenergy produced. We assumed that a kg of bioethanol and biodiesel provides 29.7 and 40.5 MJ of bioenergy, respectively, based on Ecoinvent documentation (Weidema et al. 2013). Both corn and soybean were assumed to be grown under monocrop farming (i.e. without crop rotation).
2.2.2 | Allocation
Besides the grain used for bioethanol production, corn produces significant amounts of stover, which is commonly used as animal feed, and is becoming increasingly important Greenhouse gas balance of biofuel production in the United States 21
as a feedstock for cellulosic biofuels (Blanco-Canqui & Lal 2007). Therefore, using the ratios from Luo et al. (2009), we allocated 37.5% of the GHG emissions during corn cultivation to the stover based on mass, and 11.8% based on economic value (2007 cost data).
The practical use of soybean stover is limited due to low stover yields, low nutritional value, difficulty in harvesting and collecting dry stover, and the need to keep residues as soil cover for erosion prevention (Helwig et al. 2002). It is common practice in LCAs of soybean to neglect the production of stover (Dalgaard et al. 2008, Wang et al. 2011). Therefore, we assigned all GHG emissions during soybean cultivation to the beans.
No byproducts were considered for the fuel-refining phases of both biofuels.
2.2.3 | Reference state
In the present study, we assumed that natural vegetation would still be present if no agriculture had developed. Therefore, GHG emissions from land transformation in the past were retrospectively attributed to all crop biomass produced throughout the cropland lifetime. The natural background in each grid cell was estimated based on the distribution of so-called ecofloristic zones as reported by Ruesch and Gibbs (2008). Distinction was made between various types of natural forests and natural grasslands, with different carbon pools in biomass and soil (see below). We considered any other land uses that may have been present in between (e.g., pasture, managed forestry) irrelevant for our study.
2.2.4 | GHG balance calculations
Five major sources of GHG emissions were covered, including (1) removal of natural biomass upon initial land transformation, (2) change of soil organic carbon stocks upon initial land transformation, (3) change of soil nitrogen stocks upon initial land transformation, (4) application nitrogen fertilizers, and (5) energy use (e.g. by agricultural machinery and irrigation) and material inputs during the agricultural phase (e.g. seeds, pesticides and fertilizers) and fuel refining (e.g. water, electricity). Emissions of CO2, N2O, and CH4, were summed based on their respective 100-year global warming potentials (GWP100s): 1, 265 and 30 CO2-equivalents (IPCC 2013). The GWP100s were used for both fossil GHG emissions and biogenic GHG emissions from land transformation. Conceptually, the same amount of biogenic GHGs that are emitted upon land transformation would be recaptured into new biomass and soil when a cropland is abandoned and returns to a (semi-)natural state. Therefore, the period during which the emitted GHGs reside in the atmosphere and affect the climate via enhanced radiative forcing would be reduced compared to GHGs from fossil sources. However, since it is unknown how long it will take before the croplands in the USA are returned to a (semi-)natural state, we considered the GWP of the biogenic emissions from land clearing as equivalent to the GWP of fossil GHG emissions. 22 Chapter 2
The GHG balance (G; in kg CO2-eq per MJ bioenergy) for biofuel y made from crop x cultivated in in grid cell i was calculated using the equation:
-1 Falloc × (∆Cbiomass,x,i + ∆Csoil,x,i + ∆Nsoil,x,i) ×〖LT + Nfert,x,i -1 (2.1) Gx,i = ( + 〖GHGother,x,y ) Ey Yx,i × 〖BFx,y
where Falloc is the allocation factor, [Cbiomass is the difference between the amount of
carbon stored in natural biomass and that in crop biomass (kg CO2-eq per ha), [Csoil is the difference between the amount of organic carbon stored in natural soil and that in
cropland soils (kg CO2-eq per ha), [Nsoil is the difference between the amount of nitrogen
stored in natural soil and that in cropland soils (kg CO2-eq per ha), Nfert is the amount of
nitrogen emitted due to fertilizer application (kg CO2-eq per ha per year), GHGother is the
USA average amount of fossil CO2, fossil CH4, and N2O emitted during the production and use of chemicals, during farm machinery (excluding nitrogen fertilizer application) and
during biofuel refining (kg CO2-eq per kg biofuel), Y is the crop yield (kg crop per ha per year), BF is the biofuel conversion efficiency (kg biofuel per kg crop), and E is the energy
content (MJ per kg biofuel). The emissions that follow from land transformation (Cbiomass,
[Csoil, and [Nsoil) were treated differently in two scenarios for biofuel production. In the first scenario, we attribute the emissions from land transformation to all crop biomass that has been produced since. Here, the emissions were amortized over the total period of crop cultivation on a specific field LT, which was set to 30 years as a default, similar to previous assessments of USA agricultural products (Searchinger et al. 2008, US EPA 2010). As a sensitivity analysis, additional calculations were done setting the cultivation period to 100 years, representing croplands that have been in use for a more extensive period. In the second scenario, we do not account for land transformation at all, representing a way of reasoning that emissions from land transformation are not to be attributed to current biofuel production if already established croplands are used for feedstock cultivation. Hence, only ongoing GHG emissions during agricultural land use, i.e., emissions from fertilizer production and use, irrigation and other farm processes, are accounted for in this scenario.
The GHG balance results were presented on a resolution of 1x1 km grid cells. However, while the calculations were done on a 1x1 km grid level, some of the input data were only available at the county-, state-, or national level (Table 2.1). This means that multiple grid cells were assigned the same value of a parameter, based on their location in a particular county or state. In Appendix A, an example is provided of the spatially explicit calculation of the GHG balance. Greenhouse gas balance of biofuel production in the United States 23
table 2.1 | Scales and sources of the data used to calculate GHG balances for corn bioethanol and soybean biodiesel production in the Midwest and Southern USA The GHG balances were calculated on a 1x1 km grid resolution, where each grid cell was assigned the appropriate value of a parameter available at a larger scale, based on its location.
Parameter Scale Source Note natural biomass carbon regional IPCC carbon density map provided as default carbon densi- stock (Ruesch & Gibbs 2008) ties for combinations of GLC2000 class, FAO ecofloristic zone, and continent soil organic carbon regional GTAP Technical Paper (Gibbs provided as default SOC for (SOC) content of et al. 2014) combinations of agro-ecological croplands and natural zones and country vegetation soil nitrogen stock regional directly related to SOC as per IPCC guidelines (IPCC 2006) N fertilizer application state LCA Digital Commons provided as an average input rates database (http://www. during 7 years of crop production lcacommons.gov) GHG emissions from national Ecoinvent v3 (Weidema et provided as emissions during farming activity and al. 2013) a USA average crop production input/machinery pro- process duction crop yield county USDA online database provided as census-based an- (http://www.nass.usda.gov) nual production quantities and acreages
2.2.5 | Biomass carbon
The amounts of carbon stored in natural grasslands were based on the IPCC Tier-1 default values for the above- and belowground biomass of 18 natural grassland classes (some for the entire world, others specific to North America) reported by Ruesch & Gibbs (2008). The biomass carbon stock of natural forests was collected from Gibbs et al. (2014), who combined several global and regional datasets. The carbon stored in corn and soybean biomass was set to zero, because the short generation time of both annual crops impedes long-term carbon storage (Elshout et al. 2015).
2.2.6 | Soil organic carbon
The soil organic carbon (SOC) stocks of natural forests and corn and soybean croplands were collected from Gibbs et al. (2014), who provide average values for each agro-ecological zone in the USA The SOC of natural grasslands was derived using soil parameters from the Harmonized World Soil Database (FAO et al. 2012) in those areas that are considered grasslands according to the GLC2000 land cover map (Bartholome & Belward 2005). For our calculations, we used the SOC contents in the top 30 cm of soil. 24 Chapter 2
2.2.7 | Soil nitrogen
The N2O emissions from the soil due to mineralization were assumed directly proportional
to the carbon emissions from the soil. The amount of N2O emitted from the soil during cultivation of crop x in location i was calculated using the following equation (as per IPCC 2006):
1 M (2.2) 〖 N = C × × EF × N2O ∆ soil,x,i soil,x,i 1 R MN
where R is the C:N ratio of the soil organic matter (kg C / kg N) and EF1 is the emission factor
for mineralization of nitrogen (kg N2O-N / kg N). We applied a default C:N ratio of 15 kg C
/ kg N (IPCC 2006), and an emission factor of 0.01 kg N2O-N / kg N (IPCC 2006, Flynn et al.
2012). The molar masses MN2O (44 g / mol) and MN (28 g / mol) were used to convert the
amount of emitted nitrogen to the amount of N2O.
2.2.8 | Nitrogen fertilizer application
The annual amount of nitrogen fertilizer applied was collected from the LCA Digital Commons database (http://www.lcacommons.gov). This database provides state- specific average fertilizer application data for corn cultivation in a total of 19 states for the year 2005, and data for soybean cultivation in a total of 18 states for the year 2006. Distinction was made between seven types of nitrogen fertilizer, of which anhydrous ammonia, nitrogen solutions, and urea were applied most. For soybean, nitrogen fertilizer application was about tenfold smaller than for corn, as soybean is able to fix large amounts of nitrogen from the atmosphere (Dalgaard et al. 2008, Kim & Dale 2009).
The amount of N2O emitted following fertilizer application Nfert was split up in direct
emissions (that is N2O emitted directly during fertilizer application) and indirect emissions
(that is N2O emitted through volatilization and re-deposition of NH3 and NOx, and through leaching and runoff of N during fertilizer application). For the direct emissions, we multiplied the nitrogen fertilizer application rates with the nonlinear emission responses from Shcherbak et al. (2014). The indirect nitrogen emissions for both crops were calculated using default emission factors from the IPCC (2006). A more detailed explanation of these calculations can be found in Appendix A.
2.2.9 | Other GHG emissions
GHG emissions during the production of inputs, such as seeds, fertilizers and pesticides, during farming activities, such as energy use for irrigation and tillage, and during biofuel refining were calculated using life cycle inventory data from the ecoinvent v3 database (Weidema et al. 2013). Note that cropland irrigation was not considered in the soybean cultivation, since only approximately 8% of the USA soybean cropland is frequently irrigated (USDA; 2007 data). Greenhouse gas balance of biofuel production in the United States 25
Regarding the agricultural phase, the ecoinvent database provides average emissions of fossil CO2, fossil CH4, and N2O per kilogram of corn or soybean produced in the USA. These emissions were converted to CO2 equivalents per hectare per year, using the average crop yields of the USA farming processes of corn and soybean, as assumed in ecoinvent. This allowed the GHG emissions of farming activities, machinery, and input to be related to the actual farming processes based on reported county-specific crop yields (see section 2.2.10). An overview of the agricultural processes and inputs accounted for in the present study is given in Table A1.
2.2.10 | Crop yields
Crop yields were derived by dividing actual annual production quantities of corn and soybean by the acreages on which they are cultivated. The required data was collected from the USDA online database (http://www.nass.usda.gov/Charts_and_Maps/Crops_ County/index.asp), which provides county-specific, census-based production quantities and acreages for the year 2012. Corn and soybean data were available for 2,638 counties throughout 49 states and 2,162 counties throughout 45 states, respectively. Production quantities reported in numbers of bushels were converted to kilograms using the standard of 25.4 kg per bushel of corn grain, and 27.2 kg per bushel of soybeans.
2.2.11 | Analysis
Calculating grid-specific GHG balances allowed for grid-by-grid (1x1 km) comparison between both crops, and identification of the location where cultivation results in the lowest GHG emissions per unit of crop produced. Additionally, we calculated and compared GHG balances for the different counties, states, and for the whole Midwest and Southern USA region. For each county, we selected the median GHG emission of all grids within its borders. No weighting was applied, as crop production data were not available on a grid level. For each state and for the entire region, the county-specific median GHG emissions were weighted based on the share of crop production in that county (based on the USDA census data) compared to the total production in the state or the entire region, respectively. Finally, we assessed the contribution of the main carbon and nitrogen sources (loss of biomass carbon, SOC, and soil nitrogen, emission of fertilizer nitrogen, and GHG emissions from machinery use and production of inputs) to the total GHG balance.
2.3 | Results
2.3.1 | Corn
Overall, data availability allowed for the calculation of spatially explicit GHG balances for corn-based bioethanol in 2,991 unique locations (each comprised of a varying number 26 Chapter 2
of original grid cells), distributed over 836 counties in 19 states, and for soybean-based biodiesel in 1,954 unique locations, distributed over 1,339 counties in 18 states. The grids for which agricultural data was available covered most of the Midwest and Southern USA regions, where over 90% of the USA corn and soybean acreages are located (USDA, 2013 data). The results discussed below are those using a 30 year time horizon and, in case of corn, mass-based allocation, unless stated otherwise.
The location-specific GHG emissions for bioethanol production ranged from -8.1∙10-4 to -1 1.2∙10 kg CO2-eq per MJ (95% range) (Figure 2.1a), with an weighted national average of -2 5.2∙10 kg CO2-eq per MJ (Table 2.2). Average GHG emissions in the Midwest and Southern -2 -2 USA regions were 4.8∙10 and 8.7∙10 kg CO2-eq per MJ. The weighted average GHG emissions for all 19 states are given in Table 2. Using economic value-based allocation rather than mass-based allocation changes the GHG emissions attributed to the corn grain with -9% to +52% (median: +28%), resulting in location-specific GHG emissions of -3 -1 6.8∙10 and 1.6∙10 kg CO2-eq per MJ (95% range; Figure 2.1b) and an USA weighted average -2 of 6.6∙10 kg CO2-eq per MJ (Table 2.2). Negative balances indicate a net increase of carbon stored in the soil upon land transformation that outweighs the sum of positive GHG emissions. Based on data from Elshout et al. (2015), the GHG balance of petrol (i.e. the -2 fossil benchmark of bioethanol) is 8.8∙10 kg CO2-eq per MJ of fossil energy. Using mass- based allocation, 63% of the locations show a GHG balance smaller than the GHG balance of petrol, and in case of economic-value based allocation, this is 43% of the locations.
GHG emissions from nitrogen fertilizer use were found to be the most important contributor to the GHG balance of corn at about 25% of total emissions, followed closely by all four other sources of GHG emissions, each contributing to 15% to 20% of total emissions (Figure 2.3a). In turn, the greatest contributor to the machinery use and production of inputs category was the production of nitrogen fertilizer at 33% of the category total, and 7% of the overall total GHG emissions. The contribution of carbon release from natural biomass, change in SOC, and change in soil nitrogen varies considerably, owing to the spatially explicit data used for these three categories.
2.3.2 | Soybean
When assuming a 30-year time horizon, the location-specific GHG emissions for biodiesel -2 -1 production ranged from -8.5∙10 and 3.1∙10 kg CO2-eq per MJ (95% range; Figure 2.2), with -2 an overall weighted average of 6.3∙10 kg CO2-eq per MJ. The average emissions for the -2 Midwestern and Southern USA were 5.0∙10-2 and 6.3∙10 kg CO2-eq per MJ, respectively. Table 2 shows the state-specific outcomes. Compared to fossil diesel, which has a GHG -2 balance of 8.2∙10 kg CO2-eq per MJ of fossil energy according to Elshout et al. (2015), soybean-based biodiesel has a smaller balance in 33% of the locations. Greenhouse gas balance of biofuel production in the United States 27
Here carbon loss from natural biomass was found to be the most important contributor to the GHG balance at 55% of total emissions (Figure 2.3b), followed by the change in SOC content upon land transformation at 38%. The contribution of the nitrogen emissions from soil mineralization and fertilizer application, as well as the GHG emissions from machinery use and production of inputs to the GHG balance was typically less than 5%.
2.3.3 | Period of crop cultivation
A
B
figure 2.1 | GHG balance of corn-based bioethanol throughout the Midwest and Southern USA, assuming 30 years of crop cultivation. GHG emissions are allocated amongst the grain and stover based on (A) their mass and (B) their economic value. States and counties in white are excluded due to a lack of data on crop yields and/or fertilizer application. FBM = fossil benchmark, which is 8.8∙10-2 kg CO2-eq per MJ for petrol.
The GHG balances of both biofuels are sensitive to the selected period of crop cultivation, as the biogenic GHG emissions from land transformation are allocated to the crop biomass produced during this period. When assuming a period of 100 rather than 30 28 Chapter 2
years, the national average GHG emission for corn-based bioethanol decreases by 14% -2 -2 to 4.5∙10 kg CO2-eq per MJ and those for soybean-based biodiesel by 61% to 2.4∙10 kg
CO2-eq per MJ. Maps for this scenarios can be found in Appendix A, Figures A1 (corn) and A2 (soybean). While extending the period of crop cultivation resulted in a smaller GHG balance throughout most of the locations, an increase was observed in locations with a negative GHG balance. These balances will turn positive when the net gain of carbon in the soil is diluted to such an extent that it no longer outweighs the other sources of GHG emissions.
figure 2.2 | GHG balance soybean-based biodiesel throughout the Midwest and Southern USA, assuming 30 years of crop cultivation. All emissions were allocated to the bean. States and counties in white are excluded due to a lack of data on crop yields and/or fertilizer application. FBM = fossil -2 benchmark, which is 8.2∙10 kg CO2-eq per MJ for diesel.
Greenhouse gas balance of biofuel production in the United States 29
table 2.2 | Mean GHG emissions of corn bioethanol and soybean bioduesel derived at the level of states and regions. County-specific emissions were weighted based on the relative share of crop production in that specific county compared to the total production in the state or region. For corn bioethanol, the outcomes of the two different allocation approaches (i.e., mass allocation and economic allocation) are presented.
GHG balance (kg CO2-eq per MJ) 30 years cultivation 100 years cultivation corn soybean corn soybean mass economic no allocation mass economic no allocation state/region allocation allocation allocation allocation Colorado 4.0E-02 4.9E-02 - 4.1E-02 5.0E-02 - Georgia 1.2E-01 1.6E-01 - 9.1E-02 1.2E-01 - Illinois 4.8E-02 6.0E-02 3.2E-02 4.2E-02 5.2E-02 1.4E-02 Indiana 7.8E-02 1.0E-01 1.5E-01 4.8E-02 6.1E-02 5.2E-02 Iowa 2.8E-02 3.2E-02 -1.8E-02 3.5E-02 4.2E-02 -4.0E04 Kansas 3.3E-02 3.9E-02 -3.4E-02 3.7E-02 4.5E-02 -6.8E-03 Kentucky 1.0E-01 1.4E-01 1.6E-01 5.4E-02 6.9E-02 5.4E-02 Louisiana - - 1.6E-01 - - 5.2E-02 Michigan 8.9E-02 1.2E-01 2.0E-01 5.7E-02 7.3E-02 6.5E-02 Minnesota 5.1E-02 6.4E-02 1.2E-02 4.5E-02 5.5E-02 8.8E-03 Mississippi - - 1.3E-01 - - 4.6E-02 Missouri 6.6E-02 8.6E-02 5.3E-02 4.1E-02 5.2E-02 1.9E-02 Nebraska 4.1E-02 4.9E-02 -3.0E-02 4.3E-02 5.2E-02 -3.9E-03 New York 9.8E-02 1.3E-01 - 6.0E-02 7.6E-02 - North Carolina 9.9E-02 1.3E-01 2.6E-01 6.7E-02 8.5E-02 8.5E-02 North Dakota 3.6E-02 4.3E-02 -3.2E-02 4.2E-02 5.1E-02 -4.9E-03 Ohio 8.9E-02 1.2E-01 1.9E-01 5.9E-02 7.5E-02 6.2E-02 Pennsylvania 9.6E-02 1.3E-01 - 6.1E-02 7.8E-02 - South Dakota 2.7E-02 3.0E-02 -3.2E-02 3.3E-02 3.9E-02 -5.1E-03 Tennessee - - 1.7E-01 - - 5.7E-02 Texas 7.3E-02 9.3E-02 - 6.9E-02 8.8E-02 - Virginia - - 2.6E-01 - - 8.5E-02 Wisconsin 1.00E-01 1.3E-01 2.0E-01 5.6E-02 7.1E-02 6.6E-02 Midwest USA 4.8E-02 6.0E-02 5.0E-02 4.2E-02 5.2E-02 2.0E-02 Southern USA 8.7E-02 1.1E-01 2.0E-01 7.0E-02 8.9E-02 6.5E-02 Total region 5.2E-02 6.6E-02 6.3E-02 4.5E-02 5.5E-02 2.4E-02 30 Chapter 2
A 100 G G G G G G G G G G G G G G G G G
G G 75 G G G G G G G G G G G G G G G G G G G G G G G G G G 50
25 relative contribution (%) contribution relative
0 biomass C SOC soil N fertilizer N energy use and material input sources B
100
75
50 G
G G G G G G G G G G G G G G 25 G G G G G G G relative contribution (%) contribution relative
G G G G 0 biomass C SOC soil N fertilizer N energy use and material input sources figure 2.3 | Relative contribution of the five main sources of GHG emissions to the total emissions in a specific grid cell for (A) corn-based bioethanol and (B) soybean-based biodiesel, assuming 30 years of cultivation. Included are (1) the loss of carbon stored in natural biomass upon land transformation, (2) the change in SOC upon land transformation and occupation, (3) the change in soil nitrogen upon land transformation and occupation, (4) emission of nitrogen from fertilizer application, and (5) GHG emissions from energy use and material inputs, including farm machinery use, production of seeds, fertilizer production, crop refining etc.
2.3.4 | No land transformation scenario
Not attributing GHG emissions from land transformation to current feedstock cultivation hardly influences the GHG balance of corn-based bioethanol, as the overall weighted -2 -2 averages become 4.2∙10 kg CO2-eq per MJ for mass-based allocation and 5.1∙10 kg CO2- eq per MJ for economic value-based allocation. Locations with previously negative GHG balances get positive balances once SOC is not accounted for, which partly compensates for the lowering of the GHG balance in other locations. Nevertheless, GHG balances are below the GHG balance of fossil petrol in 97% and 99% of the locations for the respective allocation methods. Greenhouse gas balance of biofuel production in the United States 31
In the case of soybean-based biodiesel, neglecting land transformation strongly influences the GHG balance, as the land transformation-related emissions dominate the total GHG emissions of biodiesel production. It reduces the overall weighted average to -3 7.0∙10 kg CO2-eq per MJ, and the GHG balance becomes smaller than the fossil benchmark in all locations.
2.4 | Discussion
2.4.1 | Comparison with previous work
The GHG balances calculated in the present study were compared with a limited number of previous studies that provide GHG emissions for corn- and soybean-based biofuel production process or the cultivation phase specifically. Kim and Dale (2005) found that bioethanol from corn from Iowa, continuously grown during a 40-year period, results in
28,124 kg CO2-eq per ha. Using the conversion factor from the present study, this number -1 translates to 1.8∙10 kg CO2-eq per MJ. When also adopting a 40-year cultivation period, we -2 calculate a GHG balance for Iowa of 3.1∙10 kg CO2-eq per MJ, which is considerably lower. -1 Searchinger et al. (2008) calculated a GHG balance of 2.4∙10 kg CO2-eq per MJ for corn- based bioethanol production, including the effects of land-use change but excluding carbon uptake credit, which again exceeds our outcomes. On the other hand, Liska et al. (2009) calculated balances that are more in line with the present study, varying between -2 -2 3.1∙10 and 7.6∙10 kg CO2-eq per MJ for various type of corn-ethanol systems located in Iowa and Nebraska. Unfortunately, methodological deviations such as the allocation strategy, assumed cultivation period, and even whether or not biomass carbon release is incorporated, hamper an in-depth comparison between all studies. This does underline the need for a standardized approach in biofuel impact assessments.
Regarding soybean production, studies that derive GHG balances are sparse. However, our outcomes agree with the work of Castanheira & Freire (2013), who focused on soybean cultivation in Argentina and Brazil. They took into account the transformation of vegetation ranging from degraded grasslands (which have relatively small carbon pools) and tropical rainforest (which have large carbon pools), and report GHG emissions of 0.1 to 17.8 kg CO2-eq per kg soybean. In our USA-based assessment temperate grasslands and forest are replaced, which theoretically have a more intermediate carbon pool. Indeed, we calculated a national average of 1.9 kg CO2-eq per kg soybean, assuming a 30-year cultivation period. Castanheira & Freire (2013) found that more than 70% of the total GHG emissions during soybean cultivation is represented by emissions related to land transformation, which is slightly lower than the combined contributions of carbon loss from the biomass and SOC pools calculated in our study. 32 Chapter 2
Finally, an increase of SOC upon land transformation was observed in about half of the grids, thus suggesting a net storage of carbon in the soil. In grids where such a negative balance in SOC came combined with only small emissions from aboveground biomass, this resulted in an overall negative GHG balance. This was the case in 6% of the corn locations and 36% of the soybean locations, as SOC had a more prominent contribution in the case of soybean. However, studies like Kim et al. (2009) and Guo and Gifford (2002) only report decreases in SOC contents when converting natural vegetation into corn croplands, also in the USA. Comparison of the SOC contents used in the present study (i.e. Gibbs et al. 2014 data) with IPCC default SOC contents for temperate forests and grasslands showed only small differences: Gibbs et al. (2014) yield an average of 62.7 Mg C per ha for forests while the IPCC data suggests approximately 70 Mg C per ha (IPCC 2001; IGBP-DIS data recalculated to 30cm depth). For grasslands, the results are alike, with an average of 52 Mg C per ha from Gibbs et al. (2014) and approximately 50 Mg per ha from the IPCC. The average SOC stock of croplands provided by Gibbs et al. (2014) is 64 Mg C per ha, respectively, which shows that an increase of SOC upon land transformation was primarily found in the areas where natural grasslands are replaced by corn. It is highly unlikely that crop production itself effectuates an increase in SOC, so possibly the average SOC for croplands is calculated based on croplands that have replaced exceptionally carbon-rich natural ecosystems and consequently became relatively carbon-rich themselves. An in-depth analysis of the model mechanics and input data might reveal the underlying cause, but such an analysis was outside the scope of this study. Notably, similar to the present work, the global assessment by Elshout et al. (2015) also found a net storage of carbon in large parts of the Midwest, despite using an alternative agricultural model to derive SOC contents.
Overall, we find that, in the USA, producing biofuel from corn is preferred over biofuel from soybean, from a GHG point-of-view. Lowering the GHG balances of both biofuel types should still have priority in the light of (inter)national climate agreements. The GHG-balance of corn-based bioethanol may be lowered foremost through avoidance of carbon-rich ecosystems in case of agricultural expansion, as this would limit the carbon
loss from biomass, as well as carbon and nitrogen loss from soil. Reducing N2O emissions from fertilizer application could be another strategy, although yield reductions should be avoided. Less fertilizer may be needed when farmers adopt favorable management strategies like crop rotation with maintenance of surface residue cover and reduced tillage, which could improve SOC stocks as a side effect (Havlin et al. 1990, Yost et al.
2013). For soybean, the GHG balance mostly depends on CO2-emissions related to the land transformation phase, meaning that avoiding carbon-rich ecosystems in case of agricultural expansion would be most effective in lowering the GHG balance.
2.4.2 | Limitations and uncertainties
Calculation of the GHG balance for corn-based bioethanol and soybean-based biodiesel production required the use of several models and databases, each with their own Greenhouse gas balance of biofuel production in the United States 33
limitations. First of all, data were available at different spatial scales, which influenced the quality of our spatially explicit results. Some emissions, like those from fertilizer use and other farm activities, are likely to vary between farms, as they depend directly on choices of the farmer, influenced by local conditions such as soil type, temperature, and precipitation. In the present assessment, however, data on fertilizer application rates and other farming activities (e.g. irrigation, machinery use) were only available at the state- and national level, respectively. Therefore, future research would benefit from more information on the spatial differences in farming activities across the USA, and the GHG emissions associated with those activities. As long as such data are unavailable or unobtainable, we consider the data set used in the present study to be the best approximation. Since the removal of natural biomass was found to be a significant contributor to the total GHG emissions of corn and soybean cultivation, we propose that a more spatially detailed specification in natural biomass carbon stocks (beyond the ecofloristic zones used in the present study) should have first priority. Initiatives that include inventory and remote sensing data of natural vegetation (e.g. Kellndorfer et al. 2012) may provide more insight in local differences in natural biomass carbon stocks, and would allow policy makers to select areas with smaller carbon stocks for agricultural expansion.
Methodological choices, assumptions and definitions are a major source of variation in GHG emissions from land use across studies, along with the models used and quality of the data (Warner et al. 2014). Hence, our results are valid only within the following framework of adopted assumptions. The SOC stocks of croplands, as collected from Gibbs et al. (2014), are derived as average SOC values for all croplands in a particular agro-ecological region. This means that other crops grown in the target regions of the USA (e.g. wheat, alfalfa and rapeseed) are also represented in the average SOC calculations. However, given that corn and soybean dominate the agricultural areas of the Midwest and Southern USA (FAOSTAT, 2014 data; Prince et al. 2001), we consider the SOC stocks from Gibbs et al. (2014) to be appropriate for the present study. The same issue applies to the presence of different farm management strategies in the regions. No-tillage farming generally allows for larger SOC stocks than conventional tillage (West & Post 2002). About 25% of the croplands in the USA are predominantly maintained using no-till farming (Derpsch et al. 2010), so the SOC contents from Gibbs et al. (2014) may be slightly higher than would be the case in croplands under frequent tilling, which we simulate in the present study.
As mentioned in the methods section, our calculations were based on situations of mono- cropping. However, biannual rotation of corn and soybean is a common agricultural practice in the USA (Karlen 2004, Plourde et al. 2013). In a scenario that alternates between corn and soybean and produces an equal amount of biofuels out of both, the overall GHG -2 balance would be the average of both separate balances, i.e. 5.8∙10 kg CO2-eq per MJ bioenergy as an USA average. However, this does not account for the positive effects of such crop rotation, including a potential yield increase and enhanced storage of carbon and nitrogen in the soil (Havlin et al. 1990, Bullock 1992), which would consequently lower 34 Chapter 2
the GHG balance of the crop and biofuel. Alternating biofuel feedstocks with food or feed crops like wheat, barley or alfalfa (or corn or soybean for food/feed purposes) with only one crop cultivated per year does hardly affect the GHG balance of the biofuel. However, when rotating corn or soybean with an additional winter or spring crop in the same year, one should allocate the GHG emissions from land transformation amongst the food/ feed crops as well. As the cropland would still produce the same amount of biofuel in this scenario, the GHG balance of the biofuel would be reduced.
We set the carbon storage by crop biomass at zero, because the annual harvest of the crops restrains long-term storage. Alternatively, Gibbs et al. (2008) propose to use 50% of the maximum carbon storage by the crops (that is right before harvest) as a year- round average. When adopting this approach by assuming carbon densities of 0.23 kg C per kg corn and 0.22 kg C per kg soybean (50% of the dry matter C-contents provided by ecoinvent (Nemecek and Kägi, 2007), the net biogenic carbon emissions due to removal of natural biomass are lowered with on average 9% for corn and 1% for soybean.
Various studies have shown that it is possible to maintain crop yields and soil quality up to 100 years of cultivation, provided that optimal crop and fertilizer management is implemented (e.g. Reeves 1997, Manna et al. 2007). However, when assuming crop cultivation on a particular field for as long as 100, it is most likely that the cropping system will evolve, leading to the development of better crop varieties, more efficient fertilizers, and improved technologies for cropping and soil treatment (Buyanovsky et al. 1996). In the present study, we assumed that the current farming system (that is present-day yields, fertilizer input, and farm management) would remain unchanged, regardless of the total period of crop cultivation that was selected. By neglecting any possible improvements of yields and farm technologies in the future, our study may overestimate the emissions per unit of crop cultivated on the long term.
2.5 | Conclusions
In the present study, spatially explicit models were used to calculate the GHG balance of bioethanol from corn and biodiesel from soybean, focusing on locations in the Midwest and Southern USA where both crops are actually grown. For bioethanol, we found location- -4 -1 specific emissions ranging between -8.1∙10 to 1.2∙10 kg CO2-eq per MJ bioenergy (95% -2 -1 range). For biodiesel, the total emissions ranged between -8.5∙10 and 3.1∙10 kg CO2-eq per MJ (95% range). Especially the GHG balance of soybean-based biodiesel was highly dependent on the amount of carbon released from the natural biomass and soil upon land transformation. For corn-based bioethanol, biomass carbon and SOC contributed similarly to soil nitrogen, nitrogen fertilizer and energy use / material inputs. The outcomes depended on the total period of crop cultivation that was assumed (30 or 100 years), as well as the procedure of allocating the emissions amongst co-products. Greenhouse gas balance of biofuel production in the United States 35
Overall, our study highlights the importance of taking into account land transformation in the assessment of agricultural products, and the added value of using spatially explicit models in these assessments. We recommend that spatially explicit GHG balances, such as those derived in the present study, be used in LCAs of corn- and soybean-based biofuels. Calculation of spatially explicit GHG balances is advised, as differences in the natural biomass carbon stocks, soil carbon and nitrogen stocks, fertilizer requirements, and crop yields lead to significant variation in local GHG balances. Decision makers may keep in mind the relative contribution of the different GHG sources when selecting new locations for feedstock cultivation. In general, cropland expansion in areas with large carbon pools could lead to relatively higher GHG emissions, given that natural biomass removal is a major contributor to GHG emissions.
chapter 3
Greenhouse gas payback times for crop-based biofuels
P. M. F. Elshout, R. van Zelm, J. Balkovic, M. Obersteiner, E. Schmid, R. Skalsky, M. van der Velde, and M. A. J. Huijbregts (2015). Nature Climate Change 5: 604-610 38 Chapter 3
Abstract
A global increase in the demand for crop-based biofuels may be met by cropland expansion, and could require the sacrifice of natural vegetation. Such land transformation alters the carbon and nitrogen cycles of the original system, and causes significant greenhouse- gas emissions, which should be considered when assessing the global warming performance of crop-based biofuels. As an indicator of this performance we propose the use of greenhouse gas payback times (GPBT), that is, the number of years it takes before the greenhouse gas savings due to displacing fossil fuels with biofuels equal the initial losses of carbon and nitrogen stocks from the original ecosystem. Spatially explicit global GPBTs were derived for biofuel production systems using five different feedstocks (corn, rapeseed, soybean, sugarcane and winter wheat), cultivated under no-input and high- input farm management. Overall, GPBTs were found to range between 1 and 162 years (95% range, median: 19 years) with the longest GPBTs occurring in the tropics. Replacing no-input with high-input farming typically shortened the GPBTs by 45 to 79%. Location of crop cultivation was identified as the primary factor driving variation in GPBTs. This study underscores the importance of using spatially explicit impact assessments to guide biofuel policy. Greenhouse gas payback times for crop-based biofuels 39
3.1 | Main
Over the past few decades, many countries have adopted bioenergy directives that aim to increase the share of renewable energy and to reduce greenhouse gas (GHG) emissions from the use of fossil fuel (Sorda et al. 2010). The production of liquid biofuels for the transportation sector in particular has experienced substantial growth since 1990 (Faaij 2006). Despite rapid developments in the field of second- and third-generation biofuels (produced from lignocellulosic biomass and microalgae, respectively), only first generation biofuel production from energy crops, such as corn, soybean, rapeseed, and sugarcane, is commercial at present (Naik et al. 2010, Singh et al. 2011). A growing demand for energy crops in the future may be met either by increasing the amount of agricultural land or by increasing crop production on existing agricultural land. Expansion of agricultural land requires the sacrifice of other land cover, such as abandoned lands, pastures or natural systems. The last of these can be especially problematic from a climatic point of view, given that natural forests and grasslands store large amounts of carbon that may be released to the atmosphere on their conversion to agricultural use, thereby disturbing the global carbon balance (Johnson et al. 2014, Popp et al. 2014). Most of the carbon in natural terrestrial systems is stored in biomass and soil (Guo & Gifford 2002). Removal of natural biomass may result in large releases of carbon through postharvest combustion and decomposition. Crops also store carbon in their biomass during growth, but the regular harvest of many crops impedes long-term carbon storage. In addition, agricultural land use may alter the balance between inflows and outflows of the soil carbon pool through changes in vegetation, increasing erosion and soil disturbance through farming activities such as tillage and irrigation (Don et al. 2011, Olson et al. 2012). Conversion of native forest to croplands may result in a large loss of soil carbon stocks, releasing more than 40% of the original stock to the atmosphere (Guo & Gifford 2002).
Changes in the global carbon balance due to land conversion are especially relevant in the case of biofuel production given that carbon and nitrogen emissions from deforestation and land use intensification may nullify the environmental benefits of displacing fossil fuels (Fargione et al. 2008, Searchinger et al. 2008). The impact of biofuel production on the global carbon balance can be quantified by calculating carbon payback times (Gibbs et al. 2008, Kim et al. 2009, Havlík et al. 2011, Lamers & Junginger 2013, Yang & Suh 2014), also known as carbon debt repayment times (Fargione et al. 2008), carbon break-even points (Lamers et al. 2013), or carbon compensation points (Danielsen et al. 2009). The carbon payback time is defined as the period over which the total GHG savings due to displacement of fossil fuels by biofuels equals the initial losses in ecosystem carbon stocks caused by land conversion. These measures are analogous to the more widely known energy payback times that are used in impact assessments of, for example, photovoltaic systems. Here, we propose the term greenhouse gas payback time (GPBT) in assessing the impact of crop-based biofuel production on the balance of multiple GHGs. These GPBTs depend on the following: the amount of biogenic carbon dioxide (CO2) emitted to the atmosphere 40 Chapter 3
due to the removal and burning or decay of the original carbon-storing biomass; the
amount of biogenic CO2 and dinitrogen oxide (N2O) emitted to the atmosphere due to soil mineralization and (de)nitrification processes following land conversion, that is, the net difference between the original soil stocks and those of the bioenergy system; the
annual amount of N2O emitted to the atmosphere due to fertilizer application during crop cultivation; the amount of fossil GHGs emitted per unit of produced bioenergy (including emissions from machinery use and transportation) relative to the amount of fossil GHGs emitted per unit of fossil energy that is produced and combusted; the amount of bioenergy gained through biofuel production, which depends on the feedstock yield, feedstock-to-biofuel conversion efficiency, and energy content of the biofuel.
The GHG emissions associated with the production of crop-based biofuels (including related land-use change) have been assessed extensively before (Adler et al. 2007, Kim & Dale 2008, 2009, Hoefnagels et al. 2010). Previous assessments have shown that emissions vary with the type of crop that is cultivated, the location of cultivation, and the intensity of farm management practices. However, most previous work has consisted of case studies that focused on specific countries or regions, and researchers have thus failed to identify the implications of growing various crops worldwide. Development of standardized, globally applicable metrics, such as GPBTs, is a pre-condition for progress toward a sustainable biofuel trade. Therefore, the first aim of our study was to derive spatially explicit, high-resolution GPBTs for potential crop-based biofuel production on a global scale, taking into account the conversion of natural vegetation to feedstock cropland. These GPBTs were calculated for the production of bioethanol from corn grain, sugarcane sucrose and winter wheat grain, which could replace fossil gasoline, and for production of biodiesel from rapeseed and soybean oil, which could replace fossil diesel. The cultivation of the biofuel crops was simulated spatially explicitly, using the global crop model EPIC (see Appendix B). Second, we assessed the reduction in GPBTs when high- input croplands replace no-input croplands of the same crop (that is, farm intensification). Finally, we analyzed how geographic location, management regime and crop type, affect the GPBTs. To our knowledge, the present study is the first to calculate GPBTs at a global scale, and the first to quantitatively assess the relative importance of the three primary drivers of GPBT variation.
3.2 | By-products
The crop-based biofuel production processes studied here produce significant quantities of by-products to which part of the GHG emissions should be allocated. Examples are corn stover, rapeseed meal and soybean meal, and dried distiller grains with solubles (DDGS) from corn and wheat, which are used as animal feed, and sugarcane bagasse, which can be used in electricity production. Three commonly used methods to allocate emissions between the biofuel and its by-products are those based on energy content, mass, and Greenhouse gas payback times for crop-based biofuels 41
market value (Wang et al. 2011). The outcomes of the GPBT calculations vary with these different approaches. When allocation is included on an energy-basis, GPBTs are on average 61% shorter than when applying no allocation. For mass-based and market value- based allocation, this is 67% and 30%, respectively. The results given below are those using energy-based allocation. The outcomes of mass-based and market value-based allocation can be found in Appendix B.
3.3 | Cropland replacing natural vegetation
When taking the replacement of natural vegetation by croplands as a starting point for biofuel production, the GPBTs for our biofuel production systems varied from 1 to 162 years (95% range; median of 19 years) depending on the crop, management intensity, and location. The spatial distribution of global GPBTs for each crop-management combination is shown in Figure 3.1. The longest GPBTs were found in the tropical regions of South America, Africa, and Southeast Asia, where we calculated a median GPBT of 51 years (95% range of 7 to 313 years) when converting tropical moist forest to cropland for biofuels and 27 years (95% range of 3 to 164 years) when replacing tropical grasslands. Shorter GPBTs were found in the temperate and boreal regions, where the median GPBT was 20 years (95% range of 3 to 103 years) when converting temperate broadleaf forest to biofuel cropland, 19 years (95% range of 1 to 155 years) when replacing temperate coniferous forests, 10 years (95% range of 0 to 87 years) when replacing boreal forests and taiga, and 6 years (95% range of 0 to 54 years) when replacing temperate grasslands. In less than <1% to 3% of the grids, particularly in the temperate and boreal regions, we found negative GPBTs, which resulted from cropland soil organic carbon (SOC) stocks that exceeded the total carbon stock in the soil and biomass of the reference vegetation.
Under no-input farming, rapeseed-based biodiesel production yielded the shortest GPBTs, that is, a global median of 21 years (95% range of 1 to 404 years). Bioethanol production from sugarcane under no-input farming yielded the longest GPBTs, with a global median of 60 years (95% range of 8 to 209 years). In the tropical regions all crops had longer GPBTs, with soybean and sugarcane performing best: median GPBTs of 77 years (95% range of 13 to 141 years) and 90 years (95% range of 13 to 190 years), respectively. Under high-input farming, the median GPBTs for all crops were 45% to 79% shorter compared with no-input farming. Corn and winter wheat performed best in this case: median GPBTs of 6 years (95% range of 0 to 29 years) and 8 years (95% range of 0 to 57 years), respectively. 42 Chapter 3
1a Corn – no input 1b Corn - high input
1c Rapeseed - no input 1d Rapeseed - high input
1e Soybean - no input 1f Soybean - high input
1g Sugarcane – no input 1h Sugarcane – high input
1i Winter wheat – no input 1j Winter wheat – high input
GHG payback times (years)
A 0 0 0 0 0 0 0 / < 1 2 5 0 0 0 N - - - 1 5 5 0 0 0 - - > 1 2 0 0 5 0 1 figure 3.1 | Global maps of GPBTs for the five energy crops under no-input and high-input farm management. White areas (for example, deserts and ice cover) were deemed unsuitable for agricultural land use a priori. Grey areas were excluded because their modelled crop yields were below the yield threshold (see Appendix B). These maps were constructed at a 5-arcmin resolution. Greenhouse gas payback times for crop-based biofuels 43
We compared our GPBTs with those obtained in a few studies in which payback times were obtained for specific crop-based biofuels (Fargione et al. 2008, Gibbs et al. 2008, Kim et al. 2009, Yang & Suh 2014). In general, we found wider GPBT ranges than those previous studies. Given that the biomass carbon losses due to land conversion are similar (Gibbs et al. also used IPCC data; Fargione et al. used average data from the literature), the differences can be attributed to the highly variable SOC and crop yields. For example, we found short (or, in some cases, negative) GPBTs when the SOC content in cropland was higher or only slightly lower than the total carbon in the natural system. These circumstances were not observed in the previous studies, which used relatively high estimates of SOC in the natural system and applied a default decrease in the amount of SOC following conversion to cropland, based on scientific literature (Fargione et al. 2008, Gibbs et al. 2008). However, we obtained long GPBTs in grids where the SOC in the cropland was lower than the total carbon in the natural system and where the crop yield was low. The previous studies typically focused on crop cultivation in regions where the crops are grown at present or where the climatic conditions allow for their cultivation in the near future. In the present study, however, we simulated potential crop cultivation worldwide, which implies that more regions with relatively low yields were included (for example, sugarcane cultivation in temperate regions and rapeseed cultivation in tropical regions). This inclusion explains why more variability was observed in the present study than in previous studies.
3.4 | No input versus high input
Large differences in GPBTs were associated with the use of two types of farm management. We observed that replacing no-input farming with high-input farming tends to shorten the GPBTs, often by more than 100 years (Figure 3.2). High-input farming generally resulted in greater SOC losses to the atmosphere and higher GHG emissions from fertilizer and machinery compared with farm management without the input of fertilizer and irrigation. Nevertheless, cultivating biofuel crops under high input resulted in shorter GPBTs in 95% to 99% of the global grids due to higher crop yields, which offset the higher GHG emissions. Although lower rates of fertilizer application evidently lead to lower GHG emissions, we conclude that a reduction in fertilizer application will be counterproductive if it results in large decreases in yields. However, it should be noted that the two farm management scenarios analyzed in the present study differed only in the application of nitrogen fertilizer and irrigation. Other farm management practices that affect GHG emissions, such as tillage, potassium and phosphorus fertilizer application, stover removal, and crop rotation, were not addressed. 44 Chapter 3
figure 3.2 | Histograms of the GPBTs derived for the five energy crops under the two types of farm management. The different colors reflect the two main classes of natural vegetation that were replaced by agricultural land: (1) forests and (2) rangelands, based on the classification by Ruesch and Gibbs (2008). The dashed line shows the 20-year boundary: Grid cells located on the right side of the line do not accommodate sustainable bioenergy cropping. The low yield bar shows the percentage of grids for which no GPBTs were calculated because the modeled yield was less than the threshold value.
3.5 | Explained variance
We identified the effects of crop type, management system, and location on the variance in grid-specific GPBTs. Overall, 90.7% of the variance in GPBTs was attributable to differences in location (Appendix Table B5). The other factors were of less importance: farm management and the type of crop accounted for 6.5% and 2.5% of the variance in the GPBT, respectively, and the remaining 0.3% was due to crop-management interactions. Greenhouse gas payback times for crop-based biofuels 45
These findings stress the importance of accounting for spatial differences when assessing the influence of crop-based biofuel production on GPBTs.
Although significant differences in GPBTs were found between different crops, the effect of crop type on the global GPBTs was small compared with the influence of location. However, most crops included in the present study were annual crops, which have no long term storage of carbon owing to frequent harvest. Perennial grasses and permanent crops (for example, oil palm) generally produce higher yields and have the potential to sequester more carbon in soil and biomass (Don et al. 2011, Flynn et al. 2012). Sugarcane, the only perennial crop in our study, was indeed found to have higher average yields (7 to 25 times) and slightly higher SOC stocks (3% to 7%) than the other crops, which were partly negated by a more inefficient crop-to-fuel conversion. Earlier studies on the effects of biofuel produced from permanent crops were inconclusive. For example, Gibbs et al. (2008) reported shorter carbon payback times for oil palm biodiesel compared with several annual crop-based biofuels, whereas Fargione et al. (2008) reported that palm biodiesel yielded the longest carbon payback times. Lignocellulosic biomass, such as switchgrass, Miscanthus and grassland mixtures are frequently considered to be suitable replacements for degraded croplands (Tilman et al. 2006, Fargione et al. 2008), but the effect of replacing natural vegetation with these crops has not been extensively studied. However, under favorable conditions, lignocellulosic crops can maintain higher SOC contents than mature forests and native grasslands (Conant et al. 2001, Zan et al. 2001), and therefore biofuel production from lignocellulosic biomass is worth further investigation.
3.6 | Implications
Whether biofuel production in a specific location may be favorable or unfavorable for mitigating climate change depends on the total production period of the cropland during which it is used for biofuel feedstock cultivation in that location (Kim et al. 2009). For example, the Intergovernmental Panel on Climate Change (IPCC) proposes an average of 20 years as the typical cultivation period before cropland is converted to a different land use (IPCC 2006). In this case, therefore, the GPBT in a specific location should be shorter than 20 years for the biofuel production to be beneficial versus the use of fossil fuels in terms of total GHG emissions. Additional locations would qualify as beneficial when assuming a cropland production period of 30 or possibly 100 years (Kim et al. 2009). Frequency distributions of the GPBTs indicating the effects of assuming various cropland production periods are shown in Figure 3.3. Under no-input farming, the GPBT was shorter than 20 years in only 14% to 43% of the grids. When assuming a 100-year cropland production period (Kim et al. 2009), this areal extent increases to 62% to 93% of the grids. A similar trend was evident in high-input farming: there, the GPBT was shorter than 20 years in 42% to 82% of the grids, and shorter than 100 years in 74% to 93% of the grids (Figure 3.3). 46 Chapter 3
3.7 | Limitations and uncertainties
The data used in our GPBT calculations come with uncertainties and limitations that should be considered when interpreting the results. First, the crop model simulations with EPIC include only a limited number of natural land cover types (that is, deciduous forest, coniferous forest, rangelands), which are used to simulate the global natural soil carbon content. Therefore, the crop model simulations do not fully encompass the complexity of certain natural systems such as peatlands and mangroves, which are particularly relevant in that they store large amounts of carbon and nitrogen in their organic soils (Jauhiainen et al. 2005, Donato et al. 2011). Previous studies indicated that replacing tropical peatlands -1 -1 with oil palm plantations results in the release of up to 35 Mg CO2-eq∙ha ∙yr from the soil alone during the first 25 years of cultivation (Germer & Sauerborn 2008, Murdiyarso et al. 2010), thereby leading to a payback time of 75 to nearly 700 years (Danielsen et al. 2009). Gibbs et al. (2008) calculated payback times ranging between 750 (sugarcane) and 12,000 (soybean) years when agriculture replaces peat forests.
Second, the IPCC maps (Ruesch & Gibbs 2008) used to derive the biomass carbon stocks in natural ecosystems do not fully address local differences in carbon densities. The maps show generic carbon stocks for a variety of natural land cover types, and thus any variation within each land cover type is not accounted for. Such variation may be expected, for example, in the case of temperate forests, where land-use history varies greatly among forest sites (Keith et al. 2009). Nevertheless, we conclude that the IPCC maps adequately address the most important spatial differences in global biomass carbon stocks for the purposes of the present study.
Third, the fossil GHGs emitted during cultivation and refining of biofuel crops are based on data from a limited number of countries. The global average GHG emission data used in the present study were based on studies from Switzerland, France, Germany, Spain, the USA, and Brazil (Weidema et al. 2013). A comparison of the available country-specific fossil GHG emissions indicated that the greatest international variations, that is, 32% and 11%, were associated with the cultivation of rapeseed and refining of rapeseed, respectively, which demonstrates that the variation between these countries is moderate to low. However, other than in the few countries mentioned above, no attention has been paid to international differences in farming techniques, transportation, or refining technology, and, consequently, on fossil GHG emissions in the biofuel production chain. Projecting emissions based on this selection of countries to all countries across the globe will probably underestimate the emissions (and GPBTs) from developing countries that lack optimal techniques and infrastructure for the cultivation and refining of feedstock crops. However, the available data are too limited to improve the coverage of this assessment. Greenhouse gas payback times for crop-based biofuels 47
figure 3.3 | Histograms of the GPBTs derived for the five energy crops under the two types of farm management. The colors denote the two primary classes of natural vegetation that were replaced by agricultural land ,that is, forests and rangelands, based on the classification by Ruesch and Gibbs (2008). The dashed lines denote various cropland production periods that may be assumed, which affect the number of grids (expressed as percentages) where biofuel production is beneficial versus the use of fossil fuels in terms of total GHG emissions. The low yield bar denotes the percentage of grids for which no GPBTs were calculated because the modelled yield was less than the threshold value. 48 Chapter 3
Fourth, we did not account for the potential effects of a changing climate and higher
atmospheric CO2 concentrations on future carbon and nitrogen cycles. Although higher
CO2 concentrations may enhance crop yields (Ewert et al. 2005, Jaggard et al. 2010), a temperature increase will probably decrease yields, particularly at low latitudes (Rosenzweig et al. 2014). The amount of carbon stored in vegetation biomass is expected to increase with increasing temperatures (Friend et al. 2014), whereas decomposition
rates are expected to increase with increasing atmospheric CO2 concentrations, thereby limiting soil carbon storage (Van Groenigen et al. 2014). The net outcome of these contrasting changes remains largely unclear (Rosenzweig et al. 2014); therefore, these are not accounted for in our GPBT calculations.
Fifth, we did not address the relation between biofuel demand and agricultural production in our study. Other studies have modelled the link between biofuel demand and agricultural production thoroughly, which requires an understanding of the implications of policy making and economics, including the complex relationship between fuel and food prices (Havlík et al. 2011, Kim & Dale 2011, Zilberman et al. 2013).
Finally, previous studies have shown that biofuels are generally disadvantageous compared with fossil fuels with respect to environmental impact categories such as acidification, eutrophication, ozone depletion, and human toxicity (Kim & Dale 2005, Van Der Velde et al. 2009, Börjesson & Tufvesson 2011), and replacing natural vegetation with croplands may affect local biodiversity (Danielsen et al. 2008). Therefore, the biofuel feedstocks that performed well in the present study may not be the best options when considering the total environmental impact.
3.8 | Conclusions
We developed spatially explicit GPBTs for crop-based biofuels on a global scale, which allows for a more-detailed spatial assessment of the global warming concerns and benefits of biofuel production than was possible earlier. Under no-input cultivation, rapeseed- based biodiesel yielded the shortest GPBTs, whereas sugarcane yielded the longest GPBTs. High-input farming strongly reduced the climatic impact of biofuels. Specifically, fertilization and irrigation resulted in higher crop yields, which offset the negative effects of decreases in soil carbon and higher GHG emissions from farming activities, particularly emissions of dinitrogen oxide from fertilizer application. Geographic location was found to be the most important factor controlling the environmental performance of the biofuel production systems included in the present study: the location affects the replaced natural carbon stocks and the carbon stocks and crop yields in the bioenergy system. For example, crop cultivation in tropical forest regions typically resulted in long GPBTs (medians of 17 to 51 years), whereas cultivation in temperate regions yielded substantially shorter GPBTs (medians of 6 to 20 years). Careful selection of growing locations is thus a prerequisite for the contribution of biofuel crops to the mitigation of climate change. Greenhouse gas payback times for crop-based biofuels 49
3.9 | Methods
The GPBTs for the biofuel production systems of crop x cultivated under management strategy j in location i were calculated using the following equation
∆GHGsoil,x,i,j ∆GHGbiomass,x,i,j (3.1) 〖GPBTx,i,j = + 〖 , (Mfossil - Mbio) × Yx,i,j × BFx × Ex where ∆GHGsoil is the difference between stocks of SOC and nitrogen in natural and -1 agricultural soil (Mg CO2-eq∙ha ); Δ∆GHGbiomass is the difference between carbon stored in -1 natural and crop biomass (Mg CO2-eq∙ha ); Mfossil is the life-cycle fossil GHG emissions -1 during production and combustion of gasoline or diesel (Mg CO2-eq∙MJ produced ); Mbio is the life-cycle fossil GHG emissions during biofuel production, including crop fertilizer -1 -1 -1 application (Mg CO2-eq∙MJ produced ); Y is the crop yield (kg crop∙ha ∙yr ); BF is the biofuel conversion efficiency, that is, the amount of biofuel that can be generated per amount of crop x (kg fuel∙kg crop-1); and E is the energy content of the crop x biofuel (MJ produced∙kg fuel-1). The carbon stored in dead material was not included, as the contribution of dead material to the total carbon pool is typically low: <3% in forests (Sierra et al. 2007) and <1% in grasslands (Grace et al. 2006).
The two farm management strategies under investigation were no-input and high-input cultivation. A scenario where no-input cultivation is converted to high-input cultivation to improve crop production was studied as an alternative to the expansion of agricultural lands at the expense of natural vegetation. GPBTs for this land-use scenario were calculated using the equation
(3.2) 〖Δ∆GPBTx,i = GPBTx,i,high input -〖GPBTx,i,no input.
The SOC content of the agricultural systems and natural systems was simulated using the Environment Policy Integrated Climate (EPIC) model (Izaurralde et al. 2006). For each combination of crop type and management strategy, the EPIC model simulates the SOC content as the amount of organic carbon in the soil to a depth of 30 cm (Mg C∙ha-1), thereby accounting for the carbon content of crop residues, carbon respiration from the soil, leaching of carbon from the soil profile to lower layers, and carbon lost in runoff and eroded sediment. Regarding SOC in natural systems, the EPIC model distinguishes between forest and rangeland (that is, grass- and shrubland) vegetation. Emissions of dinitrogen oxide (N2O) resulting from soil mineralization were assumed to be directly proportional to the loss of soil carbon, following Flynn et al. (2012). The EPIC model was also used to simulate spatially explicit dry-matter yields of grain (corn and winter wheat), seed (rapeseed) and beans (soybeans), and the entire plant in the case of sugarcane. 50 Chapter 3
The amounts of carbon stocked in natural biomass were based on the IPCC Tier-1 Global Biomass Carbon Map for the Year 2000 by Ruesch and Gibbs (2008), which provides default carbon densities for both above- and below-ground biomass of various natural vegetation types at a 1x1-km spatial resolution. The amount of carbon stored in crop biomass was set to zero because annual harvesting of crops hinders the long-term storage of carbon.
Life-cycle GHG emissions related to the production and use of biofuels and fossil fuels were obtained from the ecoinvent v3. Database (Weidema et al. 2013). For fossil fuels, we used the global average of GHGs associated with the production of 1 MJ of fossil fuel energy. For the biofuels, we included only GHGs emitted during the production (for example, use of farming machinery, refining) of 1 MJ of bioenergy. Carbon emissions during combustion of biofuels were not included owing to the short rotation time of crops, implying a negligible Global Warming Potential (Cherubini et al. 2011).
Direct N2O emissions from fertilizer application during high-input crop cultivation were based on the nonlinear response between nitrogen fertilizer application and soil
N2O emissions reported by Shcherbak et al. (2014). Indirect emissions through nitrogen
volatilization (through NH3 and NOx) and from the leaching and runoff of nitrogen from fertilizer applications, were calculated using IPCC default emission factors (IPCC 2006).
For fossil fuel, as well as for biogenic GHG emissions, CO2, N2O and methane were summed on the basis of their global warming potentials over a 100-year period, that is, 1, 265, and 30
CO2 equivalents (CO2-eq), respectively (IPCC 2013).
To address by-products of biofuel production systems, GHG emissions were allocated based on energy content, mass, and market value. In the main text, we present the results of energy content-based allocation. Results of mass-based and market value-based allocation are presented in Appendix Figures B2-5.
We determined the variance between grid-specific GPBTs that was attributable to type of crop used for biofuel production, management regime during crop cultivation, and cultivation location. This variance was calculated using an ANOVA as the sums of squares for crop type and management regime factors, and the residual representing the spatial variability. The sum of squares of each factor and the residual were divided by the total sum of squares to provide a measure of the explained variance. The analyses were conducted using the R statistical software, v3.0.2, in RStudio.
A detailed account of the methods is available in Appendix B. Greenhouse gas payback times for crop-based biofuels 51
Acknowledgements
The research was partly funded by two projects of the European Commission under the 7th framework program for the environment (1) ENV.2008.3.3.2.1: PROSUITE - Sustainability Assessment of Technologies, grant agreement 227078, and (2) ENV. 2009.3.3.2.1: LC- IMPACT - Improved Life Cycle Impact Assessment methods (LCIA) for better sustainability assessment of technologies, grant agreement 243827. P. Elshout was further supported by the Dutch National Institute for Public Health and the Environment RIVM-project S/607020, Measurably Sustainable within the spearhead Healthy and Sustainable Living Environment, commissioned by the Director-General of RIVM, and run under the auspices of RIVM’s Science Advisory Board.
chapter 4
Greenhouse gas payback times for first generation bioethanol and biodiesel based on recent crop production data
P. M. F. Elshout, M. van der Velde, R. van Zelm, and M. A. J. Huijbregts (under review). 54 Chapter 4
Abstract
Potential greenhouse gas (GHG) emission benefits of replacing fossil fuels with biofuels can be assessed through the calculation of greenhouse gas payback times (GPBTs). The GPBT metric indicates how many years biofuels should be produced in an area before avoided GHG emissions from replacing fossil fuels compensate for the (direct) GHG emissions of land transformation in that area. The present study derived spatially explicit GPBTs of bioethanol and biodiesel production for locations where sugarcane, corn, soybean and rapeseed are currently cultivated, accounting for three farm management strategies. GPBTs ranged from negative, indicating a direct GHG benefit, to more than 500 years in areas where carbon-rich vegetation such as tropical rainforests were removed. Additionally, average GPBTs were calculated for major biofuel producing countries. It appeared that the production of feedstocks in Brazil, Italy, Austria and China requires agricultural lands to be used consecutively for many decades to several centuries before the GHG debt from land transformation is repaid. On the other hand, biofuel production from rapeseed in Germany, soybean in Argentina, and corn or soybean from the USA, resulted in average GPBTs of less than 40 years. Displacing fossil fuels with biofuels from these crops, produced in these countries, would thus lead to reduced GHG emissions within four decades. Greenhouse gas payback times for biofuels based on recent crop production data 55
4.1 | Introduction
Numerous national and international biofuel policies have led to biofuels becoming a considerable energy source for road transportation over the last decades (Jansen 2003, Milliken et al. 2007, Goldemberg et al. 2014). Approximately 4% of the total global transport fuel mix is currently made up of biofuels (Timilsina 2014, IEA 2015), while in pioneering country Brazil, the share of biofuels even exceeds 30% (Angelo 2012). The original reason for governments to promote use of biofuels is that they were commonly considered to be a cleaner alternative to fossil fuels when it comes to greenhouse gas (GHG) emissions (Goldemberg et al. 2004, Thompson et al. 2011). However, first- and second-generation biofuels (i.e. made from edible and inedible crop parts, respectively) require fertile agricultural land for feedstock cultivation and, thus, when maintaining food and feed production, this would require additional space that once consisted of natural ecosystems. Croplands generally store less carbon and nitrogen in their soil and biomass than this original natural vegetation (Righelato & Spracklen 2007, Danielsen et al. 2009). Land transformation (either directly from a natural situation to croplands, or indirectly via other anthropogenic land covers) therefore causes a net loss of GHGs to the atmosphere that can be attributed to the biofuel production in that location. Studies that account for the GHG emissions related to transformation of natural vegetation into croplands find that these emissions are significant contributors to the GHG balance of biofuels, and thereby determine to what extent biofuels are advantageous over fossil fuels from a GHG perspective (Fargione et al. 2008, Gibbs et al. 2008, Searchinger et al. 2008, Hallgren et al. 2013, Elshout et al. 2015).
To quantify the GHG impacts of biofuels compared to fossil fuels, so-called GHG payback times (GPBTs; also known as carbon debt repayment times, or break-even points) can be used (Fargione et al. 2008, Gibbs et al. 2008, Elshout et al. 2015). These GPBTs indicate how many years it takes before the total GHG savings due to displacement of fossil fuels by biofuels equals the initial GHG emissions during conversion of natural land to cropland. Previously, GPBTs (or similar indicators such as carbon payback times) for crop-based biofuels have been derived for corn ethanol in the USA (Searchinger et al. 2008, Kim et al. 2009, Yang & Suh 2014), bioethanol and biodiesel from the USA, Brazil and southeast Asia (Fargione et al. 2008), and for various biofuel crops grown in the tropics (Gibbs et al. 2008). Recently, Elshout et al. (2015) calculated spatially explicit GPBTs for the production of first generation biofuels at a global scale. When including all areas where crops yields could potentially reach commercial levels, they found that GPBTs may vary from less than a decade to several centuries, which mostly depends on the location of crop cultivation, but also changes with the type of feedstock and the intensity of land management during cultivation. However, as Elshout et al. focused on a theoretical situation of feedstock cultivation all over the world, the actual impact of biofuel production in locations where crop cultivation effectively takes place remains unassessed. 56 Chapter 4
The goal of the present study was to derive spatially explicit GPBTs for corn- and sugarcane- based bioethanol and rapeseed- and soybean-based biodiesel production on the global scale accounting for current growth location, farm management strategy, yields and production quantities. These four first generation biofuels represent almost 90% of the global biofuel production (Searchinger et al. 2013). We here present grid-specific GPBTs per feedstock, as well as national and global averages for bioethanol and biodiesel production.
4.2 | Materials and methods
4.2.1 | Framework
Locations of crop production under different farm management strategies were collected from the 2005 Spatial Production Allocation Model (SPAM), which estimates global crop distribution at a 5 minute resolution based on production statistics, land cover, and the suitability of landscape, climate and soil (You et al. 2014) (http://mapspam.info). Three farm management strategies with different nitrogen and irrigation inputs were accounted for: (1) no input, rain-fed, (2) high input, rain-fed, and (3) high input, irrigated farming. For each actual combination of crop x, location i, and management strategy j, GPBTs were calculated using the method from Elshout et al. (2015):
∆GHGsoil,x,i,j + ∆GHGbiomass,x,i,j (4.1) 〖GPBTx,i,j = (GHGfossil,x - GHGbiofuel,x,i,j) × Yx,i,j × BFx × Ex
where [GHGsoil is the difference between stocks of soil organic carbon (SOC) and nitrogen -1 in natural and agricultural soil (Mg CO2-eq∙ha ); [GHGbiomass is the difference between -1 carbon stored in natural biomass and crop biomass (Mg CO2-eq∙ha ); GHGfossil is the life- cycle fossil GHG emissions during fossil fuel production and combustion as benchmark -1 (Mg CO2-eq∙MJ produced ); GHGbiofuel is the life-cycle fossil GHG emissions during biofuel -1 production, including crop fertilizer application (Mg CO2-eq∙MJ produced ); Y is the crop yield (kg crop∙ha-1∙yr-1); BF is the biofuel conversion efficiency, i.e., the amount of pure (B100) biofuel that can be generated per amount of feedstock (kg fuel∙kg crop-1); and E is the energy density of the crop-based biofuel (MJ produced∙kg fuel-1). The conversion efficiencies of the different feedstocks and energy densities of the different biofuels are provided in Table C1. Grid-specific and management-specific crop yields were directly obtained from SPAM.
4.2.2 | Biogenic GHG emissions
In each location where one of the four biofuel feedstocks is cultivated (based on SPAM),
we calculated the biogenic GHG emissions, i.e. emissions of carbon dioxide (CO2) and
dinitrogen monoxide (N2O) from the soil and biomass due to land transformation and occupation. These biogenic GHG emissions in a certain location were assumed to equal Greenhouse gas payback times for biofuels based on recent crop production data 57
the difference between carbon and nitrogen stocks in the natural ecosystem and those in the cropland in that same location. Carbon densities of natural forest biomass and SOC contents of natural forests and croplands were collected from the Global Trade and Analysis Project (Gibbs et al. 2014). The carbon densities of natural grassland biomass were collected from Ruesch and Gibbs (2008). The SOC of natural grasslands was derived for 18 agro-ecological zones (AEZs) around the globe, using Harmonized World Soil Database (FAO et al. 2012) (see Appendix C for more details). For the changes in SOC stocks upon land use change, we focused on the top 30 cm of soil. Emissions of N2O resulting from soil mineralization were assumed to be directly proportional to the loss of soil carbon, following Flynn et al. (2012). Finally, the carbon stored in crop biomass was set to zero because annual harvesting of crops hinders the long-term storage of carbon (Elshout et al. 2015).
4.2.3 | GHG emissions related to biofuel and fossil fuel production
Emissions of fossil CO2, fossil methane (CH4) and N2O related to biofuel production were collected from the ecoinvent v3 Life Cycle Inventory database (Weidema et al. 2013). This includes emissions due to energy and material use during agricultural processes such as sowing, ploughing, harvesting, and the production and application of pesticides, phosphorus fertilizers, and irrigation water, as well as during the refining process (i.e. esterification of oils and fermentation of sugars). These emission data are feedstock- specific, but generic for different countries. Only N2O emissions related to nitrogen fertilizer application were derived in a spatially explicit manner. Application rates of nitrogen fertilizer under high input, rain-fed and high input, irrigated management were calculated with the Environmental Policy Integrated Climate (EPIC) crop growth simulation model. Corresponding N2O emissions were calculated as per Shcherbak et al. (2014). For both the no input, rain-fed, and low input, rain-fed farming strategies, we assumed no use of nitrogen fertilizers. More details on the calculation of GHG emissions related to nitrogen fertilizer application can be found in Appendix C.
Emissions of fossil CO2, fossil CH4 and N2O during the production and combustion of fossil fuels were also collected from ecoinvent (Weidema et al. 2013). Unleaded petrol and fossil diesel were selected as the fuels that would be replaced by bioethanol and biodiesel, respectively. The same fuel production process (and corresponding GHG emission) was assumed for all countries, as country-specific data were lacking.
4.2.4 | Country average GPBTs
We calculated average GPBTs for the main biofuel-producing countries, which together cover about 90% of the total global biofuel production: the USA (corn and soybean), Brazil (sugarcane and soybean), Argentina (soybean), China (corn), and a selection of European countries: Germany, France, Poland, Italy and Austria (mostly rapeseed; some 58 Chapter 4
soybean) (Guyomard et al. 2011, Ajanovic & Haas 2014). All these countries have substantial domestic feedstock production, in contrast to, for example, the Netherlands, which is a major producer of biodiesel but greatly depends on rapeseed imports from Germany and France (Goh & Junginger 2013).
The actual end use of produced crops (whether this is biofuel production or food, feed or fibre purposes) may vary between farms and years, based on the crop market at the time of harvest. Farmers are likely to change their supply to any of the industries as the demand for crops changes. In this sense, farmlands are interchangeable, and all may contribute to
a country’s biofuel production. Therefore, average GPBTs for each crop in country r (GPBTx,r) were calculated by weighting the impacts of the three management strategies based on recent production quantities, using the following equation:
∑j,i∈r▒(Px,i,j ×〖GPBTx,i,j) (4.2) GPBTx,r = ∑j,i∈r▒Px,i,j
where GPBTx,i,j is the GHG payback time (in years) specific to the biofuel made from crop x cultivated at location i under management strategy j, P is the actual production (in kg∙year-1) of the crop. The quantities of current crop production under each farm management strategy (in t∙year-1) were collected from SPAM (You et al. 2014).
In addition to the country-specific averages for each feedstock, we calculated country- specific averages of GPBTs for bioethanol and biodiesel. To this end, the feedstock production quantities were converted to MJ∙year-1 using crop-specific feedstock-to- biofuel conversion efficiencies and default energy densities for bioethanol and biodiesel (Table C1). However, it should be noted that one MJ of biofuel and one MJ of fossil fuel do not necessarily provide the same wattage or driving distance (a unit that can also be used when comparing fuels), e.g. due to differences in engine efficiency (Yan et al. 2013, Bergthorson & Thomson 2015). Additional information on how the global average was calculated can be found in Appendix C.
4.2.5 | Allocation
We allocated the GHG emissions related to biofuel production between the main product (i.e. bioethanol or biodiesel) and any major by-products of crop cultivation (e.g. corn stover, rapeseed straw) and crop refining (e.g. distillers grains from corn, meal and glycerine from soybean) based on their respective economic values. Economic allocation is recommended for production systems where main and by-products have different end uses (ISO 1997, Wang et al. 2011), and has been adopted by earlier studies on biofuel-related payback times as well (Fargione et al. 2008). The allocation factors can be found in Table C3. Greenhouse gas payback times for biofuels based on recent crop production data 59
4.3 | Results
4.3.1 | Grid-specific GPBTs
GPBTs were calculated for 87,819 grid cells around the globe, located throughout 149 countries and all continents except Antarctica. Longest GPBTs were found in the tropical regions, where croplands have replaced carbon-rich rainforests (Figure 4.1a-d). Negative GPBTs were found in 9% of the grid cells, resulting from cropland carbon stocks that exceed the natural reference carbon stocks. This was especially notable in the central USA, where we found that the SOC of croplands often exceeded the summed SOC and biomass carbon of the original mixed and short grassland vegetation, according to our input datasets (Gibbs et al. 2014, You et al. 2014). When comparing different management strategies in the same location, corn was found to have longest GPBTs when cultivated under high input, irrigated farm management, while the GPBTs for soybean were longest under no/low input, rain-fed.
4.3.2 | Country GPBTs
Average GPBTs for bioethanol and biodiesel were calculated for the nine major biofuel producing countries (Figure 4.2). Rapeseed-based biodiesel production in Germany and soybean-based biodiesel production in Argentina had shortest average GPBTs. Longest GPBTs were found for corn-based bioethanol production in China, followed by sugarcane- based bioethanol production in Brazil. Different management strategies did not result in significantly different GPBTs (Figures 4.3 and 4.4). However, we did find that longest GPBTs were found in grid cells where forests were sacrificed.
Within the biofuel producing countries, there was large variation in GPBTs between different grid cells. Germany had most grid-specific GPBTs below 30 years, at 82%, while Poland had fewest, at 18%. Notably, over half of the Italian grid cells had a GPBT below 30 years, which is more than Poland, even though the latter has a much shorter average GPBT. Overall, biodiesel production generally yielded shorter GPBTs than bioethanol production. An additional analysis was done using expected crop yields for 2050 rather than current yields. The results of this analysis can be found in Appendix C. 60 Chapter 4
A
B
C
D
figure 4.1 | Grid-specific GPBTs (in years) for the production of biofuels based on cultivation of (A) corn, (B) sugarcane, (C) rapeseed, and (D) soybean around the globe. White areas were excluded from the calculations, as these are considered not suitable for crop cultivation. In the grey areas no current cultivation of the respective feedstock crop was reported. Maps were created using ArcGIS 10.1. Greenhouse gas payback times for biofuels based on recent crop production data 61
figure 4.2 | Average GPBTs for the production of bioethanol and biodiesel in nine major biofuel- producing countries and the global average.
4.4 | Discussion and conclusion
4.4.1 | Uncertainties and assumptions
The results of our study should be interpreted in consideration of several important assumptions and uncertainties regarding the methodology and data selection. Foremost, it is important to note that we did not account for the international trade in feedstocks. In other words, we assumed that the major biofuel-producing countries solely produced biofuels from feedstocks grown within their own borders. In reality, most biofuel- producing countries (and the European ones in particular) use a mix of domestically produced and imported feedstocks. Taking into account the exact origin of feedstocks will be important in more detailed country-specific biofuel impact assessments, but it was outside of scope of the present study, which aimed particularly at the land use part of biofuel production on a global scale.
Furthermore, we did not account for indirect land use change (iLUC) that may occur when current cropland is now used to cultivate biofuel crops and food/feed is sourced from elsewhere. iLUC is considered to be an important driver of deforestation worldwide (Matthew et al. 2009, Lambin & Meyfroidt 2011). It could be argued that the reference state of the location where food or feed production is displaced to in favour of biofuel production should be used in our GPBT calculations. Following this line of reasoning, our calculations lead to an underestimation of GPBTs for biofuels that caused food/feed reallocation to regions with relative high natural carbon storage (and vice versa). Previous work concludes that the use of (soy) biodiesel results in more iLUC than the use of (corn) 62 Chapter 4
bioethanol, in part due to its low yield and demand as vegetable oil (Broch et al. 2013). However, the impact of iLUC varies with the location of crop production, which in turn depends on complex economic and political considerations. The empirical information to account for iLUC in our GPBT calculations is, however, currently lacking.
A
B
C
figure 4.3 | Histograms of the GPBTs for bioethanol production in (A) Brazil, (B) China, and (C) the USA. The colours denote the type of farm management. The percentages denote the percentage of grid cells where biofuel production is beneficial over the use of fossil fuels in terms of GHG emissions, provided that the agricultural land is used to produce feedstocks for 30 years (upper percentage) or 20 years (lower percentage). Greenhouse gas payback times for biofuels based on recent crop production data 63
A B
C D
E F
G H
figure 4.4 | Histograms of the GPBTs for biodiesel production in (A) Argentina, (B) Austria, (C) Brazil, (D) France, (E) Germany, (F) Italy, (G) Poland, and (H) the USA. The colours denote the type of farm management. The percentages denote the percentage of grid cells where biofuel production is beneficial over the use of fossil fuels in terms of GHG emissions, provided that the agricultural land is used to produce feedstocks for 30 (upper percentage) or 20 (lower percentage) years. 64 Chapter 4
While our study provides a comparison between the GHG performance of four important feedstocks, two alternative first generation feedstocks for biodiesel were excluded from this study, as we lacked the data to derive spatially explicit GPBTs for these crops: oil palm (mainly produced in Malaysia and Indonesia), and sunflower (mainly produced in Spain). Sunflower is often marked as being less sustainable than other biodiesel feedstocks (Pimentel & Patzek 2005, Sanz Requena et al. 2011). Palm oil is generally considered a more viable (and cheap) option, and large quantities are imported by European countries to produce biodiesel. However, if oil palm plantations sacrifice carbon-rich natural vegetation, GHG emissions will greatly exceed those for rapeseed production (Reijnders & Huijbregts 2008, Schmidt 2010). Also missing from the present assessment are second- generation biofuels (e.g. bioethanol from corn stover, switchgrass and Miscanthus), which have long been identified as a better option than first generation biofuels because they do not compete for land with food, but large scale production has until now been hampered by relatively high production costs (Eggert & Greaker 2014). Calculating GPBTs for oil palm, sunflower, and second-generation biofuels would help to further identify which (generation of) biofuel performs best in which location from a GHG point of view.
Finally, note that the outcomes of the present study are heavily influenced by the allocation method adopted. Here we allocated the GHG emissions between the biofuel and any by-products based on their respective economic values. Of the three main allocation methods proposed by ISO 14040, economic allocation allocates the largest percentage of emissions to the biofuel. In other words, the GPBTs would have turned out shorter if mass- or energy-based allocation was adopted. Alternatively, allocating 100% of the emissions to the biofuel would strongly elongate the GPBTs.
4.4.2 | Interpretation
Biodiesel from German rapeseed was found to be the biofuel that first effectuates GHG reductions when replacing fossil fuels, as it has the shortest average GPBT and the largest percentage of grid cells with GPBTs below 30 years. However, biodiesel from Argentina, the USA (both soybean), France (rapeseed), as well as bioethanol from the USA (corn), can also be beneficial in terms of GHG reductions, with average GPBTs below 40 years. GPBTs in particular locations may be shortened by increasing crop yield (e.g. through crop breeding or as a result of global warming) or decreasing the GHG emissions during biofuel production in that location (e.g. through technological progress and use of sustainable energy sources (Matt et al., 2011). However, we found no significant differences in GBPTs at different management strategies, so there is currently little to gain when it comes to reducing material inputs and energy use during cultivation. Instead, selecting locations with low natural carbon storage for crop cultivation should have first priority to minimize the impact of biofuel production. Greenhouse gas payback times for biofuels based on recent crop production data 65
Some differences exist between the outcomes of the present work and previous studies. Sanz Requena et al. (2011) found that both rapeseed and soybean perform relatively well as biodiesel feedstocks in terms of GHG emissions. However, they did not specify the location of crop cultivation and biofuel production, did not account for emissions during land transformation (which is likely to favour rapeseed, depending on the location of cultivation), and considered any carbon taken up by the crop during growth to equate to carbon sequestration, which is a different approach than used in the present study. In another study, Halleux et al. (2008) found that the ratio between the fossil GHG emissions (i.e. excluding the biogenic emissions from biomass and soil) associated with biodiesel production from Belgian rapeseed and those related to the production and combustion of fossil diesel is approximately 1:4. In the present study, we found the difference in GHG emissions between both fuel types to be smaller, with average biodiesel-fossil diesel emission ratios of 1:3.2 for German rapeseed and 1:2.7 for French rapeseed. Malça et al. -1 (2014) calculated GHG emissions of approximately 104 g CO2-eq MJ for high input rapeseed -1 cultivation in Germany and 113 g CO2-eq MJ in France, using economic allocation. These values exceed our outcomes, which translate to average GHG emissions of 57 g CO2-eq -1 -1 MJ in Germany and 94 g CO2-eq MJ in France when we amortize the biogenic emissions over 30 years of cultivation. It should be noted that our outcomes for European rapeseed production are based on an average fertilizer application rate of 81 kg nitrogen∙ha-1∙yr-1. This is a lower rate than reported by some previous studies, which report rates up to 160 kg nitrogen for the countries included here (Van Der Velde et al. 2009, Pehnelt & Vietze 2012). Changing the nitrogen fertilizer application rate in the present study to an average of 150 kg nitrogen∙ha-1∙yr-1 would increase the GPBTs of European rapeseed-based biodiesel by approximately 20 years. As for soybean, Panichelli et al. (2009) previously found that biodiesel from Argentinian soybean performs similar to soybean from Brazil in terms of various impact categories including global warming potential. However, they found soybean-based biodiesel from both countries to perform worse than fossil diesel in terms of global warming potential mostly due to emissions from land transformation. We generally used smaller reference biomass carbon stocks than Panichelli et al., with the result that the global warming impacts for biodiesel were well below those for fossil diesel. Moreover, smaller biomass carbon stocks in the subtropical forests of Argentina than in the tropical rainforests of Brazil resulted in better environmental performance of biodiesel in the former country.
Negative GPBTs were found in the present study, which means that in certain locations cropland soils contain more carbon and nitrogen than the original ecosystem’s soil and biomass. This is mostly the case in the steppes and prairies of the central USA and southern Canada, likely due to relatively low estimates of SOC in natural grasslands and relatively high estimates of SOC in croplands, but there is too little previous work available to validate our input data in this region. One exception is the work by Burke et al. (1989), who report SOC stocks in natural grasslands of central USA to be approximately 30-50 Mg C ha-1, while we found an average SOC of 37.7 Mg C ha-1. Regarding the croplands, Gibbs et 66 Chapter 4
al. (2014) themselves state that the SOC stocks they provide are likely overestimations, but they do not quantify the expected difference.
Note that the outcomes of the present study apply to GHG emissions only, and therefore relate to one of the main reasons for producing biofuels; reduction of GHG emissions by replacing fossil fuels. The crops and countries that show shortest GPBTs in our study are, however, not necessarily the crops and countries that should be invested in for future expansion of energy cropping, as they may rank differently when taking other impact categories into account. For example, European rapeseed was previously found to be preferable over other feedstocks when it comes to water consumption (Berger et al. 2015), human toxicity, and cumulative energy demand (Panichelli et al. 2009), while it performs worse than sugarcane, soybean and corn in terms of ecotoxicity related to pesticide use (Nordborg et al. 2014). Fossil fuels often perform best, e.g. with water use being up to 1000 times smaller than for bioethanol production (Kevin et al. 2010). Furthermore, cultivating biofuel feedstocks in species-rich areas has a larger direct impact on biodiversity per unit of natural land transformed into cropland, which disfavours use of sugarcane and soybean grown in the tropical regions of Brazil and northern Argentina (Mattson et al. 2000, Koh 2007). When it comes to susceptibility to acidification and eutrophication, Italy is a better option for crop cultivation than Germany or France (Brentrup et al. 2004), while the efficiency of water and nutrient use was previously shown to be higher in Poland and Germany than in other European countries (Van Der Velde et al. 2009).
4.5 | Conclusion
In terms of reducing GHG emissions by replacing fossil fuels with biofuels, we found that biodiesel from German rapeseed and Argentinean soybean, as well as bioethanol from USA corn are currently the most promising options, although some important uncertainties and assumptions are to be regarded. Reducing the uncertainties by including more detailed data on e.g. the import of feedstocks to biofuel-producing countries, the induction of iLUC, and country- or region-specific GHG emissions related to farming activities and fuel refining would further improve the assessment on the most suitable type of biofuel and the preferable location of feedstock cultivation. Still, our study provides a valuable overview of the most important spatial differences in GHG reductions from replacing fossil fuels by biofuels, accounting for land use-related GHG emissions.
Acknowledgements
The authors wish to thank Erwin Schmid (BOKU) for running the EPIC model simulations. P.E. was financed by FP7-grant DESIRE (ENV.2012.6.3-3) and M.H. was financed by the ERC- Consolidation Grant SIZE (Proposal N° 647224).
chapter 5
A spatially explicit data-driven approach to assess the effect of agricultural land occupation on species groups
P. M. F. Elshout, R. van Zelm, R. Karuppiah, I.J. Laurenzi, and M. A. J. Huijbregts (2014). International Journal of LCA 19(4): 758-769 70 Chapter 5
Abstract
Purpose Change of vegetation cover and increased land use intensity, particularly for agricultural use, can affect species richness. Within life cycle impact assessment, methods to assess impacts of land use on a global scale are still in need of development. In this work we present a spatially explicit data driven approach to characterize the effect of agricultural land occupation on different species groups.
Methods We derived characterization factors for the direct impact of agricultural land occupation on relative species richness. Our method identifies potential differences in impacts for cultivation of different crop types, on different species groups, and in different world regions. Using empirical species richness data gathered via an extensive literature search, characterization factors were calculated for four crop groups (oil palm, low crops, Pooideae, and Panicoideae), four species groups (arthropods, birds, mammals, vascular plants), and six biomes.
Results and discussion Analysis of the collected data showed that vascular plant richness is more sensitive than the species richness of arthropods to agricultural land occupation. Regarding the differences between world regions, the impact of agricultural land use was lower in boreal forests/taiga than in temperate and tropical regions. The impact of oil palm plantations was found to be larger than that of Pooideae croplands, although we cannot rule out that this difference is influenced by the spatial difference between the oil palm and Pooideae growing regions as well. Analysis of a subset of data showed that the impact of conventional farming was larger than the impact of low-input farming.
Conclusions The impact of land occupation on relative species richness depends on the taxonomic groups considered, the climatic region, and farm management. The influence of crop type, however, was found to be of less importance. Impact of agricultural land occupation on species groups 71
5.1 | Introduction
Agricultural land use can directly affect species diversity in a region (Matson et al. 1997, Vitousek et al. 1997, Foley et al. 2005) and has been deemed to be the most influential driver for biodiversity loss to date (Millennium Ecosystem Assessment 2005). The impact of agricultural land use on species diversity worldwide depends on several factors. Firstly, crops are grown under different climatic conditions with different ways of cultivation, which can lead to a large geographical variability in the environmental impact of crop cultivation (McLaughlin & Mineau 1995, Holland 2004). Moreover, growing a specific crop in a tropical region may require clearance of rainforest, while the same crop may replace natural grasslands in temperate regions. Species from these different regions may react differently to agricultural land use as the structure of the cropland vegetation will resemble the natural vegetation structure to a greater or lesser extent. This makes the impact of land occupation region-specific. Most research on the impact of agricultural land use has been done in tropical regions where crops like oil palm, rubber, and soybean replace the natural rainforest and potentially impoverish the faunal community (Nepstad et al. 1999, Sodhi et al. 2004, Wright & Muller-Landau 2006, Danielsen et al. 2009, Gardner et al. 2009).
Secondly, some types of cropland may be more suitable as habitat to plants and animals than others, not only because of differences in farming practices, but also due to differences in crop structure. Vegetation density influences the success of concealing from a predator or prey, provides shelter against extreme weather conditions, and influences the diversity and accessibility of food items (Cody 1981, Wilson et al. 2005). A few studies have assessed the impact of different crops on biodiversity. Booij and Noorlander (1992) found small differences in ground beetle species densities in six row crop systems, showing that species densities were largest in wheat, sugar beet, and pea fields. Perennial crops such as switchgrass and Miscanthus provide greater habitat stability than annual row crops and were found to support a larger abundance and diversity of insects than corn (Andow 1991, Ward & Ward 2001, Gardiner et al. 2010), as well as a larger diversity of birds than wheat (Bellamy et al. 2009).
Thirdly, different taxonomic groups may respond in a different way to agricultural land use change and intensification due to variations in, for example, size, mobility, and diet. Also, farmers may actively control species (groups) that are considered pests to a specific crop (Kessler et al. 2009, Flohre et al. 2011). Schulze et al. (2004) found that the decline of bird and plant species when changing a primary forest into agroforestry and cropland is larger than the decline of butterflies and dung beetles. Kessler et al. (2009) found that replacement of a mature forest with agroforestry caused species richness of trees, dung beetles, and birds to decline, of antsto stay equal, and of herbs and canopy beetles to increase. A meta-analysis by Danielsen et al. (2009) showed that species richness of 72 Chapter 5
vertebrates was always lower in oil-palm plantations than in tropical forests while no difference was found for invertebrates.
There has been an increased effort to include the impacts of land use in Life Cycle Assessment (LCA) during the last few decades (e.g. Brentrup et al. 2002, Kløverpris et al. 2007, Köllner & Scholz 2007, Milà i Canals et al. 2007, Müller-Wenk & Brandão 2010, Bare 2011, De Baan et al. 2013). Multiple land quality indicators have been proposed to quantify land use impacts, including ecological soil quality, biotic production potential, and biodiversity (Milà i Canals et al. 2007). In case of biodiversity, the impact has often been quantified by deriving characterization factors (CFs) based on the relative difference between the species composition (e.g. species richness, abundance, evenness and/or naturalness) during land use and that in a (semi-)natural reference situation (e.g. Weidema & Lindeijer 2001, Vogtländer et al. 2004, Köllner & Scholz 2008, Schmidt 2008, De Schryver et al. 2010, Curran et al. 2011, De Baan et al. 2013, De Souza et al. 2013). Impact assessment methods are highly dependent on data availability, and many LCA studies are biased towards well-studied taxonomic groups and geographic regions: Taxonomic coverage is often limited to vascular plants (e.g. Lindeijer 2000, Vogtländer et al. 2004, Schmidt 2008, De Schryver et al. 2010), and most studies focus on Northern Europe (Köllner 2000, Vogtländer et al. 2004, Köllner & Scholz 2008, Michelsen 2008, De Schryver et al. 2010), North America (Geyer et al. 2010), and South-East Asia (Schmidt 2008). Moreover, different crop types are often considered as one group when croplands are compared to other land cover types (e.g. ‘arable land’; Köllner & Scholz 2008, Schmidt 2008, De Schryver et al. 2010), or they are grouped into e.g. annual versus permanent crops (De Baan et al. 2013, De Souza et al. 2013) or grain crops versus row/field crops (Geyer et al. 2010).
Within LCA, three phases of land use are distinguished: (1) land transformation, i.e. the impact of making the land suitable for a new activity; (2) land occupation, i.e. the impact during the new activity; and (3) land relaxation, i.e. recovery of the land after the activity has ended (Lindeijer et al. 2002, Köllner & Scholz 2007, Milà i Canals et al. 2007). The goal of the present study was to increase understanding of the factors that influence the impact of land occupation on species richness, identify data gaps, and facilitate the development of more detailed CFs for land occupation in the future. We aimed to identify potential differences in (1) the impact of cultivation of different crop types, (2) the impact of crop cultivation in different world regions, and (3) the sensitivity of different species groups to crop cultivation. Crop, species, and biome-specific CFs were calculated based on data gathered via an extensive literature search, using a method recently described by De Baan et al. (2013). Our method builds upon the method of De Baan et al. (2013) by expanding the available dataset, differentiating between crop types, and taking into account farm management strategies in the analysis. Impact of agricultural land occupation on species groups 73
5.2 | Methods
5.2.1 | Framework
Characterization factors (CFs) for the impact of agricultural land occupation were determined following the linear relationship described by Köllner and Scholz (2008) and De Baan et al. (2013):
Scrop,x,i,y (5.1) CFx,i,y = 1 - Sref,i,y where Scrop and Sref are the observed species richness (number of species) of species group y when cultivating crop type x in region i, and the observed species richness of the reference land cover in region i, respectively. In this study we use natural ecosystems as a reference, which allows the species richness of the cropland to be compared with the number of species that may have existed in that location if the land occupation would not have taken place. The equation yields characterization factors between -∞ and +1, where a negative value means a positive effect of land occupation (i.e. a larger species richness), and the maximum of one represents a hundred percent decline in species richness. A low CF indicates lower impact of land occupation on biodiversity.
5.2.2 | Data collection and processing
Data were collected for corn, sugarcane, sugar beet, soybean, oil palm, rapeseed, switchgrass, sorghum, sunflower, jatropha, cassava, potato, wheat, barley, and rye, which include major second generation biofuel feedstocks. For the CF calculations, publications on species richness on agricultural land (up to April 2012) were searched within the ISI Web of Knowledge database using the following search key:
TS = ((biodiversit* OR “species richness*” OR “species abundanc*”) AND (corn* OR maize* OR “sugarcane*” OR “sugar beet*” OR soy* OR “oil palm*” OR rape* OR switchgrass* OR sorghum* OR sunflower* OR jatropha* OR cassava* OR potato* OR wheat* OR cereal* OR rye* OR barley*))
This resulted in a total of 2,591 hits. The summaries of these publications were checked in order to select those studies that provide data on the occurrence of species on croplands. The dataset from De Baan et al. (2013) was used to complement our selection. Ultimately, data on species richness on croplands were collected from 155 publications. In many of these publications, data for multiple cropland types, locations, and/or species groups were reported, which led to a total of 1,053 data points. Information on crop type, data type (i.e. total species richness, richness per field/sample and diversity indices), taxonomic group studied, sampling method, sampling effort (number of fields, number of samples per field, number of sampling days), sampling period (year, month), location (country, 74 Chapter 5
region, coordinates), and farm management (regime years, tillage, crop rotation, pesticide use) were listed, if reported. When coordinates were not reported, Google Earth was used to identify approximate coordinates. The location of each study site was spatially mapped on a global ecoregion and biome map (Olsen et al. 2001, WWF 2010) using ArcGIS 9.2.
In order to assess the impact of agricultural land occupation, our method required species richness data from the natural reference situation of each cropland location for the same taxonomic group. Because of limited reference data availablity, we combined land use data and reference data from different publications, which was a new approach in this field of work. In our study, we assumed that a reference resembled the typical natural vegetation that would have been if transformation to cropland had not occurred. Since each ecoregion reflects a specific distribution of natural flora and fauna (Olsen et al. 2001), such typical natural vegetation is assumed to be similar throughout an ecoregion. Hence, we paired cropland and reference data from different publications if the studies were located in the same ecoregion. We distinguished three quality levels for the reference data collection. The preferred way to collect reference data was from the same publication (and therefore region) as the cropland data. The next preferred way was to collect reference data from the same ecoregion through a collection of additional publications. If no reference data were found using the above listed options, we selected reference data from the same biome using the set of already selected publications.
Using equation 1, CFs were calculated for each pair of cropland and reference situation. Schmidt (2008) states that there are two options when comparing species richness: (1) use a sample size where the species-area curve has reached a clear asymptote or (2) use a standardized area for all land use types (see also Gotelli & Colwell 2001). Here, CFs were derived using reference data obtained with the same sampling technique (e.g. visual survey, quadrat sampling, pitfall traps), similar sampling area, and similar sampling effort (e.g. number of sampling points, sampling duration) as the cropland data. Whether sampling area and effort were similar between studies, and thus comparable was decided on a case-by-case basis. Reference data were considered sufficiently similar in case the sampling area and sampling effort of the reference site was within one order of magnitude compared to the agricultural site. An important assumption that was made was that each of the studies correctly and sufficiently assessed the number of species in the agricultural field or natural area.
When data were available to calculate multiple CFs within one study (e.g. when multiple agricultural fields of the same crop were surveyed for the same species), we averaged these factors to come up with one CF per combination of crop type, species group, and ecoregion, per study. Hereafter, the crops were aggregated into four crop groups, while the taxonomic groups were divided into four broader species groups. The first crop group was oil palm, which was chosen to be a group on its own because of its distinct growth characteristics and because it is a permanent crop rather than an annual crop. The second group includes soybean, sugar beet, potato and cassava which, based on their low density and height, we Impact of agricultural land occupation on species groups 75
expect to provide little shelter to flora and fauna. The remainder of crops were grouped based on taxonomy, distinguishing Pooideae (small cereals; wheat, barley and rye), and Panicoideae (tall grasses; corn, switchgrass, Miscanthus and sugarcane). Insufficient data were available on sunflower and rapeseed croplands to include these in our assessment. The different species were aggregated based on taxonomy into mammals, birds, arthropods, and vascular plants. Additionally, the data points were spatially reclassified to the biome level, which serves as a proxy of the original vegetation. Of the total 14 terrestrial biomes in the world (Olsen et al. 2001), we had data for six. A more detailed description of the process of data collection and CF calculation can be found in Appendix D.
5.2.3 | Statistical analysis
Differences between CFs were assessed through two ways of data analysis. In the first approach, median CFs were derived per combination of crop group, species group, and biome (hereafter: combination-based grouping). This yielded CFs for specific cases of land occupation, e.g. the impact of corn cultivation on birds in the temperate broadleaf and mixed forest biome. On the downside, structuring our CFs this way limited the statistical analysis as some combinations of categories had to be excluded due to a lack of data. Also, the number of studies available per combination was in some cases rather low, which could influence the outcome of the statistical analysis. In the second approach, median CFs were derived for each crop type, each of the species groups, and each of the biomes, using all available data in that particular category (hereafter: single-category grouping). These CFs show the overall median impact (1) of a specific crop on all species groups in all biomes, (2) of all crops on a specific species group in all biomes, and (3) of all crops on all species groups in a specific biome. While this allows for a more straightforward comparison of different crop groups, different species groups, and different biomes, the outcomes of the statistical analysis may be influenced by a skewed distribution of data throughout categories. For example, if the impact of corn cultivation is found to be larger than the impact of soybean cultivation, then this may be due to the fact that more data on vulnerable species is available in corn than in soybean fields. Because of this, conclusions were drawn based on corresponding findings between both grouping approaches.
We tested for statistical differences between combinations of crop groups, species groups, and biomes if the median CFs were derived with data from at least three studies. First, the one sample Wilcoxon signed-rank test (Wilcoxon 1945) was used to test if the median CFs were significantly different from zero (p < 0.05). Statistically, a CF that is different from zero implies that the land occupation has a (negative or positive) effect on species richness, based on the available data. Second, the median CFs of the different crop groups, species groups, and biomes were compared using the Kruskal-Wallis one-way ANOVA (Kruskal & Wallis 1952). When the Kruskal-Wallis test found significant differences between groups (p < 0.05), Mann- Whitney U-tests (Mann & Whitney 1947) were performed pair-wise to identify which groups were statistically different. The same method was used previously by De Baan et al. (2013), 76 Chapter 5
except that we adjusted the significance level using the Bonferroni correction for multiple comparisons in order to reduce the probability that nonexistent differences were found. All statistical tests were done in SPSS 19.0 (SPSS Inc., Chicago, IL, USA).
5.3 | Results
Most data on species richness were available for wheat and corn produced in European and North American ecoregions, and for oil palm in Southeast Asia (Figure 5.1). A total of 309 CFs were calculated, which resulted in 152 CFs on a per-study basis (Table D1): 51 using only cropland and reference data from the same study, an additional 70 when including ecoregion-based reference data, and 30 more when also including biome-based reference data. These were all calculated based on numbers of species reported, as data availability of other diversity indices was limited. No statistical differences were found between the CFs derived with the three different sources of reference data (Kruskall-Wallis: p = 0.843). Medians and standard errors (S.E.s) for the separate groups were similar (ranging 0.43-0.49 ± 0.07-0.10), and means ranged from 0.24-0.38. We therefore consider generating more CFs by adding ecoregion and biome-based references a valid approach to improve data availability in the present study. Combination-based grouping of the 152 CFs (see section 2.3) based on the four crop categories and four species categories, and excluding any CFs derived with data from less than three different studies, resulted in a total of 17 median CFs for specific combinations of crops and species within five out of fourteen biomes (Table 5.1). Single-category grouping allowed for calculation of median CFs for each of the four species groups, the four crop groups, and a total of four biomes (Table 5.2).
figure 5.1 | Locations of cropland data points. Biome map adapted from Olsen et al. (2001). Low crops: cassava, potato, soybean, sugar beet; Panicoideae: corn, Miscanthus, sugarcane, switchgrass; Pooideae: barley, rye, wheat. Impact of agricultural land occupation on species groups 77
table 5.1 | Median characterization factors (CFs) derived with data from three or more studies, grouped per combination of species group, crop group, and biome. The full list of original CFs can be found in Appendix D. biomea species groupb crop groupc median ± S.E. n studies p-values Wilcoxond boreal forests/taiga arthropods Pooideae -0.44 ± 0.34 5 0.35 low crops -0.55 ± 0.07 4 0.07 vascular plants Pooideae 0.15 ± 0.17 4 0.47 temperate broadleaf and mixed forests birds Panicoideae 0.62 ± 0.10 3 0.04 arthropods Panicoideae 0.35 ± 0.11 14 0.02 Pooideae -0.03 ± 0.13 16 0.26 low crops 0.43 ± 0.17 5 0.08 vascular plants Panicoideae 0.77 ± 0.08 8 0.01 Pooideae 0.72 ± 0.11 11 <0.01 low crops 0.86 ± 0.05 3 0.11 temperate grasslands and shrublands birds Panicoideae 0.42 ± 0.10 5 0.11 arthropods Pooideae 0.23 ± 0.18 4 0.29 Mediterranean forests and scrub arthropods Pooideae 0.68 ± 0.10 5 0.04 (sub)tropical moist broadleaf forests mammals oil palm 0.88 ± 0.16 3 0.11 birds oil palm 0.72 ± 0.04 7 0.02 arthropods Panicoideae 0.51 ± 0.19 3 0.11 oil palm 0.53 ± 0.13 16 0.01 a All biomes: boreal forests/taiga; deserts and xeric shrublands; Mediterranean forests and scrub; temperate broadleaf and mixed forests; temperate grasslands, savannas, and shrublands; (sub)tropical moist broadleaf forests; (sub)tropical dry coniferous forests; (sub)tropical grasslands, savannas, and shrublands. No data was available for the following biomes: (sub)tropical coniferous forests; temperate coniferous forests; flooded grasslands and savannas; montane grasslands and shrublands; tundra; mangroves. b Arthropods: ants, bees, beetles, butterflies, moths, spiders, termites, other arthropods. Mammals: terrestrial mammals and bats. Birds: all. Vascular plants: all. c Pooideae: barley, rye, wheat. Panicoideae: corn, Miscanthus, sugarcane, switchgrass. Low crops: cassava, potato, soybean, sugar beet. Annual crops: all except oil palm. Permanent crops: oil palm. d The one sample Wilcoxon signed-rank test was used to statistically test whether median CFs were different from zero. This gives an indication whether the agricultural land occupation has any (negative or positive) impact on species richness. Differences were considered significant at p < 0.05.
S.E. = standard error of the mean. 78 Chapter 5
5.3.1 | Species group
Pairwise comparison of the combination-based grouped data showed that the median CF for vascular plants was larger than the one for arthropods in Pooideae croplands within the temperate broadleaf and mixed forest biome. No other significant differences between species groups were found. After single-category grouping, arthropods were found to have lower median CFs than birds and vascular plants. Notably, nearly one third of the CFs for arthropods were negative, while for mammals, birds, and plants this was less than 9% (Appendix D). Only 7 out of 17 median CFs significantly differed from zero after combination-based grouping (Table 5.1), but this can be caused by the small number of studies available for some of these combinations. In the case of the single-category grouped data, the median CFs for all species groups were significantly larger than zero (Table 5.2).
table 5.2 | Median characterization factors (CFs) derived with data from three or more studies, grouped per single category of crop group, species group, and biome. For each category tested, all available data was combined. The full list of original CFs can be found in Appendix D.
species group/crop group/biomea median ± S.E. n studies p-values Wilcoxona mammals 0.29 ± 0.12 8 0.02 birds 0.62 ± 0.05 25 <0.01 arthropods 0.20 ± 0.09 79 0.02 vascular plants 0.76 ± 0.05 35 <0.01 oil palm 0.62 ± 0.08 27 <0.01 low crops 0.58 ± 0.12 22 <0.01 Pooideae 0.20 ± 0.07 57 0.01 Panicoideae 0.51 ± 0.13 45 <0.01 boreal forests/taiga -0.44 ± 0.15 14 0.06 deserts and xeric shrublands 0.11 ± 0.24 3 0.29 Mediterranean forests and scrub 0.68 ± 0.08 7 0.02 temperate broadleaf and mixed forest 0.40 ± 0.06 72 <0.01 temperate grasslands and shrublands 0.45 ± 0.36 15 0.02 (sub)tropical moist broadleaf forests 0.70 ± 0.08 38 <0.01 annual crops 0.42 ± 0.06 125 <0.01 permanent crops 0.62 ± 0.08 27 <0.01 conventionally managed crops 0.42 ± 0.10 32 0.01 low-input managed crops 0.05 ± 0.11 19 0.67 a see Table 1 for explanation of species group, crop group, biome and statistical test. S.E. = standard error of the mean. Impact of agricultural land occupation on species groups 79
5.3.2 | Crop type
The median CF of Panicoideae on arthropods in the temperate broadleaf and mixed forest biome was found to be larger than that of Pooideae croplands in the combination-based grouped data, while no significant difference was found between these crop groups after single-category grouping. For the single-category grouped data, the median CF for oil palm was larger than that of Pooideae croplands. One third of the CFs for Pooideae croplands was negative, while oil palm had most CFs in the range 0.5-1.0 (Appendix D). All single-category grouped data median CFs were significantly larger than zero (Table 5.2). These results were derived without accounting for differences in farm management strategy between different croplands because such information was reported sporadically. However, we were able to analyse the impact of farm management in a subset of our data. When grouping all available CFs based on management strategy, a total of 19 CFs was based on low-input farming (including e.g. organic farming, ecological farming, minimal interference farming, and eco-friendly farm management), and 32 CFs were based on conventional farming (including high interference farming and high-yield farm management). The data on conventional farms mostly included studies that tested for the impact of herbicides on weeds and invertebrates (indirectly), and a limited number of studies that tested for the impact of insecticide use, either in itself or in combination with herbicides and/or fungicides. The median CF for conventional farming was found to be larger than that of low-input farming (Table 5.2). Additionally, a paired Wilcoxon signed- rank test was performed to compare a subset of the conventional and low-input farming data, where each pair of data points originated from the same study, thus ruling out the influence of different sampling techniques in different studies. This test also showed that the number of species in low-input farms was larger than in conventional farms.
5.3.3 | Region
Data comparison after combination-based grouping showed that the median CF for the Mediterranean forest and scrub biome was larger than that of the temperate broadleaf and mixed forest biome when comparing arthropod richness in Pooideae croplands. Otherwise, no significant differences were found between the medians CFs of the biomes. Analysis of the single-category grouped data showed that the median CF for the boreal forests/taiga biome was smaller than those for the biomes Mediterranean forests and scrub, temperate broadleaf and mixed forests, temperate grasslands and shrublands, and (sub)tropical moist broadleaf forests. Also, the CFs for the boreal forests/taiga and deserts/xeric shrubland biomes were not significantly larger than zero, in contrast to the CFs for the temperate and tropical biomes (Table 5.2). Otherwise, the CFs among different biomes showed no clear differences (Appendix D). 80 Chapter 5
5.4 | Discussion
In the present study, we used a data driven approach to quantify the impact on species richness of multiple species groups when replacing natural vegetation with a variety of agricultural crops types in different regions of the world. We analyzed the results and provide information on the limitations and uncertainties of the empirical approach below.
5.4.1 | Species group
We found that the impact of agricultural land occupation on vascular plants and, possibly, birds is larger than the impact on arthropods. As part of common agricultural practice, most plants are actively removed from croplands, either mechanically or by application of herbicides, to minimize competition for resources and achieve the best-possible crop yield. The loss of various plant species is thus a direct effect of agricultural practice. Most native fauna depend on this original vegetation for food, shelter, and breeding or nesting (Wilson et al. 2005), and will be impacted accordingly. However, some of the original animal species may maintain themselves in the new croplands (“shared species”; e.g. Estrada & Coates-Estrada 2005, Danielsen et al. 2009, Ottonetti et al. 2010, Ouchtati et al. 2012). Other species prefer farmland habitats and are absent or less abundant in the natural situation (e.g. Gaines & Gratton 2010, Ottonetti et al. 2010). These farmland species mostly include invertebrates like beetles, spiders, and butterflies, as well as some bird species (Wilson et al. 2005). Arrival of such species increases the field species richness, thereby lowering the CF, and masking the loss of native invertebrates. Most studies that reported increasing species of arthropods were on ground beetles (Carabidae), which was also the species group for which most data was available. A handful of studies reported larger numbers of rove beetles, ground-dwelling spiders, springtails, and moths. Most ground beetles feed on invertebrate prey, and could be beneficial to farmers. The same is applicable for rove beetles and spiders. Moths (or their caterpillars) on the other hand can be a major agricultural pest. However, it was outside the scope of this study to check for the ecological value of either the native species lost or the species newly arrived upon agricultural land occupation.
De Baan et al. (2013) reported median CFs of 0.56 and 0.65 for arthropods in permanent and annual crops, respectively. For permanent crops, i.e. oil palm, we found a similar value (0.53 ± 0.13), but our median CF for arthropods in annual crops differs considerably (0.11 ± 0.10). For vascular plants in annual croplands, De Baan et al. (2013) found a median CF of 0.42 versus 0.76 in the present study. For birds similar results were found: 0.62 and 0.53 by De Baan et al. (2013) for permanent and annual crops, respectively, versus 0.72 ± 0.04 and 0.58 ± 0.06 in the present study. Given that the methods of the present study and that of De Baan et al. (2013) are similar, the differences between some of our outcomes may be attributed to an extended dataset from our side. Impact of agricultural land occupation on species groups 81
The present study covered a wide spectrum of taxonomic (sub)groups, but the majority of published data were on plants (27.9%), ground beetles (12.5%), ground-dwelling spiders (10.3%), and birds (5.9%). Birds are typically well-represented in literature studies because they are easily surveyed, taxonomically well known, and general support for conservation of birds is high (Rodrigues & Brooks 2007, Vandewalle et al. 2010, Larsen et al. 2012). The high availability of plant, beetle, and spider data can be explained by the interests of the agricultural sector. Plants (or “weeds”) are actively removed from agricultural fields because they compete for resources with the crops of interest. Many of the collected publications were dedicated to the testing of different herbicides or mechanical efforts on the removal of plants (e.g. De Snoo 1997, Mulugeta et al. 2001, Ulber et al. 2009). The opposite holds for ground beetles and spiders, some of which predate on pest species (Riechert & Bishop 1990, Booij & Noorlander 1992, Kromp 1999, Lang et al. 1999) and thus are beneficial to agriculture. Multiple collected publications were dedicated to studying the impact of farm management on ground beetle or spider communities (e.g. Booij & Noorlander 1992, Basedow 1998, Schmidt et al. 2005, Boutin et al. 2009). Also, insects are numerous and diverse, can be sampled relatively easily, and are responsive to environmental change (Vandewalle et al. 2010).
5.4.2 | Crop type
The results showed that the impact of oil palm cultivation is larger than the impact of Pooideae cultivation. However, since oil palm is only grown in (sub)tropical regions and our data on Pooideae croplands are almost exclusively from temperate, boreal, and Mediterranean regions, we cannot rule out that the difference between these crops is actually caused by a difference between these biomes. The same applies to the difference that was found between the median CFs of annual and permanent crops since oil palm is the only permanent crop included. We would have expected a larger impact of annual crops since the annual harvest and subsequent tillage of the soil frequently disturbs the farmland habitat, while plantations of permanent crops like oil palm can be a constant, relatively undisturbed habitat for longer periods. The larger impact of the permanent crop could be caused by the data origin. All oil palm data originated from (sub)tropical regions that have a relatively large species richness in the natural habitat, while the data on annual crops mostly originated from the temperate and boreal regions. Therefore, we do not recommend the use of our oil palm-based CF for permanent crops in the impact assessment of permanent crops typically grown in more temperate regions. Likewise, the CF for annual crops should only be used for the temperate and boreal regions. Our study did not confirm the findings of earlier smaller scale studies that found a larger species richness in cellulosic crops (Miscanthus and switchgrass) than in corn and wheat (Ward & Ward 2001, Bellamy et al. 2009, Gardiner et al. 2010). More data is needed to derive more concrete conclusions. 82 Chapter 5
Regarding farm management, we found that conventional farming, which often includes presticide use and tillage, has a larger impact on biodiversity than low-input farming, producing median CFs of 0.42 ± 0.10 and 0.05 ± 0.11, respectively. This is in line with earlier findings by Köllner and Scholz (2008), who used Central European data on plant species richness to calculate CFs for ecosystem damage, and reported 0.63 for high intensity agriculture and -0.06 for low intensity agriculture. De Schryver et al. (2010) came to the same conclusion, although their median CFs were larger (0.36 for organic and 0.79 for intensive arable land). Likewise, Schmidt (2008) found that the impact of intensive cultivation of cereals and annual crops was larger than the impact of extensive cultivation, although our numbers are not readily comparable. Finally, Müller et al. (2013) reported CFs of 0.60 and 0.15 for conventional and organic cultivation of fodder crops in temperate regions, respectively, and CFs of 0.81 and 0.42 in tropical regions, when assessing its impact on plant species richness.
The focus of the present study was on a limited number of crops that are relevant as food crops and/or biofuel feedstocks. It may be possible to estimate the impact of some other crops based on its shared growth characteristics with the different crop groups in this study. For example, the impact of cultivation of grasses such as sorghum and reed canarygrass is expected to be similar to the impact of the included Panicoideae, and cultivation of numerous vegetables (e.g. alliums, legumes, salad crops) is expected to have a impact similar to our low crops group. In such cases the CFs derived in the present study could be used. In other cases, correlation based on growth characteristics or taxonomy may not apply because of differences in farming technique. For example, the impact of wet rice cultivation is expected to be very different from that of larger Pooideae grown on dry grounds. Low data availability hinders the impact assessment of many additional crops.
5.4.3 | Region
The impact of agricultural land occupation was found to be lower in boreal forests/ taiga than in most other biomes. Only deserts/xeric shrublands showed no statistically significant difference from boreal forests/taiga. Boreal forests/taiga and deserts/xeric shrublands are also the two biomes for which no statistically significant effect of land occupation was found (i.e. their mean CFs were not statistically different from zero). Like in any region, land occupation in these harsh environments will cause a decrease of sensitive species but agricultural practice like irrigation and a potential increase of food resources will also attract various opportunistic species (Cook & Faeth 2006, Khoury & Al-Shamlih 2006). When using species richness as indicator, this can result in lower CFs. In the four other biomes tested (i.e. temperate broadleaf and mixed forests; temperate grasslands and shrublands; Mediterrannean forests and scrub; (sub)tropical moist broadleaf forests), agricultural land occupation was found to have a negative effect overall, with the exception of arthropods in temperate grasslands and shrublands. Likewise, De Baan et al. Impact of agricultural land occupation on species groups 83
(2013) found a small positive effect of land occupation in the deserts and xeric shrublands biome, and a negative effect in the temperate, (sub)tropical, and Mediterranean biomes. Comparing our results for annual crops with those from De Baan et al. (2013), we found a lower impact in the temperate broadleaf and mixed forest biome (0.40 ± 0.06 versus 0.76) and a larger impact in the (sub)tropical moist broadleaf forest biome (0.83 ± 0.11 versus 0.54), likely because different selections of crops were included in our datasets. No other studies provided impacts of annual crops on a biome basis.
5.4.4 | Uncertainties and limitations
We chose relative species richness as an indicator, because it captures biodiversity at the community-level (see e.g. Curran et al. 2011) and because it is reported frequently. However, relative species richness only provides information on a small aspect of biodiversity and several limitations can be identified. A major drawback of using species richness as an indicator of biodiversity is that it does not take species abundance into account (Larsen et al. 2012). Using this indicator, there is no distinction between a single individual of one species (perhaps an accidental encounter) and a large healthy population of another species; both count as one species. This limitation may be covered by using other biodiversity indicators such as Shannon index or mean species abundance of original species (Alkemade et al. 2009, Hanafiah et al. 2012), but these indicators require data that are rarely reported on a global scale. The choice of indicators that are most suitable and meaningful is subject to debate (Geyer et al. 2010). De Baan et al. (2013) were able to compare impacts across five different biodiversity indicators (relative species richness,
Fisher’s α, Shannon’s H, Sørensen’s SS, and MSA) for one biome and found considerable variation therein, but concluded that relative species richness is the most suitable indicator in view of current data availability. Likewise, Vogtländer et al. (2004) compared the species richness indicator with an ecosystem indicator that is based on ecosystem richness, diversity, and rarity. They also found that species richness is an accurate proxy of biodiversity in most cases. The species richness data we collected can be considered indicative of differences between crops, species, and regions. It should be noted, however, that native and farmland species were treated equally in the calculations of our CFs, as information on the species’ origin was not provided in the majority of cases. However, arriving farmland species may be considered of less (ecological or societal) value than native species, or they may even be pests, so such new arrivals should not be considered a compensation for a loss of native species. Indicators that compare exclusively the richness or abundance of native species between a reference and land use situation, e.g.
Sørensen’s SS (Sørensen 1948) and MSA (Alkemade et al. 2009), are more sensitive to land use impacts than relative species richness (De Baan et al. 2013). Consequently, our results could underestimate the impact of agricultural land use on the native biodiversity.
Even though species richness is studied and reported relatively often compared to other biodiversity indicators, for many combinations of crop types, species groups, and biomes 84 Chapter 5
too little data were available to derive CFs or perform sound statistical analysis. Low data availability has been a limiting factor throughout the present work. Still, we were able to increase our dataset size by nearly two-thirds by combining land use and reference data from different sources. We here considered this approach valid as we found no significant differences between the outcomes of all three data quality levels. However, it is important to emphasize that pairing data from two different publications should not be preferred over using data from the same study because the uncertainty of the outcomes increases when the two publications use (slightly) different sampling methods or effort.
The incapability to fully cover biodiversity is an important limitation in land use impact assessments. Often, vascular plant species are used as proxy for the impact on total biodiversity (Köllner 2000, Lindeijer 2000, Schmidt 2008). In the present study, vascular plants were shown to be the most sensitive group. Therefore, concentrating on vascular plants as an indicator of biodiversity may entail overestimation of the impact of agricultural land occupation on overall species richness. This is in line with the lack of success to use single taxonomic groups as indicator for overall biodiversity (e.g. McGeoch 1998, Schulze et al. 2004, Prendergast 2006, Michelsen 2008, Larsen et al. 2012). As long as there is no consensus on which (combinations of) species groups to use as indicators, biodiversity impact assessments should be performed including a diversity of taxonomic groups in order to cover total biodiversity as comprehensive as possible. More effort should be taken to study the impact of land occupation on underexposed groups like amphibians and reptiles.
Some aspects of agricultural land occupation that potentially influence its impact on biodiversity were excluded in the present study. This includes specific choices in farm management (pesticide use, fertilization, tillage, crop rotation, intercropping), farm age, and the layout of the surrounding landscape (proximity to natural vegetation, habitat heterogeneity). The impact of most of these factors on farm biodiversity has been shown in various field experiments (e.g. McLaughlin & Mineau 1995, Meek et al. 2002, Benton et al. 2003, Hole et al. 2005) but remains neglected in current land use impact assessment methodology. While farm management information was found to rarely be reported in detail, sufficient data should be available locally (e.g. for management of cereal fields in the UK). Such local data can help to quantify the influence of farm management types in regional land use assessments. Finally, it is important to note that all the results and analyses are valid only within the framework of the assumptions made and the use of aggregated data. Increasing the granularity in the data may change some of the findings. It is recommended that more data are generated and collected in order to improve the statistical analysis and increase the accuracy of results. Impact of agricultural land occupation on species groups 85
5.5 | Conclusions
The main utility of this work was to provide insight on the factors that influence the impact of land occupation on biodiversity. We calculated CFs and performed statistical analysis to assess the effect of land occupation on multiple species groups in different regions of the world.
Different species groups were found to respond in a different way to agricultural land occupation, so focusing only on “indicator species” in life cycle impact assessments is undesirable. Whenever possible, species of multiple groups should be included. Vascular plants (most sensitive) and arthropods (least sensitive) should at least be well represented. As significant differences were found between some biomes, the impact of land occupation should not be quantified at a global level but rather at a biome or ecoregion level. Farm management strategy should be included as an additional factor whenever data is available, as it was found to be of significant influence. However, differentiation between specific crops seems redundant considering that we found no significantly different impacts between crop groups based on density and height using the collected data. Our data also suggests that a distinction between annual and permanent crops may be more useful. The CFs derived in the present study are an important addition to the already available set of CFs, to further improve impact assessments of agricultural land use. Additional and more granular data are needed to improve accuracy of the assessments.
Acknowledgments
This study was part of a collaboration between ExxonMobil Research and Engineering (NJ, USA) and the Department of Environmental Science of the Radboud University Nijmegen (The Netherlands). The authors wish to thank Laura de Baan for providing data and sharing her expertise, and two anonymous reviewers for their helpful comments.
chapter 6
Global relative species loss due to first generation biofuel production for the transport sector.
P. M. F. Elshout, R. van Zelm, M. van der Velde, Z.J.N. Steinmann and M. A. J. Huijbregts (2018). GCB Bionenergy 88 Chapter 6
Abstract
The global demand for biofuels in the transport sector may lead to significant biodiversity impacts via multiple human pressures. Biodiversity assessments of biofuels, however, seldom simultaneously address several impact pathways, which can lead to biased comparisons with fossil fuels. The goal of the present study was to quantify the influence of habitat loss, water consumption and greenhouse gas (GHG) emissions on potential global species richness loss due to the current production of biodiesel from soybean and rapeseed and bioethanol from sugarcane and corn. We found that the global relative species loss due to biofuel production exceeded that of fossil petrol and diesel production in more than 90% of the locations considered. Habitat loss was the dominating stressor with Chinese corn, Brazilian soybean and Brazilian sugarcane having a particularly large biodiversity impact. Spatial variation within countries was high, with 90th percentiles differing by a factor of 9 to 22 between locations. We conclude that displacing fossil fuels with first generation biofuels will likely negatively affect global biodiversity, no matter which feedstock is used or where it is produced. Environmental policy may therefore focus on the introduction of other renewable options in the transport sector. Global relative species loss due to first-generation biofuel production 89
6.1 | Introduction
Over the last several decades, various national and local incentives have promoted the use of renewable energy sources as a step toward more sustainable energy use. In major renewable energy markets such as the USA, Brazil and the EU, bioenergy from biomass is the most important renewable energy source, and further growth is expected in all sectors including the transport sector (IEA 2018). However, an increasing demand for biomass can only partly be met by intensifying existing agriculture, and will thus require expansion of the global agricultural area (Beringer et al. 2011, Helmut et al. 2013). A potential downside of such expansion is the potential loss of species when natural vegetation is transformed into croplands (Dale et al. 2010, Elshout et al. 2014, Strona et al. 2018). Additionally, expansion or intensification of agricultural land use may require the extraction of extra surface water to irrigate the feedstocks (Gerbens-Leenes et al. 2009). Therefore, biofuel production may negatively affect the freshwater biodiversity as well as the wetland species that depend on surface water (Vörösmarty et al. 2010, Verones et al. 2017).
Furthermore, to provide fertile soils, the removal of natural biomass and the disturbance of the original soil carbon dynamics (e.g. due to tillage) will induce the release of greenhouse gases (GHGs) into the atmosphere (Searchinger et al. 2008). Additional GHGs are emitted during crop cultivation as a result of farm machinery use, cropland fertilization and irrigation, and other processes that require fossil fuels (Lal 2004, Snyder et al. 2009). Various studies have provided evidence that switching to first generation biofuels may effectively result in an increase in GHG emissions (Fargione et al. 2008, Searchinger et al. 2008, Hoefnagels et al. 2010, Don et al. 2011, Immerzeel et al. 2013), and could thereby contribute to climate change rather than reduce it.
According to Verones et al. (2017), land use, water use and GHG emissions are the three main drivers of ecosystem damage, contributing to over 99% of the total impact. Hence, when assessing the impact of displacing fossil fuels with biofuels on biodiversity, it is important to consider all three drivers. Previously, the global impact of (agricultural) land transformation on biodiversity has been quantified, typically based on species-area relationships (Schmidt 2008, De Baan et al. 2013, Chaudhary et al. 2015). To date, only a few studies have applied such models to the case of biofuels. Chaudhary et al. (2015) analysed biodiversity impacts of bioethanol production in different areas of the world, showing that sugarcane production in Brazil results in a greater species loss than sugar beet production in France and maize (grain or stover) production in the USA. However, they did not address the additional impacts of water use and GHG emissions on biodiversity. Danielsen et al. (2009) compared species richness in natural tropical ecosystems with species richness in oil palm plantations to quantify the impact of oil-palm-related land transformation.
While they also estimated the CO2 emissions related to land transformation, they did not quantify the impact of climate change on biodiversity. Strona et al. (2018) concluded that large-scale expansion of oil palm cultivation in Africa will have unavoidable negative 90 Chapter 6
effects on primates, as there are very few areas that combine a high productivity with low biodiversity importance. Gibon et al. (2017) and Van Zelm et al. (2014) carried out comprehensive assessments of the impacts of GHG emissions and land use (along with acidification and toxicity, but no water use) related to electricity generation and wood- based biofuel production, respectively. A study that assesses the biodiversity loss related to first generation biofuel production worldwide is currently lacking.
The goal of the present study was to quantify the impact on global relative species richness of current first generation biofuel production. The selected biofuels included bioethanol from corn and sugarcane, a potential replacement for fossil petrol, and biodiesel from rapeseed and soybean, an alternative to fossil diesel. The focus area included predominant biofuel producing countries, namely, the USA (corn and soybean); Brazil (soybean and sugarcane); China (corn); and several European countries, including Austria, France, Germany, Italy and Poland (all rapeseed). We assessed the three most important stressors: (1) habitat loss due to land use, (2) habitat loss due to water use and (3) climate change due to GHG emissions. For GHG emissions we not only included the potential species loss in the current situation, but also in future years, as GHG emissions are not directly removed from the atmosphere. We used a default of species loss integrated over a time horizon of 100 years. We analysed two scenarios where biofuels are being produced respectively with and without accounting for the conversion of natural grassland or forest. The scenario ‘without land conversion’ accounts for potential global species loss in the current situation due to cropping activities (e.g. irrigation, fertilizer application) and land occupation compared to the natural state. The scenario ‘with land conversion’ adds biodiversity impacts due to initial loss of carbon after land conversion and the recovery time required for the cropland to go back to the natural state.
6.2 | Material and Methods
The biodiversity impact related to biofuel production is expressed as the global potentially disappeared fraction (PDF) of species per MJ of bioenergy produced every year. We quantified this potential global loss of species due to biofuel production by using the LC-IMPACT method (Verones et al. 2016). LC-IMPACT distinguishes itself from other life cycle impact assessment methods, including the ReCiPe method (Huijbregts et al., 2017), which typically quantified potential species losses at the local scale. The total biodiversity impact was divided in two components, i.e. occupation and transformation. Biodiversity impacts were allocated between the biofuels and by-products (e.g., corn stover, sugarcane bagasse), based on their respective market values. The allocation factors were collected from Wang et al. (2011) and are shown in Table E1. Throughout our analysis, we assume that natural vegetation (either grassland or forest) would be the counterfactual to the croplands being transformed and occupied for feedstock cultivation. Global relative species loss due to first-generation biofuel production 91
6.2.1 | Occupation
The impact of land occupation from crop x cultivated in location i under management -1 strategy j (Iocc,x,i,j in PDF·year MJ ) was calculated as the sum of the fraction of species lost due to habitat loss, water stress, and GHG emissions:
1 (6.1) Iocc,x,i,j = ∙ (BFHL,occ,i,j + (Wocc,x,i,j ∙〖BFWS,i ) + (Mocc,GHG,x,i,j ∙〖BF〗GHG)) CEFx,i,j
-2 -1 where CEF is the crop-to-energy conversion efficiency (in MJ m yr ); BFHL,occ is the terrestrial biodiversity impact factor for species loss caused by land occupation (in PDF m-2); 3 -2 -1 Wocc is the amount of water used during feedstock cultivation (in m m yr ); BFWS is the -3 biodiversity impact factor for species loss caused by water stress (in PDF m ); Mocc,GHG is the -2 -1 GHG emission during biofuel production (in kg CO2eq m yr ); and BFGHG is the terrestrial -1 biodiversity impact factor per unit of GHG emission (in PDF yr kg CO2eq ).
The CEF was calculated as
(6.2) CEFx,i,j = Yx,i,j ∙〖 CBFx ∙〖ECx where Y is the crop yield (in kg crop m-2 yr-1); CBF is the crop-to-biofuel conversion factor (in kg biofuel kg crop-1); and EC is the biofuel energy content (in MJ kg biofuel-1).
The BFGHG is calculated as
(6.3) BF =〖IAGTP ∙〖EF GHG CO2 terr
where IAGTP is the time-integrated absolute global temperature potential of 1 kg of CO2 -1 emitted (°C·year kg CO2eq ), and EFterr is the effect factor, representing the increase in global PDF due to an increase in global mean temperature (PDF °C-1). The IAGTP varies with the time horizon. We used a 100-year time horizon as default and applied the long-term effect at a 1000-year time horizon as a sensitivity check.
6.2.2 | Transformation
-1 The biodiversity impact related to transformation (Itrans,x,i,j in PDF yr MJ ) was calculated as the sum of species lost caused by initial GHG emissions directly after natural land conversion and the habitat loss due to destruction of the original ecosystem:
1 (6.4) Itrans,x,i,j = ∙ (Mtrans,GHG,i ∙ 〖BFGHG +〖BFHL,trans,i,j ) CEFx,i,j ∙PT
-2 where Mtrans,GHG is the GHG emission resulting from land transformation (in kg CO2eq m ); 2 -2 BFHL,trans is the biodiversity impact factor per m of transformed land (in PDF yr m ); and PT is 92 Chapter 6
the plantation time (in years). The default plantation period was set tot to 30 years, which means that we allocated 3.3% (1/30) of the land conversion impacts to the amount of crops produced in a year. As a sensitivity check, we also calculated transformation impacts for a plantation period of 100 years.
6.2.3 | Crop data
Locations of crop cultivation were collected from SPAM (http://mapspam.info), a model that simulates agriculture at a resolution of 10km by 10km at the equator and reduces grid-cell sizes as the distance to the equator increases. It distinguishes among four farm management strategies, which were reduced to three strategies by combining the farms under low input, rain-fed management and those under subsistence, rain-fed management into one low input - no irrigation category. The other two farm management strategies are high input – no irrigation and high input – irrigated. We assume that any agricultural arable land within a country producing the crops of interest can supply the feedstock for that country’s biofuel production. Spatially explicit crop yields were collected from SPAM, while crop-to-biofuel conversion efficiencies and biofuel energy contents were based on the ecoinvent database (Weidema et al. 2013, Wernet et al. 2016) and its documentation (Jungbluth 2007).
6.2.4 | Carbon stock data
The GHG emissions resulting from land transformation (Mtrans,GHG) were calculated as the difference between the carbon and nitrogen stocks of the original, natural system (i.e. natural forest or natural grassland) and those of the cropland. GHGs from three different pools were considered: biomass carbon, soil organic carbon (SOC), and soil nitrogen. Spatially explicit biomass carbon stocks of natural forests at a ~1km by ~1km resolution were collected from Gibbs et al. (2014), and default biomass carbon stocks of different types of natural grasslands were collected from Ruesch and Gibbs (2008). The biomass carbon stock of the crops was set at zero, which is similar to previous work (Elshout et al. 2015). Spatially explicit SOC stocks for both natural forests and croplands at a ~1km by ~1km resolution were also collected from Gibbs et al. (2014). The SOC stocks for natural grasslands were calculated for 18 agro-ecological zones (AEZs) around the globe as a function of soil carbon concentration, bulk density, and depth (as per Guo & Gifford 2002), using data from the Harmonized World Soil Database (FAO et al. 2012). The GLC2000 land- cover map (Bartholome & Belward 2005) was used to identify natural grassland areas. Finally, the average natural grassland SOC stock was calculated for each of the AEZs. The change in soil nitrogen was directly related to the change in soil carbon and was calculated using the equation from Flynn et al. (2012). All SOC values were based on the top 30 cm of soil. Global relative species loss due to first-generation biofuel production 93
6.2.5 | Other GHG emissions
CO2, N2O and CH4 emissions during the biofuel production processes were collected from the ecoinvent database (Weidema et al. 2013, Wernet et al. 2016). This included emissions from both production and application of various inputs, such as pesticides, irrigation water, and machinery use during farming and refining. Country-specific data were preferred, but for missing countries global or rest-of-the-world data were used. Direct and indirect emissions from nitrogen fertilizer application were calculated separately using the methods from Shcherbak et al. (2014) and the IPCC (2006), respectively. The amount of nitrogen fertilizer applied was collected from Elshout et al. (2015). In order to convert quantities of N2O and CH4 to CO2-equivalents, they were multiplied by their respective -1 global warming potentials (GWPs) of 265 and 30 kg CO2-eq kg , respectively, in the case of -1 the 100-year time horizon (IPCC 2013) and 79 and 5 kg CO2-eq kg , respectively, in the case of the 1000-year time horizon (Huijbregts et al. 2016). The impacts of biogenic GHGs and non-biogenic GHGs were all considered equal (as per Hanssen et al. 2017). However, the biogenic GHGs emitted upon combustion of the biofuel are not considered, given that the atmospheric residence time of these GHG can be considered net zero when the biofuel is produced from annual crops (Cherubini et al. 2011). An overview of data collected from the ecoinvent database can be found in Table E2.
6.2.6 | Biodiversity impacts related to habitat loss
The BFs for both land occupation and land transformation were collected from Chaudhary and Brooks (2018), who calculated at an ecoregion level the average global impact of transforming and occupying annual croplands on species of all terrestrial taxa (mammals, birds, amphibians, reptiles and vascular plants) relative to the total species richness of these taxa across the globe. Their factors are calculated by combining a Species-Area- Relationship model with the affinity to broad land use types of 22386 species of mammals, birds and amphibians from the IUCN Red List Habitat Classification Scheme (IUCN 2015) and reptile and plant data from Newbold et al. (2015). The use of such Species-Area- Relationship-based BFs to calculate the biodiversity impact of land use associated with a products’ life cycle was recently recommended by the UNEP-SETAC life cycle initiative (Teixeira et al. 2016, UNEP 2017). We determined which ecoregion each grid cell with feedstock cultivation was located in and selected the corresponding BFs (see Table E3). Chaudhary and Brooks (2018) distinguish between three farming intensity-levels, and we used data for minimal use for the low input - no irrigation scenario, and data for intense use for both high input scenarios.
6.2.7 | Biodiversity impacts related to water stress
For all feedstocks grown under high input – irrigated management, the biodiversity impact of water stress was accounted for. As spatially explicit data on water use by croplands was 94 Chapter 6
lacking, we used water consumption data from ecoinvent (Weidema et al. 2013). Only the water used during feedstock cultivation was considered, given that water withdrawn during feedstock-to-biofuel processing is minimal compared to water usage for irrigation (Mielke et al. 2010). Country-specific impact factors for water stress were collected from LC-IMPACT (http://www.lc-impact.eu; Verones et al. 2016) (Table E4). These factors account for the relative species loss of freshwater species, terrestrial species living in river sheds, and terrestrial vascular plant species outside the wetlands.
6.2.8 | Biodiversity impacts related to GHG emissions
-14 -1 The IAGTP was set at 4.76·10 °C yr kg CO2eq for a 100-year time horizon, based on Joos et al. (2013). For the effect factor, we used data from Urban (2015), who predicts that temperatures 0.8°C above pre-industrial levels will cause the extinction of 2.8% of terrestrial species and that temperatures 4.3°C above pre-industrial levels cause the extinction of 15.7% of terrestrial species. An effect factor of 0.037 PDF °C-1 was calculated from the differences between these two scenarios, i.e., an average of 3.7% global species loss is expected per degree Celsius global mean temperature rise. Combining the IAGTP 10-15 from Joos et al. (2013) and the effect factor from Urban (2015), we derived a BFGHG of 1.76· -1 -14 PDF yr kg CO2eq . Using the same approach and data sources, a BFGHG of 1.57·10 PDF yr kg -1 CO2eq was calculated for the 1000-year time horizon.
6.2.9 | Reference calculations
The biodiversity impact of producing and combusting fossil fuels (If,w) was calculated as a reference to the impact of producing biofuels. GHG emissions (from combustion as well as e.g. mining and refining of the crude oil), habitat loss (due to land transformation and occupation) and water stress (mostly due to cooling water extraction) were included in the calculations:
(6.5) If,w = (MGHG,w ∙〖BFGHG ) + (Atrans,w ∙〖BFHL,trans) + (Aocc,w ∙〖BFHL,occ) + (Ww ∙〖BFWS)
where the type of fossil fuel w was petrol or diesel, as a reference to bioethanol and
biodiesel, respectively; MGHG is the GHG emission during fossil fuel production and -1 combustion (in kg CO2eq MJ ); Atrans is the area of land transformation required for fossil 2 -1 fuel production (in m MJ ); Aocc is the land area occupied for fossil fuel production (in m2·year MJ-1); and W is the amount of water used during fossil fuel production (in m3 MJ-1). Area-weighted global averages of the biodiversity impact factors of habitat loss were provided by Chaudhary (personal communication; 30-04-2018), and those for water use were collected from LC-IMPACT (http://www.lc-impact.eu; Verones et al. 2016). Data on GHG emissions, land use and water use for the production and combustion of petrol and diesel were collected from the ecoinvent database (Weidema et al. 2013, Wernet et al. 2016) and its documentation (Jungbluth 2007). The GWPs mentioned above were used Global relative species loss due to first-generation biofuel production 95
to convert emissions of N2O and CH4 to CO2-equivalents. No by-products of fossil fuel production were considered.
6.2.10 | Fuel blends
Default calculations were performed for the production of pure E100 bioethanol and B100 biodiesel. However, biofuels are most often used in blends with petrol and diesel at varying mixing ratios, such as E25 (25 vol% bioethanol, 75 vol% petrol) commonly used in Brazil (Macedo et al. 2008), and B5 (5 vol% biodiesel, 95 vol% diesel) in the EU (Kousoulidou et al. 2010). We therefore calculated the global relative species loss related to the production of the most common fuel blends (Ix+w), i.e., E10, E25, E85, B5 and B20, as follows:
φBF ×〖ECx × ρx × (Iocc,x,i,j + Itrans,x,i,j) + φFF ×〖ECw× ρw × Ifossil,w (6.6) Ix+w,i,j = φBF ×〖ECx × ρx + φFF × ECw × ρw Where φ is the vo lume fraction of biofuel and fossil fuel in the fuel blend, and ρ is the fuel density (in L kg-1). Data on fossil fuel and biofuel densities were collected from Atabani et al. (2012) and Yüksel and Yüksel (2004), and can be found in Table E5. Impacts on the global biodiversity were calculated per litre of fuel, rather than per MJ, in order to avoid uncertainty from mixed fuel energy contents. Potential impacts of the blending process were not covered in the calculations.
6.2.11 | Variable importance
We determined to what extent the variation in biodiversity impact was attributable to the producing country, crop type, farm management strategy, plantation time, and time horizon of choice by using an ANOVA on the log-transformed biodiversity impact values. The unexplained variance (i.e., residual) can be attributed to the remaining spatial variation in biodiversity impacts within countries.
6.3 | Results
6.3.1 | Biofuels vs. fossil fuels
The occupation and transformation impact of biofuel production on global relative species loss was calculated for a total of 35,699 grid cells in the main biofuel-producing countries. Overall, the global relative species loss caused by bioethanol and biodiesel production systems turned out to be larger than the global relative species loss caused by fossil diesel and petrol production in more than 90% of the locations. Replacing fossil fuels with biofuels would on average increase the time-integrated global relative species loss by a factor of 30 to 128. Neglecting land transformation and only accounting for land occupation (referring to situations where feedstocks are grown on already established croplands), 96 Chapter 6
biodiversity impact of biofuel production still exceeds the impact of fossil fuel production (Figure 6.1). Bioethanol produced from Chinese corn and Brazilian sugarcane were found to have the largest median impact on biodiversity. The impacts of bioethanol production in these countries also showed highest spatial variation, with outcomes ranging +/- a factor of 19 in the case of Chinese corn and 22 in the case of Brazilian sugarcane (based on 90% range; Figure 6.1a). The biodiversity impacts of fuel blends increase with the share of biofuel in the mix (Figure 6.2). On average, B5 from European rapeseed has the smallest impact, followed by E10 and B5 from USA corn and soybean, respectively. E85 from Chinese corn and Brazilian sugarcane are the worst performing fuel blends.
6.3.2 | Environmental stressor importance
The impact of habitat loss due to land transformation and occupation dominates the total impact of biofuel production, as it was found to be two to three orders of magnitude higher than the impacts of water stress and GHG emissions. The biodiversity impact of water stress is found to be negligible, except for the production of corn-based bioethanol in the USA, where it contributes more than 25% in 10% of the locations (Figure E1a). When neglecting the impact of land transformation, the biodiversity impact of land occupation is still dominant for all biofuel production systems (Figure E1b).
Global relative species loss due to first-generation biofuel production 97
figure 6.1 | Global relative species loss due to bioethanol and biodiesel production when adopting a plantation time of (A) 30 years and (B) 100 years, and considering GHG impacts over a 100-year time horizon. The total impact is the sum of the impacts of occupation (also provided separately) and transformation. The boxes show the first quartile, median, and third quartile, and the ends of the whiskers show the 10th and 90th percentiles of the grid-specific impacts. The dashed line shows the impact of the fossil alternatives, i.e., petrol (upper graph) and diesel (lower graph). 98 Chapter 6
figure 6.2 | Global relative species loss due to production of various common fossil fuel-biofuel blends, when adopting a plantation time of 30 years and considering GHG impacts over a 100-year time horizon. Only combined impacts of occupation and transformation are shown. The boxes show the first quartile, median, and third quartile, and the ends of the whiskers show the 10th and 90th percentiles of the grid-specific impacts. Results for other scenarios can be found in Figure E3a-c.
6.3.3 | Variable importance
Country and management type were found to explain 17% and 11% of the variance, respectively, while the other variables explain less than 5% (Table E6). The residual represents the spatial variation within countries, and attributes to 67% of the variance. This indicates that the environmental performance of biofuels would improve more by selecting the most suitable locations within the countries currently producing biofuels than by switching to production in other countries, adopting different farm management strategies, growing crops for a longer time period, or approaching the impact of GHG emissions in an alternative way.
6.3.4 | Sensitivity analysis
When assuming a plantation time of 100 years, the impact of land transformation is distributed over a larger amount of crop harvested, which lowers the median global relative species loss per TJ of bioenergy produced by a factor of 1.6-2.7 (Figure 6.1b). On the other hand, using a 1000-year time horizon as the starting point for the time-integrated Global relative species loss due to first-generation biofuel production 99
impact of GHG emissions hardly changes the median global relative species loss of the biofuel production systems (Figure E2a), owing to the negligible contribution of GHG emissions to the total impact. However, the impacts of fossil petrol and diesel production more than doubled in case of a 1000-year time horizon, which caused the land occupation impacts in about 25% of the European rapeseed-producing locations to become lower than the total impact of fossil petrol production. The same holds for the extreme scenario with a 1000-year time horizon for GHG impacts and a plantation time of 100 years (Figure E2b).
6.4 | Discussion
We show that potential global species loss per unit of first generation biofuel production for transport exceeds the biodiversity impacts of their fossil counterparts. The models used in the present study come, however, with a number of limitations. First, all feedstocks were assumed to be solely mono-cropped; however, many farmers use multi- cropping systems. For example, approximately one-third of farmlands in the Midwest USA alternates between corn and soybean biannually (sometimes also including other crops, such as wheat or alfalfa) (Plourde et al. 2013, Borchers et al. 2014). In this situation, the overall impact of bioethanol and biodiesel production would equal the average of the impacts of USA corn and USA soybean. Alternatively, multi-cropping within one year would lower the impact of land transformation, as impacts are allocated among more crop biomass in the same number of years. A complete investigation of the effect of crop rotation on the relative global species loss exceeded the scope of this study, but it could potentially entail an increase in crop yield, greater soil carbon and soil nitrogen storage, and less fertilizer application, compared to a situation of mono-cropping. Whether or not this would sufficiently improve the performance of the first generation biofuels to outperform fossil fuels should be investigated in future work.
Second, our outcomes rely heavily on the data input, such as the crop yields simulated by SPAM (http://mapspam.info). Recently, Anderson et al. (2014) analysed four major agricultural models, including SPAM, and identified considerable differences in crop yields. Still, as there is no clear preference for any alternative model, we consider SPAM as appropriate for the purpose of the current work, especially given the useful disaggregation in three farm management systems it provides.
Third, while the present study bases the biodiversity impacts of land use, water stress and climate change on recent, scientifically acclaimed and, to our opinion, most suitable methods, the biodiversity loss factors are not without uncertainty. For land use, this is demonstrated by the fact that the land use impact factors from Chaudhary and Brooks (2018) differ two orders of magnitude from those derived in previous work (Chaudhary et al. 2015), owing to methodological choices. The biodiversity loss factors are based on a 100 Chapter 6
comprehensive meta-analysis from Urban (2015). Climatic tolerance of species is, however, difficult to quantify, and evolutionary changes in populations cannot be predicted (Araújo & Rahbek 2006). Furthermore, the meta-regression model does not account for the fact that a response to climate change by one species will have indirect impacts on the species that depend on them (i.e., biotic interactions at the community level) (Bellard et al. 2012). Also, the LC-IMPACT method we applied, assumes that the species losses of the three main drivers are mutually exclusive, whereas the species lost due to the three stressors may actually partly overlap. Note, however, that given the domination of land use as stressor in the total impacts of biofuel production, the influence of the assumption of simple additive effects is relative small.
Finally, it is important to emphasize that we do not take into account any potential impacts that occur abroad due to relocation of food or feed croplands after biofuel feedstock production has replaced the local food or feed production, i.e., indirect land-use change (Searchinger et al. 2008, Verstegen et al. 2015). In our study, we always quantify species loss of land use and GHG emissions compared to the natural state, regardless the current land use at the location. This means that biofuel production at a certain location is always evaluated compared to the natural reference. We may underestimate global species loss due to biofuel production, in situations where biofuel production results in indirect land use change in areas with higher species richness and/or higher initial carbon stocks. This would be the case, for instance, if producing corn-based bioethanol from the USA leads to indirect agricultural land transformation in the tropical rainforest of Brazil (e.g. Keeney & Hertel 2009).
In conclusion, the current study quantified the impact of first generation biofuels on biodiversity due to GHG emissions, land-use-induced habitat loss, and water-use- induced habitat loss. Our findings suggest that first generation biofuel production in the countries evaluated here is unfavourable compared to fossil fuel use in the transportation sector, even if the biofuel feedstocks are grown on existing cropland for a period of 100 years. Habitat loss following land transformation and occupation was found to be the dominant cause of global species loss. Hence, when aiming to protect global biodiversity, the present work suggests that policy makers should support the development of other renewable energy sources with lower land demand than first generation biofuels, such as third-generation biofuels (Correa et al. 2017). Further research is required to assess the biodiversity impacts of other renewable energy sources for the transport sector.
Acknowledgements
PE, ZS and MH were supported by the ERC project (62002139 ERC – CoG SIZE 647224). We thank Dr. A. Chaudhary for providing additional data.
chapter 7
Synthesis
Synthesis 105
In the previous chapters, the impact of biofuel production on climate change and biodiversity loss was assessed. In this synthesis, several cross-cutting aspects of these impact assessments are discussed. Section 7.1 reflects on the use of a footprint (unit is kg
CO2-eq per MJ) vs. payback time (unit is year) as a metric to evaluate the GHG impacts of biofuel production. Section 7.2 compares the outcomes of GHG footprints with biodiversity footprints of biofuels. Section 7.3 provides an overview of the environmental performance of the different biofuel production systems, as well as the role of farm management and time horizon considerations that influence the outcomes of the biofuel impact assessments. Section 7.4 briefly discusses the expected impact of expanding feedstock production within the biofuel producing countries. Finally, the overall conclusions are summarized in section 7.5.
7.1 | GHG footprints vs. GHG payback times
In this thesis, two different metrics were used to quantify the impact of biofuel production on climate change: (1) GHG footprints and (2) GHG payback times. The typical GHG footprint metric expresses the amount of CO2-equivalents emitted per MJ of bioenergy (well-to-tank) or per kilometre driven (well-to-wheel; Gnansounou et al. 2009). One major advantage of this metric is that it allows for easy comparison with GHG footprints of fossil fuels or other energy sources. For the GHG payback times metric, the initial GHG emissions from land transformation are considered an “investment” that can be “paid back” if the fossil fuel is replaced by a biofuel with less fossil GHG emissions during production and use for a certain period (Fargione et al. 2008, Gibbs et al. 2008, Holly et al. 2008). Therefore, the GHG payback time provides insight into how long biofuels should be produced on the same location before they can be considered a preferable alternative to their fossil counterparts.
Equation 7.1 shows how the GHG footprint of biofuels was calculated:
GHGbiogenic (7.1) 〖GHGbiofuel = LT +〖GHGprod Y×BF×E
Here, GHGbiofuel is the GHG footprint of biofuel production in kg CO2-eq per MJ of bioenergy produced, GHGbiogenic is the biogenic GHG emission from natural biomass and soil upon land transformation in kg CO2-eq per ha, LT is the time the agricultural field is in production in years, GHGprod is the ongoing GHG emission during production and use of chemicals and farm machinery, including fertilizer application, in kg CO2-eq per ha per year, Y is the crop yield in kg crop per ha per year, BF is the biofuel conversion efficiency in kg biofuel per kg crop, and E is the energy content in MJ per kg biofuel. 106 Chapter 7
Equation 7.2 shows how GHG payback times were derived:
GHGbiogenic (7.2) 〖PBTbiofuel = 〖 GHGbenchmark × Y × BF × E-(GHGprod)
Here, GHGbenchmark is the GHG emission of fossil fuel production in kg CO2-eq per MJ of fossil energy produced. When rearranging eq. 7.1 to: GHG biogenic +〖GHG (7.3) Y × BF × E = 〖 LT prod GHGbiofuel
and filling eq. 7.3 into eq. 7.2, PBTbiofuel can be expressed as:
GHGbiogenic (7.4) 〖PBTbiofuel =〖GHGbenchmark〖 〖GHGbiogenic 〖 × ( + GHGprod ) - GHGprod GHGbiofuel LT
Equation 7.4 was used to determine the relationship between GHG payback time and GHG footprint for the four major biofuel production systems. Figure 7.1 shows a positive relationship between the two metrics, and throughout most of the range they induce similar conclusions. However, GHG footprints of biofuels that exceed the GHG footprint
of fossil fuels (i.e. 0.088 and 0.082 kg CO2-eq per MJ for petrol and diesel, respectively) will per definition result in infinite payback times. Such an infinite payback time was in fact derived for two third of the locations in figure 7.1. In this case, the payback time metric intuitively shows that replacing fossil fuels with biofuels will not improve the amount of GHGs that are emitted. However, information on how much worse biofuel production will actually be is lost within the infinite payback time category. Therefore, the advantage of using the GHG footprint metric is that outcomes are continuous over the full data range. On the other hand, to calculate a GHG balance, one has to make assumptions regarding the time that an agricultural field is in use, as the biogenic emissions from land transformation must be allocated over all crops produced during this period. The length of this period is highly uncertain and thus possibly degrades the realism of the outcomes. Calculation of payback times does not require this assumption. All in all, both GHG footprints and payback times are considered to be useful metrics. In case of infinite GHG payback times, GHG footprints may provide further differentiation between locations and biofuels. Synthesis 107
figure 7.1 | Relationship between GHG footprint and GHG payback time, following equation 7.4. Calculations were done using corn, sugarcane, rapeseed and soybean data from chapter 4, with a 30- year plantation time and a 100-year time horizon. GHG footprints that exceed the fossil benchmark -2 -2 (i.e. 8.8·10 and 8.2·10 CO2-eq per MJ for petrol and diesel, respectively) have per definition infinite GHG payback times.
7.2 | GHG footprints vs. biodiversity footprints
In this thesis, both GHG footprints and biodiversity footprints (i.e. relative global species loss), were derived. This allows for a direct comparison between both footprints for the four major biofuel types, using data from chapter 6. Figure 7.2 shows the relationship between the GHG footprint and biodiversity footprint of a biofuel produced in a particular location. The figure reveals two different situations: On the left side of the graph, the relationship between GHG footprint and biodiversity footprint shows no clear relationship, as the biodiversity footprints in this section depend mostly on habitat loss and/or water stress. Towards the right side of the graph, there is a clear linear relationship between both footprints, indicating that GHG emissions dominate the biodiversity footprints. The majority of data points lies on the left, as habitat loss was found to be the greater contributor to global relative species loss than GHG emissions. Therefore, GHG footprint should not be used as an indicator of biodiversity impacts due to biofuel production. 108 Chapter 7
figure 7.2 | Relationship between GHG balance and global relative species loss, based on corn, sugarcane, rapeseed and soybean data from chapter 6, assuming a 30-year plantation time and a 100-year time horizon.
7.3 | Environmental performance of first generation biofuels
7.3.1 | Regional differences
European rapeseed Throughout this thesis, rapeseed grown in temperate regions and, specifically, Europe, was shown to be the best performing first generation biofuel both from a GHG and biodiversity footprint perspective. Currently, there is little rapeseed cultivation in the tropics and biodiesel production from rapeseed is therefore practically non-existent in this region (Tomm et al. 2011). The present work has shown that rapeseed grown in the tropics would be the worst performing feedstock, based on expected yields. Often these yields fall below economically viable levels, which pushes the GHG payback times beyond 100 or even 500 years. The stark contrast in environmental performance of rapeseed-based biodiesel from temperate regions and from the tropics shows that identifying the most suitable biofuel feedstock can only be done in the context of the location where it is grown.
Projections from the European Commission state that the production of rapeseed will slowly decline in the coming decade, in favour of imported soybean (Trompiz 2017, 18 December). Considering that rapeseed was found to perform better than soybean in this thesis, such alterations are likely to negatively affect the GHG and biodiversity footprint of Synthesis 109
biofuel production. Remarkably, even though rapeseed is the main feedstock for biodiesel production in the EU, few comparisons between the GHG emissions of rapeseed-based biodiesel and other biofuel production systems exist in the scientific literature. Halleux et al. (2008) found that biodiesel from rapeseed performs better than bioethanol from sugar beet in Belgium and Kaltschmitt et al. (1997) showed that biodiesel from rapeseed outperformed bioethanol from winter wheat in Germany. In contrast, according to Cherubini et al. (2009), rapeseed performs poorly due to a relatively low oil content of harvested seeds and, consequently, a low land use efficiency compared to, for example, sugarcane. The results of this thesis do not support the latter conclusion.
USA and Chinese corn Bioethanol from corn in the USA was found to be amongst the best performing biofuels, both regarding limited GHG emissions and species richness loss. However, the relative low impacts for corn-based bioethanol from the USA do not necessarily advocate the use of corn in other regions in the world. For instance, current corn-based bioethanol from China was found to be the biofuel production system with the largest impact on global relative species richness, as well as to have the highest GHG emissions of all included biofuel production systems. The difference in performance between Chinese corn and USA corn is caused by a difference in crop yield (which appears to be about 80% higher in the USA case), and a difference in biogenic GHG emissions upon land transformation (which are about 80% higher in the Chinese case). Until now, China has been neglected in most papers that compare biofuel production systems, even though it is rapidly becoming a major biofuel producer. Still, the limited evidence available from other literature sources indeed confirm that bioethanol production from Chinese corn is currently not competitive globally due to low yields (Li & Chan-Halbrendt 2009, Yang & Chen 2012). Yang and Chen (2013) calculated that GHG emissions would increase six times when replacing fossil gasoline, even though they did not account for biogenic GHG emissions during land transformation. Overall, the results strongly suggest that producing bioethanol from Chinese corn is, in its current form, not a strategy that benefits the environment.
Brazilian sugarcane In this thesis, sugarcane-based bioethanol from Brazil is the second worst biofuel with regard to relative global species loss, although the biodiversity impact of habitat loss due to production of Brazilian sugarcane was not significantly higher than the impact of habitat loss from feedstocks grown in the temperate regions. Sugarcane still performed slightly worse than most other feedstocks due to the impact of GHG emissions. The biomass and soil of tropical rainforests are especially rich in carbon, which is released to the atmosphere during burning and decay of the biomass and disturbance of the soil. These GHG emissions could be avoided if sugarcane were to be produced outside of the rainforest and cerrado grassland areas. In temperate regions, including Europe and most of the USA, the climate does not allow for sugarcane cultivation. However, sugarcane is successfully cultivated in the tropical and subtropical regions in the southern USA, especially Florida. 110 Chapter 7
Here, biogenic carbon emissions from biomass and soil upon land transformation are potentially much lower (Gibbs et al. 2014), but the overall performance of sugarcane-based bioethanol from the USA would depend on regional yields , input requirements , and the efficiency of crop refining (Gilbert et al. 2006, Rice et al. 2006, Pimentel & Patzek 2008); i.e. data that were not covered in the present work. Given the climatic restriction of sugarcane cultivation, the possibilities for sugarcane-based bioethanol production in the USA or other countries with smaller natural carbon stocks than the tropics, are very limited.
USA and Brazilian soybean As in the case of sugarcane, soybean grown in the Brazilian rainforest or cerrado grasslands has relatively high GHG emissions. Previously, Reijnders and Huijbregts (2008) found that growing soybeans in Brazil would result in GHG emissions that exceed the emissions for fossil diesel and Gibbs et al. (2008) found soybean to be the feedstock causing most GHG emissions in the tropics. Fargione et al. (2008) showed that soybean replacing rainforest is one of the worst options when it comes to GHG emissions, but soybean replacing grasslands is the second best option, behind sugarcane. In the USA, soybean cultivation was previously found to lead to net carbon sequestration (Kim & Dale 2005, Adler et al. 2007). In this thesis, a net carbon sequestration in approximately half of the soybean- cultivating locations in the USA was found, and even where our models returned a net carbon release, soybean-based biodiesel was often found to have lower GHG emissions than fossil diesel. It follows that replacing diesel with soybean-based biodiesel from the Midwest USA or other locations with relatively low natural carbon stocks may be a successful way to reduce GHG emissions. However, when focusing on relative global species loss, soybean from the USA and Brazil both perform worse than European rapeseed and corn grown in the USA. This advocates the use of other feedstocks than soybean when species conservation is the main objective.
7.3.2 | Farm management strategy
Throughout this thesis, distinction was made between different farm management strategies. Beforehand, intensive or high-input farming was expected to have a higher impact on the environment than extensive or low-input farming. After all, production and application of fertilizers causes additional life cycle GHG emissions, and a more intensive use of the farmland is likely to render it more inhabitable to many plant and animal species than extensive farm management. However, figure 7.3 shows that in all but one case (i.e. Brazilian soybean), intensive feedstock cultivation results in less GHG emissions per MJ of bioenergy produced. The additional GHG emissions from fertilizer production and application were outweighed by the positive effects of an increasing crop yield.
Regarding biodiversity loss, two seemingly opposing conclusions were drawn is this thesis. The survey data on local species richness showed that conventional, high-input farming has a much larger impact on biodiversity than low-input farming through the Synthesis 111
effects of habitat loss (i.e. excluding the effects of climate change). In the global relative species loss assessment, this difference was accounted for by selecting impact factors for three intensity levels of annual crop farming as provided by Chaudhary and Brooks (2018). However, the difference between these impact factors was smaller than expected, with the land use impacts of lightly and intensely used croplands exceeding that of minimally used croplands by only 7% and 8% on average, respectively. As a result, the negative impact on the global relative species loss of intensifying farm management was also outweighed by the crop yield increasing accordingly. Figure 7.3 reveals that the global relative species loss per MJ of bioenergy is lower for biofuels produced from intensively grown feedstocks. Whether the factors from Chaudhary and Brooks (2018) are realistic should be a focus of future research.
figure 7.3 | Scatter plot of the median GHG balance and median global relative species loss for each biofuel production system, based on data from chapter 5, and assuming a 30-year plantation time and a 100-year time horizon. Error bars indicate the 25th to 75th percentiles. Biofuel production from extensively (ext) grown feedstocks is shown in black, and biofuel production from intensively (int) grown feedstocks in red. Note that the outcomes discussed in section 7.3.1 are the result of all farm management strategies combined, and do therefore not correspond to the results in this graph.
7.3.3 | Temporal considerations
When assessing the impacts of biofuel production, methodological choices must be made that concern temporal aspects. First, there is plantation time or cultivation period, which entails the period during which a particular cropland is used to produce a certain crop/ feedstock, either in the past or in the future. While plantation time does not affect the 112 Chapter 7
ongoing emission of GHGs per MJ of bioenergy that is produced, it does directly influence the amount of land transformation-related GHG emissions that are attributed to each MJ. Given that these land transformation-related GHG emissions are the main contributor to the total GHG emissions of agricultural products and biofuels, the plantation time selection strongly affects the GHG performance of these products. Throughout this thesis, multiple plantation times of 30, 100 and 1000 years were assumed to calculate GHG balances and global relative species loss of several biofuel production systems. A 30-year plantation time was considered as an average period of crop cultivation that can be reached within one generation of farmers. A plantation time of a 100 years can be classified as a fairly long period of cropland occupation, which has been reported to be feasible without losing crop yields and soil quality (Manna et al. 2007). Finally, plantation times of 1000 years represent an extreme situation of very lengthy agricultural land use. For impact assessments, plantation times between 10 and 1000 years might all be realistic, but the implications of selecting a particular plantation time should be well understood. For example, in situations where the total impact of the biofuel is dominated by the land transformation phase, assuming a plantation time of 100 years instead of 30 years will lower the total impact to approximately a third of the original impact. Hence, the longer the assumed plantation time, the better a biofuel seems to perform regarding GHG emissions. In GHG impact assessments, it is vital to select the most realistic period in the location that is studied, or otherwise use a range of short to long plantation times, in order to properly inform policy makers.
The second methodological choice that has to be made regarding time is the time horizon at which the impact of GHG emissions is assessed. An amount of GHGs emitted to the atmosphere will persist for many decades or centuries, during which the substances will contribute to climate change. This contribution is expressed as an time-integrated
absolute global warming potential, which, for CO2, is an order of magnitude larger in the case of a 1000 year time horizon than in the case of a 100 year time horizon (Joos et al. 2013, Huijbregts et al. 2016). Therefore, time horizon can have a significant influence on the outcomes of an impact assessment of GHGs. There are currently no strict guidelines on the time horizon that should be selected in impacts assessments. A rather arbitrary time horizon of 100 years is used in various studies, including IPCC assessments. Other time horizons commonly used include 20, 50, 500 and 1000 years (e.g. Lashof & Ahuja 1990, Cherubini et al. 2011, Kendall 2012). Which time horizon is selected depends mostly on the perspective of the researcher or client. In most chapters of this thesis, GWPs that correspond to the 100-year time horizon were used. When focusing on GHG emissions only, a longer time horizon causes a small deviation in results due to a decrease in relative
contribution of N2O and fossil CH4 to the total GHG impact. In most biofuel production
systems CO2 is the dominant GHG, so a change in time horizon has little effect. However, chapter 6 showed that outcomes can differ greatly if an alternative time horizon is adopted. Here, relative species loss was also calculated when adopting a 1000-year time horizon, as per Huijbregts et al. (2016). The relative species loss due to fossil fuel production Synthesis 113
was found to more than double compared to the 100-year time horizon scenario, while the difference for the included biofuel production systems was nihil. As a result, the change in time horizon from 100 to 1000 years made biofuels relatively more favourable over fossil fuels. This shows how the selection of the time horizon influences the outcomes of fuel- related impact assessments, and scientists should substantiate their selection of a time horizon, or provide a range of time horizons so that the reader can decide which is most appropriate.
7.4 | Importance of future locations
Most impact assessments aim to quantify the impact of an established biofuel production system (e.g. Kim & Dale 2005, Luo et al. 2009, Cavalett & Ortega 2010, Malça & Freire 2011, Mukherjee & Sovacool 2014) or investigate the possibility of using feedstocks that are already grown in the region (Kadam 2002, Achten et al. 2010, Fernando et al. 2010). In those cases, actual agricultural data on crop yields and fertilizer inputs should be used as input. However, potential cultivation of feedstocks can also be modelled all over the world, thereby investigating the possibility of producing biofuels made from feedstocks grown outside of their current cultivation area. Chapter 3 has shown that, with regard to reducing GHG emissions, best results can be expected with high-input corn, high-input rapeseed and high-input winter wheat in the temperate regions of the northern hemisphere, including the EU and USA. In the southern hemisphere, including Brazil, production of bioethanol from high-input corn appears to be most promising, with GHG payback times generally below 20 years.
The current major biofuel producing countries may also expand their feedstock production within the country borders. In order to assess how this could influence the GHG impact of biofuel production, GHG balances were calculated for all potential feedstock producing grid cells within the USA, China, EU and Brazil, based on data from chapter 3 of this thesis. A subset was created containing the GHG balances in all actual feedstock producing grid cells within these countries, as identified in chapter 4. Figure 7.4 presents a comparison between the grids with potential and actual feedstock, assuming a 30-year plantation time and a 100-year time horizon. For each included biofuel-country combination, the median
GHG balances of actual and potential feedstock locations differs 0.05 kg CO2-eq per MJ of bioenergy at most. In the cases of Chinese corn and Brazilian soybean, a minor decrease in GHG emissions might be achieved when expanding or moving feedstock cultivation to new locations. For the other biofuel production systems there are no large changes in median GHG balances to be expected when new locations are selected for feedstock cultivation. However, figure 7.4 reveals the presence of more extreme GHG balances at the upper end of the 95%-range of the potential locations, indicating that biofuel production in these locations should be avoided to avert an increase of the overall GHG balance. 114 Chapter 7
The impact on global relative species loss of expanding biofuel feedstock production beyond the current production ranges has not been assessed in this thesis. The findings for GHG emissions cannot be extrapolated to biodiversity loss, given the poor relationship between GHG footprint and biodiversity footprint in most locations (see section 7.2). Nevertheless, wherever biofuels are produced, a net increase of global relative species loss compared to fossil fuel production is to be expected.
figure 7.4 | Boxplots showing the 95%-interval of GHG balances of current and potential feedstock production in the six main biofuel producing countries. A 30-year plantation time and 100-year time horizon were assumed for the calculations.
7.5 | Conclusions
The main conclusions of this PhD thesis are: – GHG footprints and GHG payback times show the same insights if biogenic carbon emissions dominate the results. If fossil emissions, however, dominate the results, these two metric do not linearly correlate. Both metrics are useful for a comparison of GHG life cycle emissions between biofuels and fossil fuels. – GHG footprints and biodiversity footprints of biofuels show no positive correlation, as the biodiversity footprints are dominated by the effects of habitat loss rather than GHG emissions. Synthesis 115
– Increasing crop yields through fertilizer application typically outweigh the GHG emissions from fertilizer production, and thereby reduce the GHG footprint of biofuels. – Displacing fossil fuels with first generation biofuels is often not preferable both from a GHG footprint as well as biodiversity footprint point of view. Land use change is the dominant factor causing GHG emissions and biodiversity loss. One way to avoid land use change is through focussing on second-generation rather than first generation biofuels, which allows for simultaneous production of biomass for both biofuels and food/feed, and could therefore result in land savings rather than additional land use. The prospects of producing sustainable second-generation biofuels should be further investigated.
chapter 8
Appendices 118 Appendices
Appendix A
A1 | Additional results
A
B
figure A1 | GHG balance of corn grain production throughout the Midwest and Southern USA, assuming 100 years of crop cultivation, when allocating the GHG emissions amongst the grain and stover based on (A) their mass and (B) their economic value. States and counties in white are excluded due to a lack of data on crop yields and/or fertilizer application. FBM = fossil benchmark, which is -2 8.8∙10 kg CO2-eq per MJ for petrol. 119
figure A2 | GHG balance of soybean production throughout the Midwest and Southern USA, assuming 100 years of crop cultivation. All emissions were allocated to the beans. States and counties in white are excluded due to a lack of data on crop yields and/or fertilizer application. FBM = -2 fossil benchmark, which is 8.2∙10 kg CO2-eq per MJ for diesel.
A2 | Emissions from nitrogen fertilizer application
The direct emissions (Nfert,direct, g N2O / ha / yr) were calculated using a nonlinear response relationship between the amount of nitrogen fertilizer applied and the amount of N2O emitted due to soil mineralization, as developed by Shcherbak et al. (2014):
MN2O (A1) Nfert,direct = Nappl × (EF0 +〖∆EFN × Nappl) × MN where Nappl is the annual nitrogen application rate (kg N / ha / yr), EF0 is the N2O-N emission factor at zero nitrogen input (%), and ∆EFN is the change in the emission factor per kilogram of additional N input (% / kg N · ha · yr). For corn, we selected the emission response specifically derived for non-N fixing crops (EF0 at 6.58%, and ∆EF at 0.0181% / kg
N · ha · yr), while the emission response for N fixing crops was selected for soybean (EF0 at 3.06%, and ∆EF at 0.1800% / kg N · ha · yr) (Shcherbak et al. 2014).
To calculate the amount of N2O indirectly emitted from fertilizer application (Nfert,indirect, kg
N2O / ha / yr), the annual nitrogen application rate was multiplied with default emission factors and fractions (as per IPCC 2006):
MN2O (A2) Nfert,indirect = Nappl × (Fv ×〖EFv + Fl ×〖EFl ) × MN
where Fv is the fraction of fertilizer nitrogen that volatilizes as NH3 and NOx (kg (NH3+NOx)
N/ kg N applied), EFv is the N2O emission factor for NH3 and NOx volatilization and re- deposition (kg N / kg NH3+NOx volatilized), Fl is the fraction of nitrogen lost through leaching or runoff (kg N / kg N applied) and EFl is the N2O emission factor for leaching 120 Appendices
and runoff (kg N / kg N leached or in runoff water). Using default emission factors and
fractions from the IPCC (2006), Fv was set at 0.1 kg NH3+NOx / kg N applied, EFv was set at
0.01 kg N / kg NH3+NOx volatilized, Fl was set at 0.3 kg N / kg N applied, and EFl was set at 0.0075 kg N / kg N leached or in runoff water.
A3 | Calculation example
Here we provide a calculation example of the GHG balance at the grid-, county-, and state- level. As a case, we selected bioethanol production from corn cultivated in grid cell 10,651 within Preble, Ohio (Figure A6), and assuming 30 years of cultivation. Allocation is based on the mass of the grain and stover.
Figure A6 | Location of Preble, Ohio.
Part 1: Grid-level calculations
Csoil,crop,10651 = 71.67 Mg C / ha cropland SOC stock (Gibbs et al. 2014)
Csoil,forest,10651 = 116.61 Mg C / ha reference SOC stock (Gibbs et al. 2014)
Cbiomass,corn,10651 = 0 Mg C / ha set (see main text)
Cbiomass,forest,10651 = 75 Mg C / ha natural biomass carbon stock (Gibbs et al. 2014)
Nappl,corn,ohio = 0.02 kg N / kg corn N fertilizer application rates = 115.31 kg N / ha / yr (LCA Digital Commons)
Ycorn,preble = 90.8 bushels grain / acre / yr crop yield (USDA) = 5.7 Mg corn / ha / yr 121
BFcorn,bioethanol = 0.66 Mg fuel / Mg corn ecoinvent v3 documentation (Weidema et al. 2013)
Ebioethanol = 29700 MJ / Mg fuel ecoinvent v3 documentation (Weidema et al. 2013)
∆Cbiomass = Cbiomass,forest - Cbiomassl,corn CO2 emissions from biomass loss = 75 Mg C / ha
= 275 Mg CO2-eq / ha
∆Csoil = Csoil,forest - Csoil,corn CO2 emissions from SOC loss = 6.06 Mg C / ha
= 44.94 Mg CO2-eq / ha
∆Nsoil = Csoil,forest - Csoil,corn x (1/15) x 0.01 x (44/28) N2O emissions from soil mineralization
= 0.05 Mg N2O / ha
= 12.48 Mg CO2-eq / ha
Nfert,direct = 0.001 x Nappl x (6.58 + 0.0181 x Nappl) x (44/28) direct N2O emissions from
= 1.57 kg N2O / ha / yr N fertilizer application
= 415 kg CO2-eq / ha / yr (Gerber et al. 2016)
Nfert,indirect = Nappl x (0.1 x 0.01 + 0.3 x 0.0075) x (44/28) indirect N2O emissions
= 0.59 kg N2O / ha / yr from N fertilizer application
= 156 kg CO2-eq / ha / yr (IPCC 2006)
Nfert,total = Nfert,direct + Nfert,indirect total N2O emissions from N fertilizer application
= 0.57 Mg CO2-eq / ha / yr
GHGother,corn,bioethanol = 0.52 kg CO2-eq / kg fuel GHG emissions during crop cultivation,
= 0.52 Mg CO2-eq / Mg fuel excl. N fertilizer application, and biofuel refining (ecoinvent v3)
-1 (∆Cbiomass,x,i + ∆Csoil,x,i + ∆Nsoil,x,i) × LT + Nfert,x,i -1 Gx,i = (Falloc × + 〖GHGother,x,y)Ey Yx,i × BFx,y (253+44.94+12.48) ×〖30-1 +0.57 G =(F × + 0.52)29700-1 corn,10651 alloc 5.7 × 0.66
-1 Gcorn,10651 = (Falloc × 2.90+0.52) 29700 Mg CO2-eq / MJ
Using an allocation factor of 0.625 based on mass, this results in a GHG balance of 7.2∙10-2 kg CO2-eq / MJ. 122 Appendices
A4 | Ecoinvent background
table A1 | Overview of the processes and inputs included in the GHG emission calculations for corn and soybean cultivation in the ecoinvent database (Weidema et al. 2013).
process corn soybean geographical representation Fertilizing, by broadcaster X X Global Transport, tractor and trailer X Global Tillage, currying, by weeder X Global Tillage, chiselling X Global Tillage, ploughing X Global Tillage, harrowing, by spring tine X X Global harrow Application of protection X X Global product, by field sprayer Irrigation X USA Combine harvesting X X Global Sowing X X Global Drying of straw and whole-plant X Global
inputs Maize seed, for sowing X Global Organophosphorus-compound X Global Diphenylether-compound X Global Glyphosate X Global [Sulfonyl]urea-compound X Global Lime X X Global Benzoic-compound X Global Phenoxy-compound X Global Metolachlor X Global Dinitroaniline-compound X Global Pyrethroid-compound X Global Nitrile-compound X Global Bipyridylium-compound X Global Atrazine X Global Pesticide, unspecified X X Global Triazine-compound X Global Potassium chloride, as K2O X X Global Acetamide-anillide-compound X Global Phosphate fertilizer, as P2O5 X X Global Ammonium nitrate, as N X Global Ammonium sulfate, as N X Global Urea, as N X Global Nitrogen fertilizer, as N X Global Heat X Global Electricity X USA 123
table A2 | The top 5 most important GHG sources in the corn and soybean production process, based on ecoinvent v3 (Weidema et al. 2013). Emissions from nitrogen fertilizer application are excluded, as these are calculated separately.
corn soybean source % of total source % of total
1 Nitrogen fertilizer production 33.1 Harvesting 34.2 2 Drying of plant 32.0 Nitrogen fertilizer production 10.5 3 Irrigation 18.6 Phosphate fertilizer production 6.6 4 Seed production 9.0 Lime production 5.7 5 Tillage, ploughing 2.1 Fertilizing by broadcaster 5.7 124 Appendices
Appendix B
B1 | METHODS
B1.1 | Carbon stock of natural biomass
The carbon stocks of natural biomass were based on the IPCC Tier-1 Global Biomass Carbon Map for the Year 2000 by Ruesch and Gibbs (2008). This map provides default carbon densities of aboveground (based directly on IPCC data) and belowground (calculated using IPCC root:shoot ratios) biomass for a variety of vegetation types at a 1x1-km spatial resolution. Areas of cultivated land were manually removed from the maps and replaced by the natural vegetation type most suitable to the location, based on the type of natural vegetation type in the surrounding area.
B1.2 | Soil organic carbon, crop yield & fertilizer application
The soil organic carbon (SOC) stocks of the agricultural systems and natural systems were acquired using the Environmental Policy Integrated Climate (EPIC) model (Izaurralde et al. 2006), which simulates crop growth around the world. The main inputs required by the EPIC model are soil type information, topography data, weather statistics, land use information, crop type and crop management information (Schmid et al. 2007). For this study, we used global input data collected during the GEO-BENE project (Sheffield et al. 2006). Crop calendars with information on planting and harvesting dates were collected from a variety of sources including FAO, USDA, EC-JRC (MARS) and national agricultural institutes. The primary geographical reference for the global dataset was a global grid created with a 0.08° resolution (i.e. about 10x10 km at the equator). The daily climate data were generated from the Princeton Global Meteorological Forcing Dataset for land surface modeling which provides global meteorological data at 1° spatial resolution and a daily temporal resolution, for the years 1948-2008 (Sheffield et al. 2006). Subsequently, intersections of homogenous response units with the weather data grid cells, and administrative country level delineations, resulted in 212,707 individual landscape units with homogenous topography, soil and weather conditions, known as Simulation Units (SIMUs). Within each SIMU, the SOC stocks (in the top 30 cm of soil), crop yields, and amount of nitrogen fertilizer applied were simulated for a period of 10 years (1991-2000), and the average values during this period were selected for the calculations in the present study. The amount of nitrogen applied to the crops in each location was assumed to be the highest quantity of fertilizer that contributed to a yield increase (max. 200 kg N∙ha-1∙yr-1). 125
B1.3 | N2O emissions from soil mineralization
The amount of N2O emitted from the top 30 cm of soil as the result of mineralization following land transformation from natural vegetation to cropland was directly related to the loss of SOC upon land transformation, using the equation from Flynn et al. (Flynn et al. 2012):
MN2O (B1) NSOM = ∆Csoil × C : N × EF × . MN Taking into account a 15:1 C:N ratio, a 1% emission factor for the proportion of nitrogen lost as N2O-N, and molecule masses to convert the amount of nitrogen emitted to the amount of N2O(IPCC 2006), this results in:
(B2) NSOM = ∆Csoil × 1/15 × 0.01 × 44/28.
B1.4 | Life-cycle GHG emissions of biofuel production (excluding nitrogen fertilization)
For the life-cycle GHG emissions during biofuel production, we used seed-to-gate emission data from ecoinvent v3 as a starting point. Ecoinvent is one of the most extensive life cycle inventory databases currently available in the field of agriculture (Weidema et al. 2013). It includes processes such as fertilization, irrigation, tillage, seed production, pesticide application and transportation. Also, ecoinvent covers the background and foreground GHG emissions of these processes consistently for different crops grown in different countries. Emissions of fossil CO2, N2O and CH4 were summed up based on their respective
100-year time frame global warming potentials, where N2O equals 265 CO2-equivalents and CH4 equals 30 CO2-equivalents (IPCC 2013).
The life-cycle GHG emissions were based on data from ecoinvent’s rest of the world category, which adapts a farming or refining process from a specific country or selection of countries to a more general, global situation. An overview of the origin of the available data can be found in Table B1. For the no input farming management scenario, irrigation and fertilization activities were manually removed from the farming process. For the high input management scenario, only the processes related to nitrogen fertilization were removed, since nitrogen emissions were recalculated based on the spatially explicit fertilizer application from the EPIC model. Also, as the farming-related processes in ecoinvent correspond to a certain average crop yield in a country or set of countries, we corrected the GHG emissions using the grid-specific crop yields from EPIC.
Emissions from the combustion of the studied biofuels were excluded from the calculations, because the continuous cycle of carbon sequestration by the feedstocks during growth and the emission of an equal amount of carbon (as carbon dioxide) during combustion can be assumed to be climate neutral. This assumption of climate neutrality has been a recent topic of discussion (Searchinger et al. 2009, Cherubini et al. 126 Appendices
2011, Haberl 2013) but is still considered valid for annual crops due to their short rotation time(Cherubini et al. 2011). While sugarcane is a perennial rather than an annual crop, the aboveground biomass (i.e. ~85% of total biomass; Smith et al. 2005) is also harvested annually. Therefore, we chose to use the same approach for all crops.
table B1 | Origin of available farm emission data for each of the crop-management type combinations, and for the biofuel refining process. The global GHG emissions data used in the present study are based on data from one particular country or region, adapted to a global situation (e.g. adding irrigation with a global water mix) within ecoinvent. The original data originated from Brazil (BR); Switzerland (CH); Germany (DE); Spain (ES); Europe (EU); France (FR); North America (NA); and the United States (US).
emissions from corn rapeseed soybean sugarcane winter wheat farming US DE, FR, US BR, US BR DE, ES, FR, US refining US EU NA BR1 CH1
1 No global data were available for the refining of sugarcane, so Brazilian data were used directly. 2 Emissions from rye refining were used as proxy for the refining of winter wheat into bioethanol.
B1.5 | N2O emissions from fertilizer application
The amount of N2O emitted following fertilizer application was split up in direct and -1 -1 indirect emissions. The direct emissions (Nfert,direct, kg N2O ha yr ) were calculated using a nonlinear relationship between the amount of nitrogen fertilizer applied and the amount
of N2O emitted due to soil mineralization, as developed by Shcherbak et al. (2014):
MN2O (B3) Nfert,direct = Nappl ×(EF0+∆EF × Nappl) × , MN
-1 -1 where Nappl is the average amount of fertilizer applied in each location (kg N ha yr ) as
calculated by EPIC. For non-N fixing crops, the amount of N2O directly emitted can be calculated using:
(B4) Nfert,direct = Nappl ×(6.58+0.0181 × Nappl)×44/28.
To calculate the amount of N2O indirectly emitted from fertilizer application (Nfert,indirect, kg -1 -1 N2O ha yr ), the average amount of fertilizer applied in each location (Nappl) was multiplied with IPCC default emission factors and fractions(IPCC 2006):
MN2O (B5) Nfert,indirect = Nappl ×(Fv × EFv + Fl × EFl) × , MN
-1 where Fv is the fraction of fertilizer nitrogen that volatilizes as NH3 and NOx (kg·kg applied ), 127
-1 EFv is the emission factor of N2O from NH3 and NOx (kg·kg volatilized ), Fl is the fraction -1 of nitrogen losses by leaching or runoff (kg·kg applied ) and EFl is the emission factor of -1 N2O from the fraction lost by leaching or runoff (kg·kg lost ). Using the default emission factors from the IPCC, this results in:
(B6) Nfert,indirect = Nappl ×(0.1×0.01+0.3×0.0075)×44/28.
The N2O emissions per hectare per year were multiplied by the crop yield, biofuel conversion factor and biofuel energy content to calculate the N2O emissions per MJ of bioenergy produced.
B1.6 | Biofuel type, energy content, and conversion efficiency
The efficiency of crop to biofuel conversion, and the energy content of the two types of biofuels included in the analysis (i.e. bioethanol and biodiesel) were taken from ecoinvent (see Table B2). Petrol and diesel were selected as the fossil counterparts of bioethanol and biodiesel, respectively.
Since the biofuel production process in ecoinvent uses the fresh matter weights of the selected crops, dry matter yields from EPIC were converted to fresh matter yields. We assumed moisture contents of 71.4% in sugarcane, 15% in winter wheat, 14% in corn, 12% in soybean, and 6% in rapeseed, similar to ecoinvent. table B2 | Biofuel type, conversion factor and energy conte
crop type x biofuel type conversion factor BFx energy content Ex (kg fuel∙kg crop-1) (MJ∙kg fuel-1) corn bioethanol grain to ethanol: 0.31 29.7 rapeseed biodiesel seed to oil: 0.52 40.5 oil to diesel: 1.12 soybean biodiesel bean to oil: 0.55 40.5 oil to diesel: 1.05 sugarcane bioethanol stalk to ethanol: 0.07 29.7 winter wheat1 bioethanol grain to ethanol: 0.30 29.7 1 Conversion factor for rye is used as proxy for the production of bioethanol from winter wheat.
B1.7 | Life-cycle GHG emissions of fossil-fuel production
The life-cycle GHG emissions during the production of petrol and diesel were also collected from the ecoinvent v3 database. This life cycle includes the mining and refining of the fossil fuel, the upstream processes related to the energy and materials required during 128 Appendices
mining and refining, as well as the actual combustion of the fuel. We assumed energy contents of 45.4 MJ∙kg fossil diesel-1 and 45.1 MJ∙kg fossil petrol-1 and a C-content of both fuels set to 86.5%, based on ecoinvent documentation (Jungbluth 2007). As a result, we -2 -2 found a total emission of 8.2∙10 kg CO2-eq/MJ for diesel and 8.7∙10 kg CO2-eq/MJ for petrol of which 87% was emitted during combustion. The remainder was emitted during mining and refining of the crude oil.
B1.8 | Crop yield thresholds
Crop cultivation in a particular grid cell was assumed to be economically viable only when the yield in that location (as modeled by EPIC) exceeded the lowest actual country yield reported by the FAO (2012 data). Grids with a specific crop yield lower than this threshold were excluded from the PBTGHG calculations for that crop. Table B3 shows the thresholds and the countries for which these yields were reported.
table B3 | The thresholds for crop cultivation modeling based on the lowest actual country yield as reported by the FAO, and the percentages of grids that are excluded from analysis due to modeled crop yields below the thresholds.
crop yield threshold (Mg product ha-1 yr-1) country corn 0.133 Botswana rapeseed 0.095 Tajikistan soybean 0.286 Tajikistan sugarcane 0.903 American Samoa winter wheat 0.286 Venezuela
B1.9 | Allocation
Three allocation methods were used to address the by-products of the biofuel production systems, i.e. based on (1) energy content, (2) mass, and (3) market value. The allocation factors for the three allocation methods can be found in Table B4. We assumed that generic allocation factors were valid worldwide because accounting for regional differences in fuel and electricity prices was beyond the scope of the present study. The results using energy- based allocation can be found in the main text, and the results using mass- and value- based allocation can be found below in Figures B1 to B4. 129
table B4 | Allocation factors for the most important by-products of the biofuel production systems. The selection of by-products related to the crop-to-biofuel processing phase was based on Wang et al. (2011) The energy-based allocation factors are based on the lower heating value of the by- products. feedstock phase by-product(s) uses allocation to by-product(s) (%) source energy mass market cont. value corn cultivation stover animal feed 38 50 12 (Luo et al. 2009) corn processing DDGS animal feed 38 46 23 (Wang et al. 2011) rapeseed cultivation straw animal feed 42 57 3 (Bernesson 2004) rapeseed processing meal + glycerin animal feed + 36 58 26 (Bernesson 2004) chemical soybean processing meal + glycerin animal feed + 67 82 57 (Wang et al. 2011) chemical sugarcane1 processing bagasse electricity 49 28 7 (Renó et al. 2011, production Renouf et al. 2011) wheat cultivation straw animal feed 42 44 10 (Börjesson & Tufvesson 2011) wheat processing DDGS animal feed 40 45 19 (Börjesson & Tufvesson 2011)
1 The mass-based and energy-based allocation factors for sugarcane relate to the mass and lower heating value of the bagasse. The market value-based allocation factor relates to the price of the electricity produced from the bagasse.
B1.10 | Calculation example
We here provide an example of a greenhouse gas payback time (PBTGHG) calculation, allocating emissions amongst the biofuel and by-products based on their energy contents. For this example, we selected bioethanol production from corn cultivated under no input management in central USA (SIMU 79540), where the reference vegetation is rangeland.
figure B1 | Location of SIMU 79540 within the IPCC biomass carbon map(Ruesch & Gibbs 2008). 130 Appendices
Csoil,corn,79540,noinput = 54.32 Mg C / ha cropland SOC (EPIC)
Csoil,range,79540 = 59.97 Mg C / ha reference SOC (EPIC)
Ycorn,79540,noinput = 3.50 Mg grain / ha / yr crop yield (EPIC)
Nappl,corn,79540,noinput = 0 kg N / ha / yr N fertilizer input (EPIC; no input)
Cbiomass,range,79540 = 6 Mg C / ha reference biomass (Ruesch and Gibbs 2008)
-5 Mcorn,cultivA = 1.80 x 10 Mg CO2-eq / MJ fossil GHG emissions during crop cultivation, excl. N fertilizer (ecoinvent v3; yield = 9.32 Mg/ha)
-6 Mcorn,cultivB = 6.19 x 10 Mg CO2-eq / MJ fossil GHG emissions during crop cultivation, excl. N fertilizer (corrected for yield = 3.50 Mg/ha)
-5 Mcorn,proc = 2.02 x 10 Mg CO2-eq / MJ fossil GHG emissions during crop to biofuel processing (ecoinvent v3)
Mcorn,total = Mcorn,cultivB + Mcorn,proc total fossil GHG emissions during biofuel -5 = 2.64 x 10 Mg CO2-eq / MJ production, excl. N fertilizer
Nfert,direct = 0.001 x Nappl x (6.58 + 0.0181 x Nappl) x (44/28) direct N2O emissions from
= 0 Mg N2O / ha / yr N fertilizer application (Shcherbak et al. 2014)
= 0 Mg CO2-eq / ha / yr
Nfert,indirect = Nappl x (0.1 x 0.01 + 0.3 x 0.0075) x (44/28) indirect N2O emissions
= 0 Mg N2O / ha / yr from N fertilizer application (IPCC 2006)
= 0 Mg CO2-eq / ha / yr
Mcorn bioeth = Nfert,direct + Nfert,indirect + Mcorn,total total fossil GHG emissions during biofuel -5 = 2.64 x 10 Mg CO2-eq / MJ production, incl. N fertilizer
-5 Mpetrol = 8.75 x 10 Mg CO2-eq / MJ total fossil GHG emissions during fossil fuel production and combustion (ecoinvent v3)
BFcorn,US = 0.66 kg fuel / kg crop crop to fuel biofuel conversion efficiency (ecoinvent v3)
Ebioethanol = 29.7 MJ / kg fuel biofuel energy content (ecoinvent v3) 131
∆Csoil = Csoil,range - Csoil,corn CO2 emissions from SOC loss = 5.65 Mg C / ha
= 20.72 Mg CO2-eq / ha
NSOM = ΔCsoil x (1/15) x 0.01 x (44/28) N2O emissions from soil mineralization
= 0.006 Mg N2O / ha
= 1.54 Mg CO2-eq / ha
∆GHGsoil = ΔCsoil + NSOM total GHG emissions from soil
= 22.26 Mg CO2-eq / ha
∆GHGbiomass = Cbiomass,range - Cbiomass,corn CO2 emissions from biomass loss = 6 Mg C / ha
= 22 Mg CO2-eq / ha
Production of corn-based bioethanol produces two major by-products, namely stover and dry distillers grain with solubles (DDGS), which are both used as animal feed. Stover is a by-product during cultivation of the corn, and its energy content is 38% of the total energy content of corn grain and stover (Luo et al. 2009). DDGS is a by-product during the processing of corn grain into bioethanol, and its energy content is also 38% of the total energy content of the bioethanol and DDGS (Wang et al. 2011). Therefore, the overall share of the bioethanol in the total energy content of the three products is 62% x 62% = 38%. Hence, we allocate only 38% of the GHG emissions that result from land transformation
(i.e., ∆GHGsoil and ∆GHGbiomass) to the bioethanol.
alloc × (∆〖GHGsoil,x,i,j + ∆GHGbiomass,x,i,j) PBTGHG,x,i,j =
(M - M ) × Y × BF × E PBT fossil = bio x,i,j x x GHG,corn,79540,noinput (8.75 × 10-5 - 2.64 × 10-5) × 3500 × 0.66 × 29.7 = 4.12 yr 0.38 × (22.29+ 22) 132 Appendices
B2 | RESULTS
B2.1 | Mass-based allocation
A
B
C
D
GHG payback times (years)
A 0 0 0 0 0 0 0 / < 1 2 5 0 0 0 N - - - 1 5 5 0 0 0 - - > 1 2 0 0 5 0 1 133
E
F
G
H
GHG payback times (years)
A 0 0 0 0 0 0 0 / < 1 2 5 0 0 0 N - - - 1 5 5 0 0 0 - - > 1 2 0 0 5 0 1 134 Appendices
I
J
GHG payback times (years)
A 0 0 0 0 0 0 0 / < 1 2 5 0 0 0 N - - - 1 5 5 0 0 0 - - > 1 2 0 0 5 0 1
figure B2 | Global maps of PBTGHGs derived for the five bioenergy crops under no input and high input farm management, allocating the emissions amongst the biofuel and its by-products based on their mass ratio.
Corn – no input A
Corn – high input
B 135
Rapeseed – no input C
Rapeseed – high input D
Soybean – no input E
Soybean – high input F 136 Appendices
Sugarcane – no input
G
Sugarcane – high input
H
Winter wheat – no input I
Winter wheat – high input J
figure B3 | Histograms of the PBTGHGs derived for the five energy crops under no input and high input farm management, allocating the emissions amongst the biofuel and its by-products based on their mass ratio. The different colors reflect the two main classes of natural vegetation that were replaced by agricultural land: (1) forests and (2) rangelands, based on the classification by Ruesch and Gibbs (2008). The dashed lines represent different cropland production periods that may be assumed, which affect the number of grids where biofuel production is beneficial over the use of fossil fuels in terms of GHG emissions (given in percentages). The low yield bar shows the percentage of grids for
which no PBTGHGs were calculated because the modeled yield was less than the threshold value. 137
B2.2 | Market value-based allocat
A
B
C
D
GHG payback times (years)
A 0 0 0 0 0 0 0 / < 1 2 5 0 0 0 N - - - 1 5 5 0 0 0 - - > 1 2 0 0 5 0 1 138 Appendices
E
F
G
H
GHG payback times (years)
A 0 0 0 0 0 0 0 / < 1 2 5 0 0 0 N - - - 1 5 5 0 0 0 - - > 1 2 0 0 5 0 1 139
I
J
GHG payback times (years)
A 0 0 0 0 0 0 0 / < 1 2 5 0 0 0 N - - - 1 5 5 0 0 0 - - > 1 2 0 0 5 0 1
figure B4 | Global maps of PBTGHGs derived for the five bioenergy crops under no input and high input farm management, allocating the emissions amongst the biofuel and its by- products based on their economic value. Corn – no input
A
B Corn – high input 140 Appendices
Rapeseed – no input
C
D Rapeseed – high input
E Soybean – no input
F Soybean – high input 141
Sugarcane – no input
G
H Sugarcane – high input
Winter wheat – no input I
Winter wheat – high input J
figure B5 | Histograms of the PBTGHGs derived for the five energy crops under no input and high input farm management, allocating the emissions amongst the biofuel and its by-products based on their economic value. The different colors reflect the two main classes of natural vegetation that were replaced by agricultural land: (1) forests and (2) rangelands, based on the classification by Ruesch and Gibbs (2008). The dashed lines represent different cropland production periods that may be assumed, which affect the number of grids where biofuel production is beneficial over the use of fossil fuels in terms of GHG emissions (given in percentages). The low yield bar shows the percentage of grids for which no PBTGHGs were calculated because the modeled yield was less than the threshold value. 142 Appendices
B2.3 | Statistical analysis
An ANOVA was performed to derive the sum of square (SSQ) for the factors crop type and management regime, and for the residual which represents spatial variability. The SSQ of both factors and the residual was divided by the total SSQ of the model in order to assess the variance attributable to these factors and residual. The outcomes are given in Table S5.
table B5 | Variance in PBTGHGs explained by differences in crop type, management type and geographical location. Explained variance (EV) is calculated by dividing the sum of square of each factor (SSQ) by the total SSQ of the model. The variance not explained by the model (i.e. the residual) is attributable to differences amongst locations.
SSQ EV crop type 2.5 x 108 2.5% management strategy 6.3 x 108 6.5% crop-management interaction 3.3 x 107 0.3% residual (location) 8.9 x 109 90.7% total 9.8 x 109
143
Appendix C
C1 | Material and methods
C1.1 | Background to methodology
SOC of natural grassland The SOC stock in each location was calculated as a function of soil carbon concentration, bulk density, and depth, as proposed by Guo and Gifford (2002). The required soil parameters were collected from the Harmonized World Soil Database (FAO et al. 2012). Subsequently, the resulting global SOC map was overlaid with the GLC2000 land cover map (Bartholome & Belward 2005) in order to exclude areas that are not natural grasslands. To this purpose, the following land covers were classified as natural grasslands: (1) evergreen shrub cover, (2) deciduous shrub cover, (3) herbaceous cover, (4) sparse herbaceous or sparse shrub cover, and (5) regularly flooded shrub and/or herbaceous cover. Finally, the average natural grassland SOC was calculated for each of the AEZs.
N2O emissions from fertilizer application Grid-specific application rates of nitrogen fertilizer were collected at a 5-arcmin resolution from the Environmental Policy Integrated Climate (EPIC) crop growth simulation model (Williams 1995, Izaurralde et al. 2006). The amount of N2O emitted following this fertilizer application was split up in direct emissions (i.e. N2O emitted directly during fertilizer application) and indirect emissions (i.e. N2O emitted through volatilization and re-deposition of NH3 and NOx, and through leaching and runoff of N during fertilizer application). The direct emissions were calculated using a nonlinear response relationship between the amount of fertilizer applied (i.e. data from EPIC) and the amount of N2O emitted due to soil mineralization, as developed by Shcherbak et al. (2014). The indirect emissions were calculated using the default emission factors and fraction proposed by the IPCC (2006).
Fossil GHG emissions
The ecoinvent database (Weidema et al. 2013) provided average emissions of fossil CO2, fossil CH4, and N2O per kilogram of biofuel or fossil fuel produced. These emissions were converted to CO2 equivalents per MJ of (bio)energy, using a default energy content of 29.7 MJ kg bioethanol-1, 40.5 MJ kg biodiesel-1, 45.1 MJ kg petrol-1 and 45.4 MJ kg diesel-1. 144 Appendices
C1.2 | Biofuel conversion factors and energy contents
table C1 | Biofuel type, conversion factor and energy content of the crop-based biofuels, as collected from the ecoinvent database(Weidema et al. 2013). The conversion factor (BF) represents the refining process efficiency, showing the amount of biofuel that is produced per unit of crop. In the case of biodiesel production, we distinguish between conversion of seed/bean into vegetable oil, and conversion of vegetable oil into biodiesel. These factors and energy densities are used to calculate the GHG payback times conform equation 1 of the main text.
1 crop type x biofuel type conversion factor BFx energy density Ex (kg fuel∙kg crop-1) (MJ∙kg fuel-1) corn bioethanol grain to ethanol: 0.31 29.7 rapeseed biodiesel seed to oil: 0.52 40.5 oil to diesel: 1.12 soybean biodiesel bean to oil: 0.55 40.5 oil to diesel: 1.05 sugarcane bioethanol stalk to ethanol: 0.07 29.7
1 Conversion factors are larger than 1 when less than 1 litre of oil is required to produce 1 litre of biofuel, due to the addition of other substances such as methanol.
C1.3 | Global average GPBT calculations
Global averages were calculated based only on the nine biofuel producing countries. However, the crops produced in these countries are not necessarily used as biofuel feedstock in the same rates. Therefore, we corrected the production quantities of the nine selected producing countries using the fraction of crop produced in that country that is actually used as a biofuel feedstock, based on data from recent years (2006-2015). These national crop-to-biofuel fractions and their corresponding references can be found in Table C2. We assumed that the fractions were valid for all farm management strategies, i.e. that farms under different management strategies contribute an equal percentage of their crop production to the total national biofuel feedstock supply. 145
table C2 | The biofuel producing countries considered in the present study, along with their most important feedstocks, the amount of feedstock crop they produce, the fraction of crop produce that is actually used for biofuel production in these countries, and their respective shares of the total biofuel production. 2 share of total total of share (%) production biofuel 49 1 50 14 <1 10 5 9 <1 1 3 58 year + source of fraction of + source year 2010/2011; Koizumi (2014) Koizumi 2010/2011; 2007; Qiu et al. (2010) 2007; ERS (2015) USDA 2014/2015; et al. (2013) Herrera 2011; Sinabell and Schmid (2008) 2010; Website & Corn Soybean 2010; 2006; Guindé et al. (2008) (2013) and Bührke Wengenmayr 2013; GAIN (2015) GAIN (2011), 2011/2012; GAIN (2015) 2015; (2012) Economics Informa 2011/2012; crop-to-biofuel crop-to-biofuel fraction 0.50 0.02 0.79 0.14 0.41 0.07 0.48 0.67 0.633 0.35 0.61 0.25 1 crop (kg) production 4.4E+8 1.4E+8 2.8E+8 3.7E+7 1.2E+5 5.1E+7 4.2E+6 5.2E+6 5.8E+3 5.4E+5 1.6E+6 8.5E+7 feedstock sugarcane corn corn soybean rapeseed soybean rapeseed rapeseed (4/7)rapeseed (3/7)soybean rapeseed soybean each country’s biofuel production was calculated as the crop production multiplied by the crop-to-biofuel fraction. Given here is the share of each of is the share here Given fraction. the crop-to-biofuel multiplied by production as the crop calculated was production each country’s biofuel all included countries; of production biofuel the total to production country’s biofuel biofuel / biofuel country bioethanol Brazil China USA biodiesel Argentina Austria Brazil France Germany Italy Poland USA European average, as data specific for Italy were not found. not were for Italy specific as data average, European collected from SPAM; from collected 1 2 3 146 Appendices
C1.4 | Allocation factors
table C3 | Allocation factors for the most important by-products of the four biofuel production systems, based on the market value of the main products and by-products. We assumed that the ratio between main product and by-products was not influenced by farm management, so the same factors were used for all three management strategies.
feedstock phase1 by-product(s) use(s) allocation source factor (%)2 corn cultivation stover animal feed 12 (Luo et al. 2009) corn processing DDGS animal feed 23 (Wang et al. 2011) rapeseed cultivation straw animal feed 3 (Bernesson 2004) rapeseed processing meal + glycerine animal feed + 26 (Bernesson 2004) chemical soybean processing meal + glycerine animal feed + 57 (Wang et al. 2011) chemical sugarcane processing bagasse electricity 7 (Renó et al. 2011, production Renouf et al. 2011)
1 Cultivation refers to by-products that are produced on the field and gathered during harvest; Processing refers to by-products that are produced during crop-to-biofuel processing. 2 Factors show the percentage of emissions that are allocated to the by-product(s).
C2 | Additional analysis
C2.1 | Expected crop yields for 2050
When adopting expected crop yields for 2050 reported by Jaggard et al. (2010), German rapeseed remains the best option, while Brazilian sugarcane has the fewest grid cells with GPBTs below 30 years (Table C4). If 50% or 95% of the locations with GPBTs currently above 30 years within each country are to be reduced below that threshold, crop yields should theoretically increase four to over seventy times, respectively, provided that the GHG emissions related to crop cultivation do not increase (Table C4).
The present study showed that a substantial increase of yield is required in order to reach the 30 years boundary in 50% or 95% of the locations with GPBTs currently longer than 30 years. However, Jaggard et al. (2010) previously predicted crop yields in our countries of interest to increase by a maximum of only 38%, in 2050 compared to 2007. The number of locations with a GPBTs below 30 years is therefore likely to increase only slightly during the decades to come. 147
table C4 | Percentages of grid cells below 30 years by 2050 under expected yield changes, and yield increases required in order to achieve GPBTs below 30 years in 50% and 95% of the locations where GPBTs currently exceed 30 years, provided that the GHG emissions related to crop cultivation remain unchanged.
2 95% 13.5 14.0 10.7 9.0 4.4 73.0 5.8 4.9 19.5 20.3 7.8 change from current yield current from change (factor) 50% 2.0 1.7 2.4 2.2 1.4 4.6 2.9 1.4 3.0 3.3 1.4 95% 1.3E+01 2.3E+01 2.0E+01 1.1E+01 1.4E+01 9.0E+01 9.0E+00 1.0E+01 8.7E+02 5.1E+01 4.3E+01 ) -1 · yr -1 yield required yield required (Mg crop∙ha 50% 1.9E+00 2.7E+00 4.4E+00 2.6E+00 4.4E+00 5.7E+00 4.5E+00 2.9E+00 1.3E+02 8.4E+00 7.8E+00 GPBTs below 30 30 below GPBTs 2050 (%) by years 82% 71% 42% 77% 99% 72% 32% 83% 21% 47% 83% 1 yield increase by by yield increase 2050 (factor) 1.44 1.39 1.47 1.39 1.39 1.39 1.39 1.34 1.37 1.55 1.36 average yield in all locations with a GPBT currently longer than 30 years. currently with a GPBT yield in all locations average based on expected yield increases by 2050 as per Jaggard et al. (2010); 2050 as per Jaggard by yield increases based on expected 1 2 country - crop country biodiesel – soybean Argentina Austria – rapeseed – soybean Brazil - rapeseed France – rapeseed Germany Italy – rapeseed – rapeseed Poland – soybean USA bioethanol – sugarcane Brazil China – corn – corn USA 148 Appendices
Appendix D
D1 | Data collection process
1 A search key in Web of Science was used to find publications containing cropland species richness data and a first selection was made based on the titles and abstracts of the 2,591 journal hits.
2 250 publications were selected and checked for usefulness of data: – Was the type of crop cultivated specified? – Was survey data reported (either as total number of species, species richness per unit of area, or any other biodiversity index) at the species level? – Was the survey done during the growth period of the crop? – Was any reference data reported in the same publication?
3 Data on species richness, crop type, studied taxonomic group, sampling method, sampling effort, sampling period, location and farm management were combined in a data sheet. If coordinates of the location were not reported, these were identified with Google Earth based on the location description.
4 All data points were mapped on a global ecoregion map using ArcGIS 9.2 (ESRI). We determined the ecoregion and biome in which each data point lay and added this information to the data sheet.
5 Another search key in Web of Science based on the taxonomic group and location (i.e. ecoregion and country) was used to find reference land cover data and a selection was made based on the titles and abstracts.
6 The selected publications were checked for usefulness of data: – Was the type of natural landscape given and does this match the ecoregion description? – Were survey data reported for the same taxonomic group as the cropland data? – Were survey data reported in the same unit (i.e. total number of species, species richness per unit of area, or any other biodiversity index) and at the species level?
7 Data on species richness, vegetation type, studied taxonomic group, sampling method, sampling effort, sampling period and location were added to the data sheet.
8 For each combination of taxonomic group and ecoregion the sampling method and effort of the cropland and reference land cover studies were compared. If these were similar, a CF was calculated for each combination of categories. If data from multiple 149
studies were available, CFs based on cropland and reference data from the same publication were preferred. Multiple data points within a study were first aggregated by calculating the mean CF for this study.
9 The available CFs were aggregated by deriving medians based on (1) combinations of crop type (Panicoideae, Pooideae, oil palm, low crops), species group (mammals, birds, arthropods and vascular plants) and location (biome) and (2) aggregation of all data available within a single crop group, species group or biome category.
D2 | Frequency distributions of characterization factors
figure D1 | Frequency distributions of the original 152 characterization factors for birds, vascular plants, mammals and arthropods.
figure D2 | Frequency distributions of the original 152 characterization factors for the crop categories Pooideae, Panicoideae, low crops, and oil palm. 150 Appendices
figure D3 | Frequency distributions of the original 152 characterization factors for eight biomes.
D3 | Characterization factors, full list
table D1 | All characterization factors (CFs) per crop type, taxonomic group, biome and literature
reference. Species richness (Scrop and Sref) represents the (average) number of species in one field or the total number of species in multiple fields. Reference data were collected from the same study (SS), from a different study in the same ecoregion (ECOR), or from a different study in the same biome
(BIOME). CFs under CFplot are all 309 original CFs, often including multiple CFs per study for the same combination of crop type, taxonomic group, and biome. After averaging per study, a total of 152 CFs
remained (CFstudy). 151 ref Gillison et al. (2003) Gillison et al. (2003) Gillison et al. (2003) Gillison et al. (2003) Juha (2004); Niemelä et al. (1994) Juha (2004); Niemelä et al. (1994) Juha (2004); Niemelä et al. (1994) Flinn et al. (2008) Flinn et al. (2008) Flinn et al. (2008) Flinn et al. (2008) Koivula (2002) Koivula Koivula (2002) Koivula Koivula (2002) Koivula Koivula (2002) Koivula Koivula (2002) Koivula Koivula (2002) Koivula Soderstrom et al. (2001) Soderstrom Bergeron et al. (2012); Niemelä et al et al. (2012); Bergeron et al. (2006) (1992); Roughley Magura et al. (2008) Magura Topping & Lövei (1997) & Lövei Topping Huusela-Veistola & Vasarainen (2000) & Vasarainen Huusela-Veistola source S source - crop Gillison et al. (2003) Shrestha et al. (2010) Shrestha Shrestha et al. (2010) Shrestha Gillison et al. (2003) Hyvönen & Salonen (2002) Hyvönen & Salonen (2002) Hyvönen & Salonen (2002) Légère et al. (2005) Légère Légère et al. (2005) Légère Légère et al. (2005) Légère Légère et al. (2005) Légère Kinnunen et al. (2001) Kinnunen et al. (2001) Kinnunen et al. (2001) Kinnunen et al. (2001) Kinnunen et al. (2001) Kinnunen & Tiainen (1999) Eggers et al. (2011) Eggers Carcamo et al. (1995) Carcamo Kovács-Hostyánszki et al. Kovács-Hostyánszki (2011) Huusela-Veistola & Vasara Huusela-Veistola Vink et al. (2004) source S source inen (2000) ref 102 102 102 34 61.4 61.4 61.4 280 280 280 280 23 17.5 17.5 17.5 17.5 23 57 43 28 25 11 S crop 15 2 15 1 25 30 32 28 37 32 37 50 28 23 36 28 53 58 48 28 36 6 S SS BIOME BIOME SS ECOR ECOR ECOR ECOR ECOR ECOR ECOR ECOR ECOR ECOR ECOR ECOR ECOR ECOR ECOR ECOR SS ECOR ref. based on ref. study 0.85 0.92 0.97 0.53 0.88 -0.75 -1.30 -0.02 -0.12 0.00 -0.44 0.45 CF plot 0.85 0.98 0.85 0.97 0.59 0.51 0.48 0.90 0.87 0.89 0.87 -1.17 -0.60 -0.31 -1.06 -0.60 -1.30 -0.02 -0.12 0.00 -0.44 CF 0.45 tropical moist broadleaf forests moist broadleaf tropical tropical moist broadleaf forests moist broadleaf tropical tropical moist broadleaf forests moist broadleaf tropical tropical moist broadleaf forests moist broadleaf tropical boreal forests/taiga boreal boreal forests/taiga boreal boreal forests/taiga boreal temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate boreal forests/taiga boreal boreal forests/taiga boreal boreal forests/taiga boreal boreal forests/taiga boreal boreal forests/taiga boreal boreal forests/taiga boreal temperate broadleaf forests broadleaf temperate temperate grasslands temperate temperate broadleaf forests broadleaf temperate boreal forests/taiga boreal biome grasslands temperate - above-ground leaf above-ground weeds weeds weeds termites weeds weeds weeds weeds weeds weeds weeds ground beetles ground ground beetles ground ground beetles ground ground beetles ground ground beetles ground ground beetles ground birds ground beetles ground birds hoppers taxonomic group taxonomic spiders above-ground cereal cassava cassava cassava cassava barley barley barley barley barley barley barley barley barley barley barley barley barley cereal barley cereal crop type crop barley 152 Appendices Ziesche & Roth (2008) Ziesche & Roth Ziesche & Roth (2008) Ziesche & Roth Ziesche & Roth (2008) Ziesche & Roth Ziesche & Roth (2008) Ziesche & Roth Ziesche & Roth (2008) Ziesche & Roth Ziesche & Roth (2008) Ziesche & Roth Ziesche & Roth (2008) Ziesche & Roth Ziesche & Roth (2008) Ziesche & Roth Ziesche & Roth (2008) Ziesche & Roth Ziesche & Roth (2008) Ziesche & Roth Dennis et al. (1997) Arroyo & Iturrondobeitia (2006) & Iturrondobeitia Arroyo Ziesche & Roth (2008) Ziesche & Roth Koivula (2002) Koivula Koivula (2002) Koivula Seric Jelaska et al. (2010) Ouchtati et al. (2012) Ouchtati et al. (2012) Ouchtati et al. (2012) Ouchtati et al. (2012) Ouchtati et al. (2012) Ouchtati et al. (2012) Ouchtati et al. (2012) Ouchtati et al. (2012) Ouchtati et al. (2012) Feest et al. (2011) Feest Feest et al. (2011) Feest Ratschker & Roth (2000) & Roth Ratschker Ratschker & Roth (2000) & Roth Ratschker Ratschker & Roth (2000) & Roth Ratschker Ratschker & Roth (2000) & Roth Ratschker Ratschker & Roth (2000) & Roth Ratschker Ratschker & Roth (2000) & Roth Ratschker Ratschker & Roth (2000) & Roth Ratschker Ratschker & Roth (2000) & Roth Ratschker Ratschker & Roth (2000) & Roth Ratschker Ratschker & Roth (2000) & Roth Ratschker Good & Giller (1991) Arroyo & Iturrondobeitia & Iturrondobeitia Arroyo (2006) Samu & Szinetár (2002) Ekroos et al. (2010) Ekroos Ekroos et al. (2010) Ekroos Batáry et al. (2008) Ouchtati et al. (2012) Errouissi et al. (2011) Errouissi Errouissi et al. (2011) Errouissi Errouissi et al. (2011) Errouissi Errouissi et al. (2011) Errouissi Errouissi et al. (2011) Errouissi Errouissi et al. (2011) Errouissi Errouissi et al. (2011) Errouissi Errouissi et al. (2011) Errouissi Rundlöf et al. (2008) Rundlöf et al. (2008) 34.3 34.3 34.3 34.3 34.3 34.3 34.3 34.3 34.3 34.3 15 82 34.3 16.6 16.6 43 32.5 32.5 32.5 32.5 32.5 32.5 32.5 32.5 32.5 25 25 22 24 25 32 30 28 33 29 28 28 17 26 30 15.7 16.6 75 22 1 1 2 4 3 3 2 4 22 28 BIOME BIOME BIOME BIOME BIOME BIOME BIOME BIOME BIOME BIOME ECOR SS BIOME ECOR ECOR ECOR SS ECOR ECOR ECOR ECOR ECOR ECOR ECOR ECOR BIOME BIOME 0.22 -0.13 0.68 0.12 0.03 -0.74 0.32 0.92 0.00 0.36 0.30 0.27 0.07 0.12 0.18 0.04 0.15 0.18 0.18 -0.13 0.68 0.12 0.05 0.00 -0.74 0.32 0.97 0.97 0.94 0.88 0.91 0.91 0.94 0.88 0.12 -0.12 temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate Mediterranean forests Mediterranean temperate broadleaf forests broadleaf temperate boreal forests/taiga boreal boreal forests/taiga boreal temperate broadleaf forests broadleaf temperate Mediterranean forests Mediterranean Mediterranean forests Mediterranean Mediterranean forests Mediterranean Mediterranean forests Mediterranean Mediterranean forests Mediterranean Mediterranean forests Mediterranean Mediterranean forests Mediterranean Mediterranean forests Mediterranean Mediterranean forests Mediterranean temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate ground-dwelling spiders ground-dwelling ground-dwelling spiders ground-dwelling ground-dwelling spiders ground-dwelling ground-dwelling spiders ground-dwelling ground-dwelling spiders ground-dwelling ground-dwelling spiders ground-dwelling ground-dwelling spiders ground-dwelling ground-dwelling spiders ground-dwelling ground-dwelling spiders ground-dwelling ground-dwelling spiders ground-dwelling ground-dwelling rove rove ground-dwelling beetles ground-dwelling beetle ground-dwelling mites ground-dwelling & ground-dwelling spiders above-ground ground beetles ground ground beetles ground ground beetles ground ground beetles ground ground beetles ground ground beetles ground ground beetles ground ground beetles ground ground beetles ground ground beetles ground ground beetles ground ground beetles ground butterflies butterflies cereal cereal cereal cereal cereal cereal cereal cereal cereal cereal cereal cereal cereal cereal cereal cereal cereal cereal cereal cereal cereal cereal cereal cereal cereal cereal cereal 153 Huntly et al. (2005); Bangert & Huntly (2010) Juha (2004); Niemelä et al. (1994) Juha (2004); Niemelä et al. (1994) Juha (2004); Niemelä et al. (1994) Juha (2004); Niemelä et al. (1994) Juha (2004); Niemelä et al. (1994) Juha (2004); Niemelä et al. (1994) Juha (2004); Niemelä et al. (1994) Juha (2004); Niemelä et al. (1994) Juha (2004); Niemelä et al. (1994) Juha (2004); Niemelä et al. (1994) Juha (2004); Niemelä et al. (1994) Juha (2004); Niemelä et al. (1994) Juha (2004); Niemelä et al. (1994) Juha (2004); Niemelä et al. (1994) Juha (2004); Niemelä et al. (1994) Juha (2004); Niemelä et al. (1994) Soderstrom et al. (2001) Soderstrom Soderstrom et al. (2001) Soderstrom Borchsenius et al. (2004); Graae & et al. (2004); Graae Borchsenius Heskjaer (1997); Thomsen et al. (2005) Heroldova et al. (2007) Heroldova Ziesche & Roth (2008) Ziesche & Roth Ziesche & Roth (2008) Ziesche & Roth Ziesche & Roth (2008) Ziesche & Roth Ziesche & Roth (2008) Ziesche & Roth Ziesche & Roth (2008) Ziesche & Roth Ziesche & Roth (2008) Ziesche & Roth Young & Thorne (2004) Young Hyvönen et al. (2003) Hyvönen et al. (2003) Salonen et al. (2001) Salonen et al. (2001) Salonen et al. (2001) Salonen et al. (2001) Salonen et al. (2001) Salonen et al. (2001) Salonen et al. (2001) Salonen et al. (2001) Salonen et al. (2001) Salonen et al. (2001) Salonen et al. (2001) Salonen et al. (2001) Salonen et al. (2001) Salonen et al. (2001) Salonen et al. (2001) Salonen et al. (2001) Hald (1999) Heroldova et al. (2007) Heroldova Samu & Szinetár (2002) Samu & Szinetár (2002) Samu & Szinetár (2002) Ratschker & Roth (2000) & Roth Ratschker Ratschker & Roth (2000) & Roth Ratschker Ratschker & Roth (2000) & Roth Ratschker 67.7 61.4 61.4 61.4 61.4 61.4 61.4 61.4 61.4 61.4 61.4 61.4 61.4 61.4 61.4 61.4 61.4 341 341 100.7 10 34.3 34.3 34.3 34.3 34.3 34.3 16 64 75 78 62 63 68 47 64 58 46 94 89 73 94 60 51 89 45 114 10 36 53 18 23 24 23 ECOR ECOR ECOR ECOR ECOR ECOR ECOR ECOR ECOR ECOR ECOR ECOR ECOR ECOR ECOR ECOR ECOR ECOR ECOR ECOR SS BIOME BIOME BIOME BIOME BIOME BIOME 0.76 -0.13 -0.10 0.80 -0.13 0.00 -0.04 0.76 -0.04 -0.22 -0.27 -0.01 -0.03 -0.11 0.23 -0.04 0.06 0.25 -0.53 -0.45 -0.19 -0.53 0.02 0.17 0.74 0.87 -0.13 0.00 -0.05 -0.55 0.47 0.33 0.30 0.33 deserts shrublands and xeric boreal forests/taiga boreal boreal forests/taiga boreal boreal forests/taiga boreal boreal forests/taiga boreal boreal forests/taiga boreal boreal forests/taiga boreal boreal forests/taiga boreal boreal forests/taiga boreal boreal forests/taiga boreal boreal forests/taiga boreal boreal forests/taiga boreal boreal forests/taiga boreal boreal forests/taiga boreal boreal forests/taiga boreal boreal forests/taiga boreal boreal forests/taiga boreal temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate weeds weeds weeds weeds weeds weeds weeds weeds weeds weeds weeds weeds weeds weeds weeds weeds weeds weeds weeds weeds small mammals ground-dwelling spiders ground-dwelling ground-dwelling spiders ground-dwelling ground-dwelling spiders ground-dwelling ground-dwelling spiders ground-dwelling ground-dwelling spiders ground-dwelling ground-dwelling spiders ground-dwelling cereal cereal cereal cereal cereal cereal cereal cereal cereal cereal cereal cereal cereal cereal cereal cereal cereal cereal cereal cereal cereal cereal cereal cereal cereal cereal cereal 154 Appendices Seric Jelaska et al. (2010); MacLean Seric Jelaska et al. (2010); & Kodobocz & Usis (1992); Magura & & Finch (2006); Fahy Sroka (2007); et al. (2003) Gormally (1998); Poole Seric Jelaska et al. (2010); MacLean Seric Jelaska et al. (2010); & Kodobocz & Usis (1992); Magura & & Finch (2006); Fahy Sroka (2007); et al. (2003) Gormally (1998); Poole Seric Jelaska et al. (2010) Larsen & Work (2003) Larsen & Work Larsen & Work (2003) Larsen & Work MacLean & Usis (1992) MacLean & Usis (1992) MacLean & Usis (1992) MacLean & Usis (1992) MacLean & Usis (1992) MacLean & Usis (1992) Paine et al. (1996) Paine Riffell et al. (2006); Bub (2004) Riffell Estrada et al. (1997) Estrada Estrada & Coates-Estrada (2005) & Coates-Estrada Estrada Puchniak et al. (2012); Fritcher et al. Fritcher Puchniak et al. (2012); (2004 Sobek et al. (2009) Gardiner et al. (2010) Gardiner Ottonetti et al. (2010) Ottonetti Castano-Meneses & Palacios-Vargas & Palacios-Vargas Castano-Meneses (2003) - Clark et al. (2006) Clark et al. (2006) Ward & Ward (2001) & Ward Ward Ellsbury et al. (1998) Ellsbury et al. (1998) Pavuk et al. (1997) Pavuk Pavuk et al. (1997) Pavuk Pavuk et al. (1997) Pavuk Pavuk et al. (1997) Pavuk Pavuk et al. (1997) Pavuk Leslie et al. (2010) Paine et al. (1996) Paine Boutin et al. (1999) Estrada et al. (1997) Estrada Estrada & Coates-Estrada & Coates-Estrada Estrada (2005) Galle et al. (2009) Jodl et al. (1998) Castano-Meneses & Pala Castano-Meneses Gardiner et al. (2010) Gardiner Ottonetti et al. (2010) Ottonetti cios-Vargas (2003) cios-Vargas 24.3 24.3 22.5 47.5 47.5 49 49 49 49 49 49 13 37.3 178 46 41.5 422 5 46 37 11 10 22 29 23 27 23 22 22 22 49 5 14 31 15 24 62 4 42 18 BIOME BIOME BIOME ECOR ECOR ECOR ECOR ECOR ECOR ECOR ECOR SS ECOR SS SS BIOME ECOR SS SS SS 0.54 0.02 0.45 0.53 0.00 0.62 0.63 0.83 0.67 0.42 0.85 0.20 0.09 0.51 0.55 0.59 0.02 0.39 0.52 0.45 0.53 0.55 0.55 0.55 0.00 0.62 0.63 0.83 0.67 0.42 0.85 0.20 0.09 0.51 temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate grasslands temperate temperate grasslands temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate tropical moist broadleaf forests moist broadleaf tropical tropical moist broadleaf forests moist broadleaf tropical temperate grasslands temperate temperate broadleaf forests broadleaf temperate tropical dry forests tropical broadleaf temperate broadleaf forests broadleaf temperate tropical moist broadleaf forests moist broadleaf tropical ground beetles ground ground beetles ground ground beetles ground ground beetles ground ground beetles ground ground beetles ground ground beetles ground ground beetles ground ground beetles ground ground beetles ground ground beetles ground breeding birds breeding birds birds birds birds beetles ants bees ants corn corn corn corn corn corn corn corn corn corn corn corn corn corn corn corn corn corn corn corn 155 Zhang & (2007) Zhang & (2007) Zhang & (2007) Zhang & (2007) Zhang & (2007) Zhang & (2007) Zhang & (2007) Collins et al. (2002) Collins Collins et al. (2002) Collins Zhang & Zhang (2007); Hedl et al. Zhang & (2007); (2010) Zhang & Zhang (2007); Hedl et al. Zhang & (2007); (2010) Zhang & Zhang (2007); Hedl et al. Zhang & (2007); (2010) Brand & Dunn (1998); Brand (2002) & Dunn (1998); Brand Brand Brand & Dunn (1998); Brand (2002) & Dunn (1998); Brand Brand Brand & Dunn (1998); Brand (2002) & Dunn (1998); Brand Brand Brand & Dunn (1998); Brand (2002) & Dunn (1998); Brand Brand Heroldova et al. (2007) Heroldova Shahabuddin et al. (2005) Hibbert & Buddle (2008) Minor & Cianciolo (2007) Minor & Cianciolo (2007) Seric Jelaska et al. (2010); MacLean Seric Jelaska et al. (2010); & Kodobocz & Usis (1992); Magura & & Finch (2006); Fahy Sroka (2007); et al. (2003) Gormally (1998); Poole Zuo et al. (2008) Zuo et al. (2008) Zuo et al. (2008) Zuo et al. (2008) Zuo et al. (2008) Zuo et al. (2008) Zuo et al. (2008) Dieleman et al. (2000) Dieleman et al. (2000) Huijser et al. (2004) Huijser et al. (2004) Huijser et al. (2004) Bitzer et al. (2005) Bitzer Bitzer et al. (2005) Bitzer Bitzer et al. (2005) Bitzer Bitzer et al. (2005) Bitzer Heroldova et al. (2007) Heroldova Shahabuddin et al. (2005) Hibbert & Buddle (2008) Minor & Cianciolo (2007) Minor & Cianciolo (2007) Clark et al. (2006) 160 160 160 160 160 160 160 158 158 166 166 166 18 18 18 18 10 14 57 98 36 24.3 6 9 7 6 7 10 7 7 6 10 10 8 63 143 127 84 6 13 64 17 25 12.75 ECOR ECOR ECOR ECOR ECOR ECOR ECOR ECOR ECOR BIOME BIOME BIOME ECOR ECOR ECOR ECOR SS SS SS SS SS BIOME 0.96 0.96 0.94 -4.79 0.40 0.07 -0.12 0.83 0.31 0.96 0.94 0.96 0.96 0.96 0.94 0.96 0.96 0.96 0.94 0.94 0.95 -2.50 -6.94 -6.06 -3.67 0.40 0.07 -0.12 0.83 0.31 0.47 temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate grasslands temperate temperate grasslands temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate grasslands temperate temperate grasslands temperate temperate grasslands temperate temperate grasslands temperate temperate broadleaf forests broadleaf temperate tropical moist broadleaf forests moist broadleaf tropical temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate - weeds weeds weeds weeds weeds weeds weeds weeds weeds weeds weeds weeds springtails springtails springtails springtails ground-dwelling me ground-dwelling small mammals ground-dwelling dung ground-dwelling beetles ground-dwelling spiders ground-dwelling ground-dwelling beetle ground-dwelling mites so-stigmata ground beetles ground corn corn corn corn corn corn corn corn corn corn corn corn corn corn corn corn corn corn corn corn corn corn 156 Appendices Feest et al. (2011) Feest Feest et al. (2011) Feest Sobek et al. (2009) Degen et al. (2005) Degen Degen et al. (2005) Degen Peterson & Reich (2008) & Reich Peterson Burke et al. (2008); Zhang & Burke (2007) Burke et al. (2008); Zhang & Burke (2007) Burke et al. (2008); Zhang & Burke (2007) Burke et al. (2008); Zhang & Burke (2007) Burke et al. (2008); Zhang & Burke (2007) Burke et al. (2008); Zhang & Burke (2007) Burke et al. (2008) Burke Burke et al. (2008) Burke Zhang & (2007) Zhang & (2007) Zhang & (2007) Zhang & (2007) Zhang & (2007) Zhang & (2007) Zhang & (2007) Zhang & (2007) Zhang & (2007) Zhang & (2007) Semere & Slater (2007) & Slater Semere Semere & Slater (2007) & Slater Semere Jodl et al. (1998) Hiltbrunner et al. (2008) Hiltbrunner et al. (2008) Cook et al. (2007) Cook Mulugeta et al. (2001) Mulugeta Mulugeta et al. (2001) Mulugeta Mulugeta et al. (2001) Mulugeta Mulugeta et al. (2001) Mulugeta Mulugeta et al. (2001) Mulugeta Mulugeta et al. (2001) Mulugeta Swanton et al. (2006) Swanton Swanton et al. (2006) Swanton Zuo et al. (2008) Zuo et al. (2008) Zuo et al. (2008) Zuo et al. (2008) Zuo et al. (2008) Zuo et al. (2008) Zuo et al. (2008) Zuo et al. (2008) Zuo et al. (2008) Zuo et al. (2008) 25 25 422 64 64 109 121 121 121 121 121 121 307 307 160 160 160 160 160 160 160 160 160 160 15 15 62 33 35 35 29 12 11 19 15 15 24 22 4 5 6 5 7 3 4 6 6 6 BIOME BIOME ECOR ECOR ECOR ECOR BIOME BIOME BIOME BIOME BIOME BIOME ECOR ECOR ECOR ECOR ECOR ECOR ECOR ECOR ECOR ECOR ECOR ECOR 0.40 0.85 0.47 0.68 0.86 0.93 0.40 0.40 0.85 0.48 0.45 0.68 0.76 0.90 0.91 0.84 0.88 0.88 0.92 0.93 0.98 0.97 0.96 0.97 0.96 0.98 0.98 0.96 0.96 0.96 temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate butterflies butterflies beetles weeds weeds weeds weeds weeds weeds weeds weeds weeds weeds weeds weeds weeds weeds weeds weeds weeds weeds weeds weeds weeds miscanthus miscanthus miscanthus corn corn corn corn corn corn corn corn corn corn corn corn corn corn corn corn corn corn corn corn corn 157 Chung et al. (2000) Pfeiffer et al. (2008) Pfeiffer Pfeiffer et al. (2008) Pfeiffer Fayle et al. (2010) Fayle Benedick (2005) Fayle et al. (2010) Fayle Aratrakorn et al. (2006) Aratrakorn Danielsen & Heegaard (1995) Danielsen & Heegaard Koh & Wilcove (2008) & Wilcove Koh Koh & Wilcove (2008) & Wilcove Koh Azhar et al. (2011) Azhar et al. (2011) Anderson (2009) Anderson (2009) Sheldon et al. (2010) Sheldon et al. (2010) Sheldon et al. (2010) Liow et al. (2001) Danielsen & Heegaard (1995) Danielsen & Heegaard Chung et al. (2000) Fahy & Gormally (1998); Poole et al. & Gormally (1998); Poole Fahy (2003) Fahy & Gormally (1998); Poole et al. & Gormally (1998); Poole Fahy (2003) Fahy & Gormally (1998); Poole et al. & Gormally (1998); Poole Fahy (2003) Fahy & Gormally (1998); Poole et al. & Gormally (1998); Poole Fahy (2003) Chung et al. (2000) Pfeiffer et al. (2008) Pfeiffer Pfeiffer et al. (2008) Pfeiffer Fayle et al. (2010) Fayle Benedick (2005) Fayle et al. (2010) Fayle Aratrakorn et al. (2006) Aratrakorn Danielsen & Heegaard Danielsen & Heegaard (1995) Koh & Wilcove (2008) & Wilcove Koh Koh & Wilcove (2008) & Wilcove Koh Azhar et al. (2011) Azhar et al. (2011) Najera & Simonetti (2010) Najera Najera & Simonetti (2010) Najera Sheldon & Styring (2011) Sheldon & Styring (2011) Koh (2008) Koh Liow et al. (2001) Danielsen & Heegaard Danielsen & Heegaard (1995) Chung et al. (2000) Semere & Slater (2007) & Slater Semere Semere & Slater (2007) & Slater Semere Semere & Slater (2007) & Slater Semere Semere & Slater (2007) & Slater Semere 557 280 280 120 26 36 108 67 159 159 194 194 157 157 115 70 92.5 8 8 174 30.5 30.5 13.5 13.5 75 39 36 58 12 35 41 17 40 40 67 130 12 22 24 24 37 17 1 40 25 14 6.6 6.6 SS SS SS SS SS SS SS SS SS SS SS SS ECOR ECOR ECOR ECOR ECOR SS SS SS ECOR ECOR ECOR ECOR 0.87 0.86 0.87 0.52 0.54 0.03 0.62 0.75 0.75 0.49 0.89 0.72 0.60 -1.13 0.88 0.77 0.36 0.51 0.87 0.86 0.87 0.52 0.54 0.03 0.62 0.75 0.75 0.75 0.65 0.33 0.92 0.86 0.79 0.66 0.60 -1.13 0.88 0.77 0.18 0.54 0.51 0.51 tropical moist broadleaf forests moist broadleaf tropical tropical moist broadleaf forests moist broadleaf tropical tropical moist broadleaf forests moist broadleaf tropical tropical moist broadleaf forests moist broadleaf tropical tropical moist broadleaf forests moist broadleaf tropical tropical moist broadleaf forests moist broadleaf tropical tropical moist broadleaf forests moist broadleaf tropical tropical moist broadleaf forests moist broadleaf tropical tropical moist broadleaf forests moist broadleaf tropical tropical moist broadleaf forests moist broadleaf tropical tropical moist broadleaf forests moist broadleaf tropical tropical moist broadleaf forests moist broadleaf tropical tropical moist broadleaf forests moist broadleaf tropical tropical moist broadleaf forests moist broadleaf tropical tropical moist broadleaf forests moist broadleaf tropical tropical moist broadleaf forests moist broadleaf tropical tropical moist broadleaf forests moist broadleaf tropical tropical moist broadleaf forests moist broadleaf tropical tropical moist broadleaf forests moist broadleaf tropical tropical moist broadleaf forests moist broadleaf tropical temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate ground beetles ground canopy ants canopy canopy ants canopy canopy ants canopy butterflies bird’s nest ants bird’s birds birds birds birds birds birds birds birds birds birds birds bees bats arboreal beetles arboreal weeds weeds ground beetles ground ground beetles ground oil palm oil palm oil palm oil palm oil palm oil palm oil palm oil palm oil palm oil palm oil palm oil palm oil palm oil palm oil palm oil palm oil palm oil palm oil palm oil palm miscanthus miscanthus miscanthus miscanthus 158 Appendices Koivula (2002) Koivula Sroka & Finch (2006) Sroka Sroka & Finch (2006) Sroka Sroka & Finch (2006) Sroka Sroka & Finch (2006) Sroka Feest et al. (2011) Feest Feest et al. (2011) Feest Feest et al. (2011) Feest Feest et al. (2011) Feest Koivula (2002) Koivula Chung et al. (2000) Scott et al. (2004) Scott Chey (2006) Chey (2006) Chey (2006) Chang et al. (2007) Glor et al. (2005) Fayle et al. (2010) Fayle Maddox et al. (2007) Maddox Hassall et al. (2006) Davis & Philips (2005) Davis Room (1975) Room Brühl & Eltz (2010) Brühl & Eltz (2010) Brühl & Eltz (2010) Brühl & Eltz (2010) Brühl & Eltz (2010) Kinnunen & Tiainen (1999) De Snoo (1997) De Snoo (1997) De Snoo (1997) De Snoo (1997) De Snoo (1997) De Snoo (1997) De Snoo (1997) De Snoo (1997) Kinnunen & Tiainen (1999) Chung et al. (2000) Scott et al. (2004) Scott Chey (2006) Chey (2006) Chey (2006) Chang et al. (2007) Glor et al. (2005) Fayle et al. (2010) Fayle Maddox et al. (2007) Maddox Hassall et al. (2006) Davis & Philips (2005) Davis Room (1975) Room Brühl & Eltz (2010) Brühl & Eltz (2010) Brühl & Eltz (2010) Brühl & Eltz (2010) Brühl & Eltz (2010) 23 23.1 23.1 23.1 23.1 12.3 12.3 12.3 12.3 23 306 5 78 133 75 6 5.81 216 38 8 25 49 187 187 187 187 187 33 9.5 9.4 14 11.8 2.5 0.7 2.2 1.3 42 64 3 90 73 85 6 4.71 56 4 4 20 29 9 7 14 10 23 ECOR ECOR ECOR ECOR ECOR ECOR ECOR ECOR ECOR ECOR SS SS SS SS SS SS SS SS SS SS SS SS SS SS SS SS SS -0.43 0.52 0.86 -0.83 0.79 0.40 0.05 0.00 0.19 0.74 0.89 0.50 0.20 0.41 0.93 -0.43 0.59 0.59 0.39 0.49 0.80 0.94 0.82 0.89 -0.83 0.79 0.40 -0.15 0.45 -0.13 0.00 0.19 0.74 0.89 0.50 0.20 0.41 0.95 0.96 0.93 0.95 0.88 boreal forests/taiga boreal temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate boreal forests/taiga boreal tropical moist broadleaf forests moist broadleaf tropical tropical moist broadleaf forests moist broadleaf tropical tropical moist broadleaf forests moist broadleaf tropical tropical moist broadleaf forests moist broadleaf tropical tropical moist broadleaf forests moist broadleaf tropical tropical moist broadleaf forests moist broadleaf tropical tropical moist broadleaf forests moist broadleaf tropical tropical moist broadleaf forests moist broadleaf tropical tropical moist broadleaf forests moist broadleaf tropical tropical moist broadleaf forests moist broadleaf tropical tropical moist broadleaf forests moist broadleaf tropical tropical moist broadleaf forests moist broadleaf tropical tropical moist broadleaf forests moist broadleaf tropical tropical moist broadleaf forests moist broadleaf tropical tropical moist broadleaf forests moist broadleaf tropical tropical moist broadleaf forests moist broadleaf tropical tropical moist broadleaf forests moist broadleaf tropical ground beetles ground ground beetles ground ground beetles ground ground beetles ground ground beetles ground butterflies butterflies butterflies butterflies ground beetles ground subterranean beetles subterranean small mammals moths moths moths mosquitoes lizards litter-dwelling ants litter-dwelling large mammals large ground-dwelling ground-dwelling isopods ground-dwelling dung ground-dwelling beetles ground-dwelling ants ground-dwelling ground-dwelling ants ground-dwelling ground-dwelling ants ground-dwelling ground-dwelling ants ground-dwelling ground-dwelling ants ground-dwelling ground-dwelling ants ground-dwelling potato potato potato potato potato potato potato potato potato rapeseed oil palm oil palm oil palm oil palm oil palm oil palm oil palm oil palm oil palm oil palm oil palm oil palm oil palm oil palm oil palm oil palm oil palm 159 Seric Jelaska et al. (2010); MacLean Seric Jelaska et al. (2010); & Kodobocz & Usis (1992); Magura & & Finch (2006); Fahy Sroka (2007); et al. (2003) Gormally (1998); Poole Seric Jelaska et al. (2010); MacLean Seric Jelaska et al. (2010); & Kodobocz & Usis (1992); Magura & & Finch (2006); Fahy Sroka (2007); et al. (2003) Gormally (1998); Poole Larsen & Work (2003) Larsen & Work Larsen & Work (2003) Larsen & Work Paine et al. (1996) Paine Riffell et al. (2006); Bub (2004) Riffell Puchniak et al. (2012); Fritcher et al. Fritcher Puchniak et al. (2012); (2004) Juha (2004); Niemelä et al. (1994) Juha (2004); Niemelä et al. (1994) Juha (2004); Niemelä et al. (1994) Ponge & Delhaye (1995) & Delhaye Ponge Ponge & Delhaye (1995) & Delhaye Ponge Ponge & Delhaye (1995) & Delhaye Ponge Gaines & Gratton (2010) Gaines & Gratton Koivula (2002) Koivula Koivula (2002); Niemelä et al. (1994); (2002); Koivula Halme & Niemela (1993) Koivula (2002); Niemelä et al. (1994); (2002); Koivula Halme & Niemela (1993) Koivula (2002); Niemelä et al. (1994); (2002); Koivula Halme & Niemela (1993) Koivula (2002); Niemelä et al. (1994); (2002); Koivula Halme & Niemela (1993) Clark et al. (2006) Clark et al. (2006) Ellsbury et al. (1998) Ellsbury et al. (1998) Paine et al. (1996) Paine Boutin et al. (1999) Galle et al. (2009) Hyvönen & Salonen (2002) Hyvönen & Salonen (2002) Hyvönen & Salonen (2002) Emmerling (2001) Emmerling (2001) Emmerling (2001) Gaines & Gratton (2010) Gaines & Gratton Kinnunen et al. (2001) Kinnunen et al. (2001) Kinnunen et al. (2001) Kinnunen et al. (2001) Kinnunen et al. (2001) 24.3 24.3 47.5 47.5 13 37.3 41.5 61.4 61.4 61.4 11 11 11 25 23 17.5 17.5 17.5 17.5 12 13.25 22 19 2 16 17 31 38 40 6 6 4 30 56 33 22 22 15 BIOME BIOME ECOR ECOR SS ECOR BIOME ECOR ECOR ECOR BIOME BIOME BIOME SS ECOR ECOR ECOR ECOR ECOR 0.43 0.57 0.85 0.57 0.59 0.41 0.52 -0.20 -0.54 0.51 0.45 0.54 0.60 0.85 0.57 0.59 0.50 0.38 0.35 0.45 0.45 0.64 -0.20 -1.43 -0.89 -0.26 -0.26 0.14 temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate grasslands temperate temperate grasslands temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate grasslands temperate boreal forests/taiga boreal boreal forests/taiga boreal boreal forests/taiga boreal temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate boreal forests/taiga boreal boreal forests/taiga boreal boreal forests/taiga boreal boreal forests/taiga boreal boreal forests/taiga boreal ground beetles ground ground beetles ground ground beetles ground ground beetles ground breeding birds breeding birds birds weeds weeds weeds earthworms earthworms earthworms ground beetles ground ground beetles ground ground beetles ground ground beetles ground ground beetles ground ground beetles ground soybean soybean soybean soybean soybean soybean soybean rye rye rye rye rye rye potato potato potato potato potato potato 160 Appendices Nunes et al. (2006) Nunes et al. (2006) Manica et al. (2010) Heroldova et al. (2007) Heroldova Koivula (2002) Koivula Koivula (2002); Niemelä et al. (1994); (2002); Koivula Halme & Niemela (1993) Koivula (2002); Niemelä et al. (1994); (2002); Koivula Halme & Niemela (1993) Koivula (2002); Niemelä et al. (1994); (2002); Koivula Halme & Niemela (1993) Koivula (2002); Niemelä et al. (1994); (2002); Koivula Halme & Niemela (1993) Koivula (2002) Koivula Sroka & Finch (2006) Sroka Sroka & Finch (2006) Sroka Sroka & Finch (2006) Sroka Sroka & Finch (2006) Sroka Burke et al. (2008) Burke Burke et al. (2008) Burke Burke et al. (2008) Burke Collins et al. (2002) Collins Collins et al. (2002) Collins Flinn et al. (2008) Flinn et al. (2008) Flinn et al. (2008) Flinn et al. (2008) Seric Jelaska et al. (2010); MacLean Seric Jelaska et al. (2010); & Kodobocz & Usis (1992); Magura & & Finch (2006); Fahy Sroka (2007); et al. (2003) Gormally (1998); Poole Nunes et al. (2006) Nunes et al. (2006) Manica et al. (2010) Heroldova et al. (2007) Heroldova Kinnunen et al. (2001) Kinnunen et al. (2001) Kinnunen et al. (2001) Kinnunen et al. (2001) Kinnunen et al. (2001) Kinnunen & Tiainen (1999) De Snoo (1997) De Snoo (1997) De Snoo (1997) De Snoo (1997) Mulugeta et al. (2001) Mulugeta Mulugeta et al. (2001) Mulugeta Mulugeta et al. (2001) Mulugeta Swanton et al. (2006) Swanton Swanton et al. (2006) Swanton De La Fuente et al. (2010) De La Fuente De La Fuente et al. (2006) De La Fuente Boutin (2006) Boutin (2006) Clark et al. (2006) 2 2 112 10 23 17.5 17.5 17.5 17.5 23 23.1 23.1 23.1 23.1 121 121 121 307 307 158 158 280 280 24.3 5 4 28 9 50 31 22 33 31 36 16.1 14.3 16 15.7 16 18 18 25 23 62 66 78 58 16 SS SS SS SS ECOR ECOR ECOR ECOR ECOR ECOR ECOR ECOR ECOR ECOR BIOME BIOME BIOME ECOR ECOR BIOME BIOME ECOR ECOR BIOME -1.25 0.75 0.10 -0.77 -0.57 0.33 0.86 0.92 0.61 0.58 0.76 -1.50 -1.00 0.75 0.10 -1.17 -0.77 -0.26 -0.89 -0.77 -0.57 0.30 0.38 0.31 0.32 0.87 0.85 0.85 0.92 0.93 0.61 0.58 0.72 0.79 0.34 tropical moist broadleaf forests moist broadleaf tropical tropical moist broadleaf forests moist broadleaf tropical tropical grasslands tropical temperate broadleaf forests broadleaf temperate boreal forests/taiga boreal boreal forests/taiga boreal boreal forests/taiga boreal boreal forests/taiga boreal boreal forests/taiga boreal boreal forests/taiga boreal temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate grasslands temperate temperate grasslands temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate earthworms earthworms birds small mammals ground beetles ground ground beetles ground ground beetles ground ground beetles ground ground beetles ground ground beetles ground ground beetles ground ground beetles ground ground beetles ground ground beetles ground weeds weeds weeds weeds weeds weeds weeds weeds weeds ground beetles ground sugarcane sugarcane sugarcane sugar beet sugar sugar beet sugar sugar beet sugar sugar beet sugar sugar beet sugar sugar beet sugar sugar beet sugar sugar beet sugar sugar beet sugar sugar beet sugar sugar beet sugar soybean soybean soybean soybean soybean soybean soybean soybean soybean soybean 161 Burke et al. (2004) Burke Seric Jelaska et al. (2010) Paine et al. (1996) Paine Paine et al. (1996) Paine Au et al. (2008) Au et al. (2008) Riffell et al. (2006); Bub (2004) Riffell Bakker & Higgins (2009) Bakker Thogmartin et al. (2009) Eggleton et al. (2009); Ponge & Ponge et al. (2009); Eggleton (1995) Delhaye Gardiner et al. (2010) Gardiner Eggleton et al. (2009); Ponge & Ponge et al. (2009); Eggleton (1995) Delhaye Puchniak et al. (2012); Fritcher et al. Fritcher Puchniak et al. (2012); (2004 Feest et al. (2011) Feest Gillison et al. (2003) Feest et al. (2011) Feest Vasconcellos (2010) Vasconcellos Feest et al. (2011) Feest Takele et al. (2011) Takele Feest et al. (2011) Feest Takele et al. (2011) Takele Diaz (2006) Dotta & Verdade (2007) & Verdade Dotta Topping & Lövei (1997) & Lövei Topping Dotta & Verdade (2011) & Verdade Dotta Huusela-Veistola & Vasarainen (2000) & Vasarainen Huusela-Veistola - Gardiner et al. (2010) Gardiner Ward & Ward (2001) & Ward Ward Paine et al. (1996) Paine Paine et al. (1996) Paine Roth et al. (2005) Roth Roth et al. (2005) Roth Robertson et al. (2011) Bakker & Higgins (2009) Bakker Murray & Best (2003) Murray Schmidt et al. (2001) Gardiner et al. (2010) Gardiner Schmidt et al. (2001) Galle et al. (2009) De Snoo (1997) Shrestha et al. (2010) Shrestha De Snoo (1997) Miranda et al. (2004) Miranda De Snoo (1997) Takele et al. (2011) Takele De Snoo (1997) Takele et al. (2011) Takele Guerrero et al. (2010) Guerrero Dotta & Verdade (2007) & Verdade Dotta Vink et al. (2004) Huusela-Veistola & Vasara Huusela-Veistola Dotta & Verdade (2011) & Verdade Dotta inen (2000) 307 22.5 13 13 64 45 37.3 3.6 141 13 46 13 41.5 12.3 102 12.3 15 12.3 9 12.3 10 35.5 18 11 25 23 143 29 9 10 17 17 30 2.4 45 6 55 9 27 4 12 1.7 4 3 9 1.3 8 20 15 11 7 19 ECOR BIOME SS SS ECOR ECOR ECOR SS ECOR BIOME SS BIOME BIOME ECOR BIOME ECOR ECOR ECOR ECOR ECOR ECOR ECOR SS ECOR SS SS 0.53 -0.29 0.27 0.68 0.20 0.33 0.68 -0.20 0.46 0.35 0.88 0.73 0.80 0.10 0.44 0.00 0.72 0.17 0.53 -0.29 0.31 0.23 0.73 0.62 0.20 0.33 0.68 0.54 -0.20 0.31 0.35 0.68 0.88 0.86 0.73 0.76 0.00 0.89 0.20 0.44 0.17 0.00 0.72 0.17 temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate grasslands temperate temperate grasslands temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate grasslands temperate temperate broadleaf forests broadleaf temperate tropical moist broadleaf forests moist broadleaf tropical temperate broadleaf forests broadleaf temperate tropical moist broadleaf forests moist broadleaf tropical temperate broadleaf forests broadleaf temperate tropical moist broadleaf forests moist broadleaf tropical temperate broadleaf forests broadleaf temperate tropical moist broadleaf forests moist broadleaf tropical Mediterranean forests Mediterranean tropical grasslands tropical temperate grasslands temperate boreal forests/taiga boreal tropical grasslands tropical - above-ground leaf above-ground weeds ground beetles ground breeding birds breeding breeding birds breeding birds birds birds birds birds earthworms bees earthworms birds butterflies weeds butterflies termites butterflies small mammals butterflies small mammals birds large mammals large above-ground spiders above-ground hoppers large mammals large wheat switchgrass switchgrass switchgrass switchgrass switchgrass switchgrass switchgrass switchgrass switchgrass wheat switchgrass wheat sunflower wheat sugarcane wheat sugarcane wheat sugarcane wheat sugarcane wheat sugarcane wheat sugarcane 162 Appendices Hibbert & Buddle (2008) Hibbert & Buddle (2008) Ziesche & Roth (2008) Ziesche & Roth Ziesche & Roth (2008) Ziesche & Roth Ziesche & Roth (2008) Ziesche & Roth Ziesche & Roth (2008) Ziesche & Roth Ziesche & Roth (2008) Ziesche & Roth Pluess et al. (2008) Pluess et al. (2008) Seric Jelaska et al. (2010) Seric Jelaska et al. (2010) Liu et al. (2010); Jelaska et al. (2011); Jelaska et al. (2011); Liu et al. (2010); & Finch (2006) Sroka Liu et al. (2010); Jelaska et al. (2011); Jelaska et al. (2011); Liu et al. (2010); & Finch (2006) Sroka Avgin & Emre (2010); Avgin (2006) Avgin (2010); & Emre Avgin Avgin & Emre (2010); Avgin (2006) Avgin (2010); & Emre Avgin Seric Jelaska et al. (2010) Larsen & Work (2003) Larsen & Work Larsen & Work (2003) Larsen & Work Sroka & Finch (2006) Sroka Sroka & Finch (2006) Sroka Sroka & Finch (2006) Sroka Sroka & Finch (2006) Sroka Sroka & Finch (2006) Sroka Eggleton et al. (2009) Eggleton Eggleton et al. (2009); Ponge & Ponge et al. (2009); Eggleton (1995) Delhaye Schmidt-Entling & Döbeli Schmidt-Entling (2009) Schmidt-Entling & Döbeli Schmidt-Entling (2009) Bátary et al. (2012) Bátary et al. (2012) Basedow (1998) Basedow (1998) Basedow (1998) Butt & Sherawat (2012) Butt & Sherawat Pluess et al. (2008) Purtauf et al. (2005) Clough et al. (2007) Bátary et al. (2012) Bátary et al. (2012) Avgin & Luff (2009) Avgin Avgin & Luff (2009) Avgin Kromp & Steinberger (1992) & Steinberger Kromp Ellsbury et al. (1998) Ellsbury et al. (1998) Saska et al. (2007) De Snoo (1997) De Snoo (1997) De Snoo (1997) De Snoo (1997) Schmidt et al. (2001) Schmidt et al. (2001) 57 57 34.3 34.3 34.3 34.3 34.3 38 38 43 43 28 28 39.5 39.5 43 47.5 47.5 47 23.1 23.1 23.1 23.1 15 13 35 65 24 20 55 73 72 39 34 84 90 37 42 13 12 44 20 15 75 16.5 14.9 22.7 21.5 5 6 BIOME BIOME ECOR ECOR ECOR ECOR ECOR BIOME SS BIOME BIOME BIOME BIOME ECOR ECOR ECOR ECOR ECOR ECOR ECOR ECOR ECOR ECOR ECOR BIOME 0.12 0.36 -0.95 -0.03 0.11 -0.95 -1.09 -0.41 0.68 -0.02 0.63 -0.60 0.18 0.67 0.39 -0.14 0.30 0.42 -0.61 -1.13 -1.10 -0.03 0.11 -0.95 -1.09 -0.32 -0.50 0.67 0.70 -0.02 0.58 0.68 -0.60 0.29 0.35 0.02 0.07 0.67 0.54 temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate deserts shrublands and xeric deserts shrublands and xeric temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate Mediterranean forests Mediterranean Mediterranean forests Mediterranean temperate broadleaf forests broadleaf temperate temperate grasslands temperate temperate grasslands temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate spiders spiders ground-dwelling spiders ground-dwelling ground-dwelling spiders ground-dwelling ground-dwelling spiders ground-dwelling ground-dwelling spiders ground-dwelling ground-dwelling spiders ground-dwelling ground-dwelling spiders ground-dwelling ground-dwelling spiders ground-dwelling ground beetles ground ground beetles ground ground beetles ground ground beetles ground ground beetles ground ground beetles ground ground beetles ground ground beetles ground ground beetles ground ground beetles ground ground beetles ground ground beetles ground ground beetles ground ground beetles ground earthworms earthworms wheat wheat wheat wheat wheat wheat wheat wheat wheat wheat wheat wheat wheat wheat wheat wheat wheat wheat wheat wheat wheat wheat wheat wheat wheat 163 Degen et al. (2005) Degen Degen et al. (2005) Degen Degen et al. (2005) Degen Degen et al. (2005) Degen Degen et al. (2005) Degen Degen et al. (2005) Degen Degen et al. (2005) Degen Degen et al. (2005) Degen Degen et al. (2005) Degen Burke et al. (2008) Burke Burke et al. (2008) Burke Taboada et al. (2010) Taboada Taboada et al. (2010) Taboada Taboada et al. (2010) Taboada Taboada et al. (2010) Taboada Taboada et al. (2010) Taboada Taboada et al. (2010) Taboada Taboada et al. (2010) Taboada Zhang & Zhang (2007); Hedl et al. Zhang & (2007); (2010) Zhang & Zhang (2007); Hedl et al. Zhang & (2007); (2010) Zhang & Zhang (2007); Hedl et al. Zhang & (2007); (2010) Zhang & Zhang (2007); Hedl et al. Zhang & (2007); (2010) Ulber et al. (2009) Ulber et al. (2009) Ulber et al. (2009) Ulber et al. (2009) Diehl et al. (2012) Bátary et al. (2012) Bátary et al. (2012) Hiltbrunner et al. (2008) Hiltbrunner et al. (2008) Swanton et al. (2006) Swanton Swanton et al. (2006) Swanton Kiss et al. (1997) Hernandez Plaza et al. (2011) Hernandez Plaza et al. (2011) Hernandez Plaza et al. (2011) Caballero-López et al. (2012) Caballero-López Caballero-López et al. (2012) Caballero-López Caballero-López et al. (2012) Caballero-López Roschewitz et al. (2005) Roschewitz Roschewitz et al. (2005) Roschewitz Roschewitz et al. (2005) Roschewitz Gabriel et al. (2005) 64 64 64 64 64 64 64 64 64 307 307 172 172 172 172 172 172 172 166 166 166 166 31 38 59 75 18 40 69 25 31 24 22 7 34 31 30 15 35 40 142 104 153 41 ECOR ECOR ECOR ECOR ECOR ECOR ECOR ECOR ECOR ECOR ECOR ECOR BIOME BIOME BIOME BIOME BIOME BIOME BIOME BIOME BIOME BIOME 0.21 0.72 0.15 0.56 0.93 0.96 0.82 0.83 0.20 0.75 0.52 0.41 0.08 -0.17 0.72 0.38 -0.08 0.61 0.52 0.92 0.93 0.96 0.80 0.82 0.83 0.91 0.80 0.77 0.14 0.37 0.08 0.75 temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate Mediterranean forests Mediterranean Mediterranean forests Mediterranean Mediterranean forests Mediterranean Mediterranean forests Mediterranean Mediterranean forests Mediterranean Mediterranean forests Mediterranean temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate temperate broadleaf forests broadleaf temperate weeds weeds weeds weeds weeds weeds weeds weeds weeds weeds weeds weeds weeds weeds weeds weeds weeds weeds weeds weeds weeds weeds wheat wheat wheat wheat wheat wheat wheat wheat wheat wheat wheat wheat wheat wheat wheat wheat wheat wheat wheat wheat wheat wheat 164 Appendices
D4 | Statistical test results
table D2 | p-values for the Kruskal-Wallis statistical analyses. Comparisons with p-values < 0.05 are redone pairwise using the Mann-Whitney U-test to identify which groups differ significantly (see Table III-X).
comparing for the effect on p-value boreal forests/taiga; arthropods in Pooideae croplands 0.022 Mediterranean forests and scrub; temperate broadleaf and mixed forests; temperate grasslands and shrublands temperate broadleaf and mixed forests; arthropods in Panicoideae croplands 0.659 (sub)tropical moist broadleaf forests temperate broadleaf and mixed forests; arthropods in low crop croplands 0.014 boreal forests/taiga temperate broadleaf and mixed forests; birds in Panicoideae croplands 0.764 temperate grasslands and shrublands boreal forests/taiga; vascular plants in Pooideae croplands 0.105 temperate broadleaf and mixed forests boreal forests/taiga; all species groups and crop types combined 0.000 deserts and xeric shrublands; Mediterranean forests and scrub; temperate broadleaf and mixed forests; temperate grasslands and shrublands; (sub)tropical moist broadleaf forests arthropods; vascular plants Pooideae croplands in boreal forests/taiga 0.327 birds; arthropods; vascular plants Panicoideae croplands in temperate broadleaf 0.063 and mixed forests arthropods; vascular plants Pooideae croplands in temperate broadleaf and 0.000 mixed forests arthropods; vascular plants low crop croplands in temperate broadleaf and 0.072 mixed forests mammals; birds; arthropods oil palm croplands in (sub)tropical moist 0.546 broadleaf forests mammals; birds; arthropods; vascular plants all biomes and crop types combined 0.000 Pooideae; low crops arthropods in boreal forests/taiga 0.624 Panicoideae; Pooideae; low crops arthropods in temperate broadleaf and mixed 0.018 forests Panicoideae; oil palm arthropods in (sub)tropical moist broadleaf 0.655 forests Panicoideae; Pooideae; low crops vascular plants in temperate broadleaf and 0.439 mixed forests Panicoideae; Pooideae; low crops; oil palm all biomes and species groups combined 0.038 annual crops; permanent crops (oil palm) all biomes and species groups combined 0.020 same study references; ecoregion-based refer- all biomes, species groups and crop types 0.843 ences; biome-based references combined 165
table D3 | p-values for the Mann-Whitney U-test comparing the impact of different crop types on arthropods in the temperate broadleaf and mixed forests biome. In this case, differences are considered significant at p < 0.017, in accordance with the Bonferroni correction for multiple comparisons.
temperate broadleaf temperate broadleaf temperate broadleaf and and mixed forests and mixed forests mixed forests arthropods arthropods arthropods Panicoideae Pooideae low crops temperate broadleaf and mixed forests arthropods Panicoideae temperate broadleaf and mixed forests p = 0.015 arthropods Pooideae temperate broadleaf and mixed forests p = 0.677 p = 0.032 arthropods low crops
table D4 | p-values for the Mann-Whitney U-test comparing the impact of Pooideae croplands on different species groups in the temperate broadleaf and mixed forests biome. In this case, differences are considered significant at p < 0.005.
temperate broadleaf temperate broadleaf and mixed forests and mixed forests arthropods vascular plants Pooideae Pooideae temperate broadleaf and mixed forests arthropods Pooideae temperate broadleaf and mixed forests p = 0.000 vascular plants Pooideae 166 Appendices
table D5 | p-values for the Mann-Whitney U-test comparing the impact of Pooideae croplands on arthropods in different biomes. In this case, differences are considered significant at p < 0.008, in accordance with the Bonferroni correction for multiple comparisons.
temperate broadleaf temperate grasslands Mediterranean boreal for- and mixed forests and shrublands forests and scrub ests/taiga arthropods arthropods arthropods arthropods Pooideae Pooideae Pooideae Pooideae temperate broadleaf and mixed forests arthropods Pooideae temperate grasslands and shrublands p = 0.017 arthropods Pooideae Mediterranean for- ests and scrub p = 0.004 p = 0.049 arthropods Pooideae boreal forests/taiga arthropods p = 0.563 p = 0.327 p = 0.075 Pooideae
table D6 | p-values for the Mann-Whitney U-test comparing the impact of low crop croplands on arthropods in different biomes. In this case, differences are considered significant at p < 0.05.
temperate broadleaf and boreal forests/taiga mixed forests arthropods arthropods low crops low crops temperate broadleaf and mixed forests arthropods low crops boreal forests/taiga arthropods p = 0.014 low crops 167
table D7 | p-values for the Mann-Whitney U-test comparing the average impact of all crop types on all species groups in different biomes. In this case, differences are considered significant at p < 0.003, in accordance with the Bonferroni correction for multiple comparisons.
boreal forests/ deserts and Mediterranean temperate temperate (sub)tropical taiga xeric shrub- forests and broadleaf and grasslands and moist broad- lands scrub mixed forests shrublands leaf forests boreal forests/ taiga deserts and xeric shrub- p = 0.078 lands Mediterranean forests and p = 0.002 p = 0.137 scrub temperate broadleaf and p = 0.000 p = 0.636 p = 0.073 mixed forests temperate grasslands p = 0.003 p = 0.678 p = 0.072 p = 0.744 and shrub- lands (sub)tropical moist broad- p = 0.000 p = 0.229 p = 0.684 p = 0.016 p = 0.069 leaf forests table D8 | p-values for the Mann-Whitney U-test comparing the average impact of all crop types in all biomes on different species groups. In this case, differences are considered significant at p < 0.008, in accordance with the Bonferroni correction for multiple comparisons.
mammals birds arthropods vascular plants mammals birds p = 0.178 arthropods p = 0.347 p = 0.002 vascular plants p = 0.057 p = 0.060 p = 0.000 table D9 | p-values for the Mann-Whitney U-test comparing the average impact of different crop types on all species groups in all biomes. In this case, differences are considered significant at p < 0.008, in accordance with the Bonferroni correction for multiple comparisons.
Pooideae Panicoideae low crops oil palm Pooideae Panicoideae p = 0.020 low crops p = 0.092 p = 0.705 oil palm p = 0.004 p = 0.258 p = 0.638 168 Appendices
table D10 | p-values for the Mann-Whitney U-test comparing permanent crops (oil palm) and annual crops (all others). In this case, differences are considered significant at p < 0.05.
annual crops permanent crops annual crops permanent crops p = 0.020
D5 | Characterization factor calculation example
As an example, we explain the case of arthropod species richness in oil crop plantation within the (sub)tropical moist broadleaf forest biome. There were a total of 22 CFs calculated for this case (Table D11). The way of calculating is the same for all 22, so we only give one example (step 1; see below).
Secondly, we calculated the average per study when a study provided multiple species richness data points (for example from different plots or from different years). The data in bold in Table D11 is one example of a study with five data points (study no. 9). Step 2 (see below) shows how we calculated the study-average of these 5 CFs. The same was done for the 3 CFs in study no. 15. Now a total of 16 CFs from 16 different studies remain.
In step 3 (see below) we calculate the median CF for the specific combination of species, crop, and biome, in this case for arthropods in oil crop plantation within the (sub)tropical moist broadleaf forest biome. 169
table D11 | Characterization factors for the calculation example. See Table D1 for underlying information. Data in bold are used for step 1 and 2 of the example (see below).
crop type species group biome CFplot study CFstudy Scrop Sref oil palm arboreal beetles tropical moist 0.77 1 0.77 40 174 broadleaf forests oil palm bees tropical moist -1.13 2 -1.13 17 8 broadleaf forests oil palm bird’s nest ants tropical moist 0.03 3 0.03 35 36 broadleaf forests oil palm butterflies tropical moist 0.54 4 0.54 12 26 broadleaf forests oil palm canopy ants tropical moist 0.52 5 0.52 58 120 broadleaf forests oil palm canopy ants tropical moist 0.87 6 0.87 36 280 broadleaf forests oil palm canopy ants tropical moist 0.86 7 0.86 39 280 broadleaf forests oil palm ground beetles tropical moist 0.87 8 0.87 75 557 broadleaf forests oil palm ground-dwelling ants tropical moist 0.88 9 0.93 23 187 broadleaf forests oil palm ground-dwelling ants tropical moist 0.95 9 10 187 broadleaf forests oil palm ground-dwelling ants tropical moist 0.93 9 14 187 broadleaf forests oil palm ground-dwelling ants tropical moist 0.96 9 7 187 broadleaf forests oil palm ground-dwelling ants tropical moist 0.95 9 9 187 broadleaf forests oil palm ground-dwelling ants tropical moist 0.41 10 0.41 29 49 broadleaf forests oil palm ground-dwelling dung tropical moist 0.20 11 0.20 20 25 beetles broadleaf forests oil palm ground-dwelling isopods tropical moist 0.50 12 0.50 4 8 broadleaf forests oil palm litter-dwelling ants tropical moist 0.74 13 0.74 56 216 broadleaf forests oil palm mosquitoes tropical moist 0.00 14 0.00 6 6 broadleaf forests oil palm moths tropical moist -0.15 15 0.05 85 75 broadleaf forests oil palm moths tropical moist 0.45 15 73 133 broadleaf forests oil palm moths tropical moist -0.15 15 90 78 broadleaf forests oil palm subterranean beetles tropical moist 0.79 16 0.79 64 306 broadleaf forests 170 Appendices
Step 1 Characterization factor per plot
Scrop,x,i,y CFplot,x,i,y = 1 - Sref,i,y
CFplot,oil,trop.forest,arthr = 1 - 23/187 = 0.88
Step 2 Characterization factor per study (e.g. for study no. 9)
M CFplot,x,i,y,m CFstudy,x,i,y =∑▒〖 m=1 M 0.88+0.95+0.93+0.96+0.95 CF = = 0.93 study,oil,trop.forest,arthr 5
Step 3 Characterization factor per crop-species-biome combination
rank 1 2 3 4 5 6 7 8 CF -1.13 0.00 0.03 0.05 0.20 0.41 0.50 0.52 9 10 11 12 13 14 15 16 0.54 0.74 0.77 0.79 0.86 0.87 0.87 0.93
Median CFoil,trop.forest,arthr = 0.53 171
Appendix E
E1 | Methodology details
table E1 | Allocation factors for the most important by-products of the four biofuel production systems, based on the market values of the main products and by-products. We assumed that farm management did not influence the ratio between main products and by-products, so the same factors were used for all three management strategies. feedstock phase1 by-product(s) use(s) allocation source factor (%)2 corn cultivation stover animal feed 12 Luo et al. (2009) corn processing DDGS animal feed 23 Wang et al. (2011) rapeseed cultivation straw animal feed 3 Bernesson (2004) rapeseed processing meal + glycerine animal feed + chemical 26 Bernesson (2004) soybean processing meal + glycerine animal feed + chemical 57 Wang et al. (2011) sugarcane processing bagasse electricity production 7 Renó et al. (2011), Renouf et al. (2011) 1 Cultivation refers to by-products that are produced on the field and gathered during harvest; Processing refers to by-products that are produced during crop-to-biofuel processing. 2 Factors show the percentage of emissions that are allocated to the by-product(s). 172 Appendices ) -1 kg crop kg 3 water used3 water (m 1.7E-01 (CHN) 1.7E-01 (USA) 1.7E-01 1.6E-04 - 1.5E-04 - 2 ) -1 MJ 2 3.1E-05 - 3.1E-05 - land transformed (m 2 ) -1 year MJ year 2 based on spatially explicit yields based on spatially explicit - (FRA) 2.3E-01 (GER) 8.4E-02 (RoW) 1.5E-01 - (BRA) 1.7E-03 (USA) 9.1E-03 - (BRA) 1.9E-02 - 2.9E-04 - 2.8E-04 - (m land occupied
1000 ) -1 -eq MJ 2 GHG emissions GWP 1.3E-02 1.8E-02 9.7E-03 7.3E-03 5.9E-03 1.0E-02 2.5E-03 2.0E-02 1.6E-02 7.0E-02 1.0E-02 7.0E-02 (kg CO (kg 100 ) -1 -eq MJ 2 1.8E-02 2.0E-02 1.7E-02 8.1E-03 1.3E-02 1.1E-02 4.6E-03 2.1E-02 1.8E-02 7.0E-02 1.2E-02 7.0E-02 GHG emissions GWP CO (kg 1 RoW RoW EU EU EU RoW RoW RoW RoW Brazil Brazil EU country Summary of data collected or derived from the ecoinvent database (Weidema et al. 2013). database (Weidema the ecoinvent from or derived | Summary data collected of corn cultivation corn corn processing corn combustion petrol diesel production diesel combustion rapeseed cultivation rapeseed processing rapeseed cultivation soybean processing soybean cultivation sugarcane processing sugarcane production petrol feedstock/fuel- feedstock/fuel- phase cycle life Calculated as the net difference between the amount of surface water that is withdrawn and the amount of water that flows back to the surface water. to the surface that flows back water and the amount of that is withdrawn water surface the amount of between as the net difference Calculated Over 30 categories of land use are distinguished in ecoinvent. distinguished in ecoinvent. land use are of 30 categories Over RoW = Rest of the World, which is a weighted average of multiple producing countries. multiple producing of average which is a weighted the World, of = Rest RoW table E2 table 1 2 3 173
table E3 | Ecoregions located in the biofuel producing countries, with their respective land occupation and land transformation biodiversity impact factors (BF) from Chaudhary and Brooks (2018). Ecoregion codes are in accordance with Olson et al. (2001)
country ecoregion code BFocc BFocc BFtrans BFtrans minimal use intense use minimal use intense use (PDF / m2) (PDF / m2) (PDF∙year / m2) (PDF∙year / m2) Austria Central European mixed forests PA0412 1.50E-14 1.75E-14 6.40E-12 7.42E-12 Pannonian mixed forests PA0431 1.92E-14 2.28E-14 8.16E-12 9.68E-12 Western European broadleaf forests PA0445 1.63E-14 1.90E-14 6.92E-12 8.07E-12 Alps conifer and mixed forests PA0501 7.90E-14 9.39E-14 3.03E-11 3.59E-11
Brazil Araucaria moist forests NT0101 4.80E-13 4.89E-13 4.07E-11 4.14E-11 Atlantic Coast restingas NT0102 1.52E-12 1.78E-12 1.26E-10 1.47E-10 Bahia coastal forests NT0103 1.44E-12 1.50E-12 1.22E-10 1.27E-10 Bahia interior forests NT0104 6.10E-13 6.21E-13 5.21E-11 5.30E-11 Caatinga Enclaves moist forests NT0106 1.70E-13 1.97E-13 1.44E-11 1.66E-11 Caqueta moist forests NT0107 1.56E-13 1.65E-13 1.32E-11 1.40E-11 Guayanan Highlands moist forests NT0124 4.63E-13 4.77E-13 3.91E-11 4.03E-11 Guianan moist forests NT0125 2.20E-13 2.25E-13 1.87E-11 1.92E-11 Gurupa varzea NT0126 3.85E-13 4.20E-13 3.23E-11 3.51E-11 Iquitos varzea NT0128 3.43E-13 3.50E-13 2.91E-11 2.97E-11 Japura-Solimoes-Negro moist forests NT0132 1.55E-13 1.60E-13 1.32E-11 1.36E-11 Jurua-Purus moist forests NT0133 1.64E-13 1.68E-13 1.40E-11 1.43E-11 Madeira-Tapajos moist forests NT0135 1.95E-13 1.98E-13 1.68E-11 1.70E-11 Marajo Varzea forests NT0138 2.07E-13 2.15E-13 1.77E-11 1.83E-11 Maranhao Babacu forests NT0139 8.48E-14 8.82E-14 7.24E-12 7.52E-12 Mato Grosso tropical dry forests NT0140 1.05E-13 1.07E-13 8.95E-12 9.12E-12 Monte Alegre varzea NT0141 2.85E-13 2.96E-13 2.42E-11 2.51E-11 Negro-Branco moist forests NT0143 1.74E-13 1.79E-13 1.48E-11 1.52E-11 Northeastern Brazil restingas NT0144 1.21E-13 1.31E-13 1.03E-11 1.11E-11 Parana-Paraiba interior forests NT0150 3.82E-13 3.88E-13 3.26E-11 3.31E-11 Pernambuco coastal forests NT0151 2.50E-12 2.71E-12 2.08E-10 2.25E-10 Pernambuco interior forests NT0152 8.75E-13 8.97E-13 7.39E-11 7.57E-11 Purus varzea NT0156 1.89E-13 1.93E-13 1.61E-11 1.65E-11 Purus-Madeira moist forests NT0157 1.73E-13 1.77E-13 1.47E-11 1.51E-11 Rio Negro campinarana NT0158 1.71E-13 1.76E-13 1.46E-11 1.50E-11 Serra do Mar coastal forests NT0160 2.28E-12 2.39E-12 1.93E-10 2.02E-10 Solimoes-Japura moist forest NT0163 2.00E-13 2.07E-13 1.70E-11 1.75E-11 Southwest Amazon moist forests NT0166 2.82E-13 2.87E-13 2.40E-11 2.45E-11 Tapajos-Xingu moist forests NT0168 1.71E-13 1.75E-13 1.46E-11 1.49E-11 Tepuis NT0169 1.06E-13 1.07E-12 8.94E-11 9.08E-11 Tocantins-Araguaia-Maranhao moist forests NT0170 1.60E-13 1.64E-13 1.37E-11 1.40E-11 Uatuma-Trombetas moist forests NT0173 1.50E-13 1.54E-13 1.28E-11 1.31E-11 Xingu-Tocantins-Araguaia moist forests NT0180 1.55E-13 1.59E-13 1.32E-11 1.35E-11 Guianan piedmont and lowland moist forests NT0182 2.45E-13 2.50E-13 2.08E-11 2.12E-11 Atlantic dry forests NT0202 1.91E-13 1.99E-13 1.70E-11 1.78E-11 Chiquitano dry forests NT0212 1.71E-13 1.75E-13 1.54E-11 1.58E-11 Campos Rupestres montane savanna NT0703 9.70E-13 9.77E-13 6.11E-11 6.15E-11 Cerrado NT0704 1.09E-13 1.10E-13 6.86E-12 6.92E-12 Guyanan savanna NT0707 2.14E-13 2.16E-13 1.34E-11 1.35E-11 Humid Chaco NT0708 1.03E-13 1.04E-13 6.53E-12 6.57E-12 Uruguayan savanna NT0710 1.47E-13 1.48E-13 9.29E-12 9.35E-12 Pantanal NT0907 9,49E-14 9.58E-14 8.57E-12 8.65E-12 Caatinga NT1304 9.99E-14 1.01E-13 6.56E-12 6.61E-12 Alvarado mangroves NT1401 4.05E-13 4.12E-13 5.06E-11 5.15E-11 Belizean Reef mangroves NT1406 2.24E-12 2.28E-12 2.80E-10 2.85E-10 174 Appendices
China Jian Nan subtropical evergreen forests IM0118 1.33E-13 1.44E-13 1.69E-11 1.83E-11 Northern Indochina subtropical forests IM0137 3.99E-13 4.24E-13 5.13E-11 5.45E-11 South China-Vietnam subtropical evergreen IM0149 2.80E-13 3.06E-13 3.59E-11 3.93E-11 forests Hainan Island monsoon rain forests IM0169 1.35E-12 1.48E-12 1.73E-10 1.90E-10 South Taiwan monsoon rain forests IM0171 4.54E-12 5.67E-12 5.53E-10 6.90E-10 Taiwan subtropical evergreen forests IM0172 1.68E-12 1.87E-12 2.14E-10 2.38E-10 Eastern Himalayan broadleaf forests IM0401 7.01E-13 7.50E-13 1.83E-10 1.95E-10 Eastern Himalayan subalpine conifer forests IM0501 6.66E-13 7.15E-13 1.98E-10 2.12E-10 Gizhou Plateau broadleaf and mixed forests PA0101 1.71E-13 1.82E-13 4.01E-11 4.27E-11 Yunnan Plateau subtropical evergreen forests PA0102 2.34E-13 2.50E-13 5.50E-11 5.87E-11 Central China loess plateau mixed forests PA0411 5.53E-14 6.00E-14 2.32E-11 2.52E-11 Changbai Mountains mixed forests PA0414 4.42E-14 4.81E-14 1.86E-11 2.03E-11 Changjiang Plain evergreen forests PA0415 9.42E-14 1.01E-13 3.95E-11 4.25E-11 Daba Mountains evergreen forests PA0417 1.58E-13 1.68E-13 6.66E-11 7.06E-11 Huang He Plain mixed forests PA0424 4.56E-14 4.93E-14 1.92E-11 2.08E-11 Manchurian mixed forests PA0426 4.88E-14 5.20E-14 2.06E-11 2.19E-11 Northeast China Plain deciduous forests PA0430 3.65E-14 3.93E-14 1.52E-11 1.64E-11 Qin Ling Mountains deciduous forests PA0434 2.03E-13 2.19E-13 8.57E-11 9.22E-11 Sichuan Basin evergreen broadleaf forests PA0437 1.46E-13 1.55E-13 6.10E-11 6.45E-11 Altai montane forest and forest steppe PA0502 2.90E-14 3.18E-14 1.13E-11 1.23E-11 Da Hinggan-Dzhagdy Mountains conifer forests PA0505 1.77E-14 1.91E-14 6.72E-12 7.27E-12 Helanshan montane conifer forests PA0508 1.64E-13 1.70E-13 6.51E-11 6.74E-11 Hengduan Mountains subalpine conifer forests PA0509 3.64E-13 3.98E-13 1.38E-10 1.51E-11 Northeastern Himalayan subalpine conifer PA0514 6.16E-13 6.51E-13 2.33E-10 2.46E-10 forests Nujiang Langcang Gorge alpine conifer and PA0516 6.74E-13 7.31E-13 2.60E-10 2.81E-10 mixed forests Qilian Mountains conifer forests PA0517 2.47E-13 2.60E-13 9.56E-11 1.00E-10 Qionglai-Minshan conifer forests PA0518 8.79E-13 9.51E-13 3.34E-10 3.61E-10 Tian Shan montane conifer forests PA0521 6.65E-14 7.76E-14 2.50E-11 2.91E-11 East Siberian taiga PA0601 8.87E-15 9.37E-15 5.06E-12 5.34E-12 Altai steppe and semi-desert PA0802 3.03E-14 3.13E-14 6.54E-12 6.75E-12 Daurian forest steppe PA0804 2.15E-14 2.20E-14 4.59E-12 4.71E-12 Emin Valley steppe PA0806 2.43E-14 2.51E-14 5.19E-12 5.36E-12 Mongolian-Manchurian grassland PA0813 1.90E-14 1.94E-14 4.03E-12 4.12E-12 Tian Shan foothill arid steppe PA0818 3.46E-14 3.64E-14 7.29E-12 7.64E-12 Amur meadow steppe PA0901 2.28E-14 2.36E-14 4.50E-12 4.65E-12 Bohai Sea saline meadow PA0902 2.26E-14 2.42E-14 4.39E-12 4.68E-12 Nenjiang River grassland PA0903 2.59E-14 2.75E-14 5.05E-12 5.35E-12 Ussuri-Wusuli meadow and forest meadow PA0907 4.93E-14 5.13E-14 9.75E-12 1.01E-11 Yellow Sea saline meadow PA0908 3.01E-13 3.21E-13 6.18E-11 6.59E-11 Altai alpine meadow and tundra PA1001 2.77E-14 2.87E-14 5.55E-12 5.74E-12 Central Tibetan Plateau alpine steppe PA1002 2.03E-14 2.05E-14 4.03E-12 4.08E-12 Eastern Himalayan alpine shrub and meadows PA1003 1.79E-13 1.83E-13 3.53E-11 3.59E-11 Karakoram-West Tibetan Plateau alpine steppe PA1006 7.21E-14 7.30E-14 1.46E-11 1.47E-11 Ordos Plateau steppe PA1013 2.72E-14 2.79E-14 5.38E-12 5.51E-12 Qilian Mountains subalpine meadow PA1015 2.59E-14 2.63E-14 5.14E-12 5.22E-12 Southeast Tibet shrublands and meadow PA1017 1.02E-13 1.03E-13 2.02E-11 2.04E-11 Tian Shan montane steppe and meadow PA1019 3.10E-14 3.24E-14 6.13E-12 6.38E-12 Tibetan Plateau alpine shrublands and meadows PA1020 3.13E-14 3.17E-14 6.20E-12 6.28E-12 Western Himalayan alpine shrub and Meadows PA1021 9.09E-14 9.49E-14 1.77E-11 1.85E-11 Yarlung Zambo arid steppe PA1022 5.69E-14 5.80E-14 1.12E-11 1.14E-11 Alashan Plateau semi-desert PA1302 1.33E-14 1.35E-14 1.95E-12 1.98E-12 Eastern Gobi desert steppe PA1314 1.29E-14 1.31E-14 1.89E-12 1.92E-12 Junggar Basin semi-desert PA1317 1.95E-14 2.00E-14 2.87E-12 2.95E-12 Qaidam Basin semi-desert PA1324 1.34E-14 1.36E-14 1.96E-12 1.98E-12 Taklimakan desert PA1330 1.16E-14 1.21E-14 1.69E-12 1.75E-12 175
France Atlantic mixed forests PA0402 1.76E-14 2.07E-14 7.44E-12 8.73E-12 Cantabrian mixed forests PA0406 7.26E-14 8.94E-14 3.07E-11 3.78E-11 Pyrenees conifer and mixed forests PA0433 2.32E-13 2.93E-13 9.49E-11 1.20E-10 Western European broadleaf forests PA0445 1.63E-14 1.90E-14 6.92E-12 8.07E-12 Alps conifer and mixed forests PA0501 7.90E-14 9.39E-14 3.03E-11 3.59E-11 Corsican montane broadleaf and mixed forests PA1204 1.04E-12 1.29E-12 2.04E-10 2.55E-10 Italian sclerophyllous and semi-deciduous PA1211 8.69E-14 9.71E-14 1.74E-11 1.94E-11 forests Northeastern Spain & Southern France Mediter- PA1215 7.53E-14 8.43E-14 1.50E-11 1.68E-11 ranean forests Tyrrhenian-Adriatic Sclerophyllous and mixed PA1222 2.07E-13 2.44E-13 4.19E-11 4.92E-11 forests
Germany Atlantic mixed forests PA0402 1.76E-14 2.07E-14 7.44E-12 8.73E-12 Baltic mixed forests PA0405 1.10E-14 2.07E-14 4.57E-12 5.54E-12 Central European mixed forests PA0412 1.50E-14 1.75E-14 6.40E-12 7.42E-12 Western European broadleaf forests PA0445 1.63E-14 1.90E-14 6.92E-12 8.07E-12 Alps conifer and mixed forests PA0501 7.90E-14 9.39E-14 3.03E-11 3.59E-11
Italy Appenine deciduous montane forests PA0401 8.16E-14 9.70E-14 3.42E-11 4.05E-11 Dinaric Mountains mixed forests PA0418 7.24E-14 8.66E-14 3.07E-11 3.66E-11 Po Basin mixed forests PA0432 6.10E-14 7.10E-14 2.60E-11 3.03E-11 Alps conifer and mixed forests PA0501 7.90E-14 9.39E-14 3.03E-11 3.59E-11 Illyrian deciduous forests PA1210 1.66E-13 1.88E-13 3.31E-11 3.74E-11 Italian sclerophyllous and semi-deciduous PA1211 8.69E-14 9.71E-14 1.74E-11 1.94E-11 forests Northeastern Spain & Southern France Mediter- PA1215 7.53E-14 8.43E-14 1.50E-11 1.68E-11 ranean forests South Appenine mixed montane forests PA1218 2.43E-13 2.82E-13 4.76E-11 5.52E-11 Tyrrhenian-Adriatic Sclerophyllous and mixed PA1222 2.07E-13 2.44E-13 4.19E-11 4.92E-11 forests
Poland Baltic mixed forests PA0405 1.10E-14 2.07E-14 4.57E-12 5.54E-12 Central European mixed forests PA0412 1.50E-14 1.75E-14 6.40E-12 7.42E-12 Western European broadleaf forests PA0445 1.63E-15 1.90E-14 6.92E-12 8.07E-12 Carpathian montane conifer forests PA0504 3.33E-14 4.13E-14 1.26E-11 1.55E-11
United Sierra Madre Occidental pine-oak forests NA0302 3.23E-13 3.50E-13 6.40E-11 6.93E-11 States of Sierra Madre Oriental pine-oak forests NA0303 6.73E-13 7.46E-13 1.34E-10 1.48E-10 America Allegheny Highlands forests NA0401 3.84E-14 4.17E-14 1.65E-11 1.79E-11 Appalachian mixed mesophytic forests NA0402 6.71E-14 7.06E-14 2.89E-11 3.04E-11 Appalachian/Blue Ridge forests NA0403 1.45E-13 1.49E-13 6.21E-11 6.37E-11 Central U.S. hardwood forests NA0404 5.00E-14 5.38E-14 2.15E-11 2.31E-11 East Central Texas forests NA0405 8.64E-14 9.33E-14 3.72E-11 4.01E-11 Eastern forest/boreal transition NA0406 1.97E-14 2.08E-14 8.48E-12 8.96E-12 Eastern Great Lakes lowland forests NA0407 3.09E-14 3.32E-14 1.33E-11 1.42E-11 Mississippi lowland forests NA0409 7.77E-14 8.34E-14 3.32E-11 3.57E-11 New England/Acadian forests NA0410 3.00E-14 3.15E-14 1.29E-11 1.36E-11 Northeastern coastal forests NA0411 6.59E-14 6.97E-14 2.85E-11 3.01E-11 Ozark Mountain forests NA0412 1.27E-13 1.33E-13 5.44E-11 5.67E-11 Southeastern mixed forests NA0413 7.12E-14 7.59E-14 3.05E-11 3.25E-11 Southern Great Lakes forests NA0414 3.84E-14 4.16E-14 1.65E-11 1.78E-11 Upper Midwest Forest/Savanna transition zone NA0415 3.10E-14 3.34E-14 1.33E-11 1.43E-11 Western Great Lakes forests NA0416 2.68E-14 2.83E-14 1.15E-11 1.21E-11 Willamette Valley forests NA0417 2.58E-13 2.74E-13 1.14E-10 1.21E-10 Arizona Mountains forests NA0503 1.35E-13 1.43E-13 5.07E-11 5.36E-11 Atlantic coastal pine barrens NA0504 1.02E-13 1.10E-13 3.72E-11 4.00E-11 Blue Mountains forests NA0505 6.50E-14 6.94E-14 2.43E-11 2.59E-11 British Columbia mainland coastal forests NA0506 3.93E-14 4.13E-14 1.45E-11 1.52E-11 176 Appendices
United Cascade Mountains leeward forests NA0507 4.43E-14 4.80E-14 1.63E-11 1.76E-11 States of Central and Southern Cascades forests NA0508 1.52E-13 1.58E-13 5.69E-11 5.90E-11 America Central Pacific coastal forests NA0510 1.59E-13 1.64E-13 5.97E-11 6.18E-11 Colorado Rockies forests NA0511 6.52E-14 6.94E-14 2.40E-11 2.55E-11 Eastern Cascades forests NA0512 8.49E-14 9.02E-14 3.16E-11 3.35E-11 Florida sand pine scrub NA0513 2.96E-13 3.38E-13 1.08E-10 1.23E-10 Great Basin montane forests NA0515 2.35E-13 2.55E-13 8.80E-11 9.54E-11 Klamath-Siskiyou forests NA0516 2.04E-13 2.13E-13 7.59E-11 7.95E-11 Middle Atlantic coastal forests NA0517 1.13E-13 1.21E-13 4.16E-11 4.45E-11 North Central Rockies forests NA0518 3.22E-14 3.40E-14 1.19E-11 1.26E-11 Northern California coastal forests NA0519 2.71E-13 2.85E-13 1.01E-10 1.06E-10 Northern Pacific coastal forests NA0520 3.87E-14 4.03E-14 1.43E-11 1.49E-11 Okanogan dry forests NA0522 3.80E-14 4.16E-14 1.39E-11 1.52E-11 Piney Woods forests NA0523 6.69E-14 7.49E-14 2.45E-11 2.75E-11 Puget lowland forests NA0524 1.49E-13 1.58E-13 5.51E-11 5.84E-11 Sierra Nevada forests NA0527 2.41E-13 2.51E-13 8.94E-11 9.29E-11 South Central Rockies forests NA0528 4.04E-14 4.32E-14 1.50E-11 1.60E-11 Southeastern conifer forests NA0529 1.42E-13 1.54E-13 5.23E-11 5.64E-11 Wasatch and Uinta montane forests NA0530 6.66E-14 7.18E-14 2.47E-11 2.66E-11 Alaska Peninsula montane taiga NA0601 2.62E-14 2.72E-14 1.24E-11 1.29E-11 Cook Inlet taiga NA0603 1.69E-14 1.77E-14 7.97E-12 8.37E-12 Copper Plateau taiga NA0604 1.49E-14 1.57E-14 7.06E-12 7.45E-12 Interior Alaska/Yukon lowland taiga NA0607 1.35E-14 1.38E-14 6.44E-12 6.60E-12 Western Gulf coastal grasslands NA0701 1.18E-13 1.21E-13 1.04E-11 1.07E-11 California Central Valley grasslands NA0801 2.68E-13 2.84E-13 6.06E-11 6.40E-11 Canadian Aspen forests and parklands NA0802 1.54E-14 1.56E-14 3.46E-12 3.52E-12 Central and Southern mixed grasslands NA0803 5.02E-14 5.18E-14 1.14E-11 1.17E-11 Central forest/grasslands transition zone NA0804 4.29E-14 4.44E-14 9.69E-12 1.00E-11 Central tall grasslands NA0805 2.79E-14 2.88E-14 6.29E-12 6.48E-12 Edwards Plateau savanna NA0806 1.66E-13 1.70E-13 3.74E-11 3.82E-11 Flint Hills tall grasslands NA0807 4.68E-14 4.95E-14 1.05E-11 1.11E-11 Montana Valley and Foothill grasslands NA0808 3.32E-14 3.42E-14 7.47E-12 7.70E-12 Nebraska Sand Hills mixed grasslands NA0809 3.77E-14 3.90E-14 8.48E-12 8.77E-12 Northern mixed grasslands NA0810 2.63E-14 2.68E-14 5.92E-12 6.03E-12 Northern short grasslands NA0811 2.99E-14 3.04E-14 6.78E-12 6.89E-12 Northern tall grasslands NA0812 2.44E-14 2.50E-14 5.47E-12 5.62E-12 Palouse grasslands NA0813 4.33E-14 4.52E-14 9.76E-12 1.02E-11 Texas blackland prairies NA0814 1.59E-13 1.61E-13 3.56E-11 3.62E-11 Western short grasslands NA0815 4.39E-14 4.52E-14 9.95E-12 1.02E-11 Alaska/St. Elias Range tundra NA1101 1.49E-14 1.53E-14 5.68E-12 5.86E-12 Aleutian Islands tundra NA1102 4.08E-13 4.19E-13 1.58E-10 1.62E-10 Beringia lowland tundra NA1106 3.88E-14 4.01E-14 1.47E-11 1.52E-11 Beringia upland tundra NA1107 5.41E-14 5.59E-14 2.05E-11 2.12E-11 Pacific Coastal Mountain icefields and tundra NA1117 3.09E-14 3.17E-14 1.19E-11 1.22E-11 California coastal sage and chaparral NA1201 2.71E-13 2.88E-13 5.48E-11 5.80E-11 California interior chaparral and woodlands NA1202 2.30E-13 2.42E-13 4.63E-11 4.86E-11 California montane chaparral and woodlands NA1203 3.78E-13 4.07E-13 7.53E-11 8.08E-11 Chihuahuan desert NA1303 8.38E-14 8.82E-14 1.50E-11 1.58E-11 Colorado Plateau shrublands NA1304 4.68E-14 4.78E-14 8.41E-12 8.59E-12 Great Basin shrub steppe NA1305 3.68E-14 3.76E-14 6.62E-12 6.76E-12 Mojave desert NA1308 7.76E-14 8.10E-14 1.40E-11 1.46E-11 Snake/Columbia shrub steppe NA1309 4.01E-14 4.10E-14 7.27E-12 7.43E-12 Sonoran desert NA1310 8.52E-14 8.99E-14 1.53E-11 1.61E-11 Tamaulipan mezquital NA1312 8.47E-14 8.84E-14 1.51E-11 1.57E-11 Wyoming Basin shrub steppe NA1313 3.10E-14 3.17E-14 5.58E-12 5.70E-12 South Florida rocklands NT0164 1.77E-12 2.25E-12 1.45E-10 1.83E-10 Everglades NT0904 2.73E-13 3.07E-13 2.39E-11 2.68E-11 Hawaii tropical moist forests OC0106 1.15E-11 1.18E-11 1.32E-09 1.36E-09 Hawaii tropical dry forests OC0202 5.47E-12 5.76E-12 4.25E-10 4.47E-10 Hawaii tropical high shrublands OC0701 6.00E-12 6.27E-12 7.12E-10 7.42E-10 Hawaii tropical low shrublands OC0702 4.09E-12 4.87E-12 4.73E-10 5.62E-10
Global average1 - 1.60E-13 1.99E-11 1.70E-13 2.13E-11 1 global averages were provided by dr. Chaudhary (personal communication; 30-04-2018) 177
table E4 | Country-specific biodiversity impact factors for water stress (BFWS) from http://lc-impact.eu (Verones et al. 2016).
country BFWS (PDF∙year / m3) Austria 1.60E-14 Brazil 2.76E-15 China 2.32E-15 France 6.19E-16 Germany 4.21E-15 Italy 3.41E-15 Poland 4.30E-16 United States of America 1.15E-12 Global average 1.63E-13
table E5 | Biofuel and fossil fuel densities, used to convert kilograms to litre before calculating the impact of fuel blends. Values are based on Atabani et al. (2012) and Yüksel and Yüksel (2004). fuel type kg fuel / litre bioethanol 0.79 biodiesel 0.88 petrol 0.74 diesel 0.83 178 Appendices
E2 | Additional results
A 179
B
figure E1 | Contributions of water use, GHG emissions and land use to the total biodiversity impact. Contributions are shown for (A) a scenario with both land transformation and land occupation, and (B) a scenario with only occupation. The boxes show the first quartile, median, and third quartile, and the ends of the whiskers show the 10th and 90th percentiles of the grid-specific impacts. 180 Appendices
A
B
figure E2 | Global relative species loss due to bioethanol and biodiesel calculated for two scenarios: (A) 30-year plantation time and 1000-year time horizon; and (B) 100-year plantation time and 1000-year time horizon. Impacts are given for a scenario with both land transformation and land occupation (total) and a scenario with only occupation. The boxes show the first quartile, median, and third quartile, and the ends of the whiskers show the 10th and 90th percentiles of the grid- specific impacts. The dashed line shows the impact of the fossil alternatives, i.e., gasoline (upper graph) and diesel (lower graph). 181
A
B
figure E3 | Global relative species loss due to production of various common fossil fuel-biofuel blends, calculated for three scenarios: (A) 30-year plantation time and 1000-year time horizon; (B) 100-year plantation time and 100-year time horizon; and (c) 100-year plantation time and 1000- year time horizon. Only combined impacts of occupation and transformation are shown. The boxes show the first quartile, median, and third quartile, and the ends of the whiskers show the 10th and 90th percentiles of the grid-specific impacts. 182 Appendices
table E6 | Explained variance of five variables and the residual to the total biodiversity impact, based on an ANOVA’s sum of squares.
variable explained variance country 17.0% crop type 0.5% management type 10.8% plantation time 4.2% time horizon 0.3% location (residual) 67.3% 183
Literature
Achten, W. M. J., J. Almeida, V. Fobelets, E. Bolle, E. Mathijs, V. P. Singh, D. N. Tewari, L. V. Verchot and B. Muys (2010). “Life cycle assessment of Jatropha biodiesel as transportation fuel in rural India.” Applied Energy 87(12): 3652-3660. Adler, P. R., S. J. D. Grosso and W. J. Parton (2007). “Life-cycle assessment of net greenhouse-gas flux for bioenergy cropping systems.” Ecological Applications 17(3): 675-691. Ajanovic, A. and R. Haas (2014). “On the future prospects and limits of biofuels in Brazil, the US and EU.” Applied Energy 135: 730-737. Alkemade, R., M. Van Oorschot, L. Miles, C. Nellemann, M. Bakkenes and B. Ten Brink (2009). “GLOBIO3: A framework to investigate options for reducing global terrestrial biodiversity loss.” Ecosystem 12: 374-390. Anderson, W., L. You, S. Wood, U. Wood‐Sichra and W. Wu (2014). “An analysis of methodological and spatial differences in global cropping systems models and maps.” Global Ecology and Biogeography 24(2): 180-191. Andow, D. A. (1991). “Vegetational diversity and arthropod population response.” Annual Review of Entomology 36: 561-586. Angelo, C. (2012). “Growth of ethanol fuel stalls in Brazil.” Nature 491: 646-647. Araújo, M. B. and C. Rahbek (2006). “How does climate change affect biodiversity?” Science 313(5792): 1396-1397. Aro, E.-M. (2016). “From first generation biofuels to advanced solar biofuels.” Ambio 45(Suppl. 1): S24-S31. Atabani, A. E., A. S. Silitonga, I. A. Badruddin, T. M. I. Mahlia, H. H. Masjuki and S. Mekhilef (2012). “A comprehensive review on biodiesel as an alternative energy resource and its characteristics.” Renewable and Sustainable Energy Reviews 16(4): 2070-2093. Bare, J. (2011). “Recommendation for land use impact assessment: first steps into framework, theory, and implementation.” Clean Technologies and Environmental Policy 13: 7-18. Bartholome, E. and A. S. Belward (2005). “GLC2000: A new approach to global land cover mapping from Earth observation data.” International Journal of Remote Sensing 26(9): 1959-1977. Basedow, T. (1998). “The species composition and frequency of spiders (Araneae) in fields of winter wheat grown under different conditions in Germany.” Journal of Applied Entomology 122: 585-590. Bellamy, P. E., P. J. Croxton, M. S. Heard, S. A. Hinsley, L. Hulmes, S. Hulmes, P. Nuttall, R. F. Pywell and P. Rothery (2009). “The impact of growing miscanthus for biomass on farmland bird populations.” Biomass and Bioenergy 33(2): 191-199. Bellard, C., C. Bertelsmeier, P. Leadley, W. Thuiller and F. Courchamp (2012). “Impacts of climate change on the future of biodiversity.” Ecology Letters 15(4): 365-377. 184 Literature
Benton, T. G., J. A. Vickery and J. D. Wilson (2003). “Farmland biodiversity: Is habitat heterogeneity the key?” Trends in Ecology & Evolution 18(4): 182-188. Berger, M., S. Pfister, V. Bach and M. Finkbeiner (2015). “Saving the planet’s climate or water resources? The trade-off between carbon and water footprints of European biofuels.” Sustainability 7(6): 6665. Bergthorson, J. M. and M. J. Thomson (2015). “A review of the combustion and emissions properties of advanced transportation biofuels and their impact on existing and future engines.” Renewable and Sustainable Energy Reviews 42: 1393-1417. Beringer, T. I. M., W. Lucht and S. Schaphoff (2011). “Bioenergy production potential of global biomass plantations under environmental and agricultural constraints.” GCB Bioenergy 3(4): 299-312. Bernesson, S. (2004). Life cycle assessment of rapeseed oil, rape methyl ester and ethanol as fuels - A comparison between large- and small-scale production. Uppsala, Swedish University of Agricultural Sciences, Department of Biometry and Engineering. ISSN 1652 3237. Bernesson, S. (2004). Life cycle assessment of rapeseed oil, rape methyl ester and ethanol as fuels: A comparison between large- and small-scale production. Uppsala, Sveriges lantbruksuniversiteit. Blanco-Canqui, H. and R. Lal (2007). “Soil and crop response to harvesting corn residues for biofuel production.” Geoderma 141(3–4): 355-362. Booij, C. J. H. and J. Noorlander (1992). “Farming systems and insect predators.” Agriculture, Ecosystems and Environment 40: 125-135. Borchers, A., E. Truex-Powell, S. Wallander and C. Nickerson (2014). Multi-Cropping Practices: Recent Trends in Double Cropping, U.S. Department of Agriculture, Economic Research Service. Borjesson, P. and L. M. Tufvesson (2011). “Agricultural crop-based biofuels - resource efficiency and environmental performance including direct land use changes.” Journal of Cleaner Production 19(2-3): 108-120. Boutin, C., P. A. Martin and A. Baril (2009). “Arthropod diversity as affected by agricultural agricultural management (organic and conventional farming), plant species, and landscape context.” Ecoscience 16(4): 492-501. Brentrup, F., J. Küsters, H. Kuhlmann and J. Lammel (2004). “Environmental impact assessment of agricultural production systems using the life cycle assessment methodology. I. Theoretical concept of a LCA method tailored to crop production.” European Journal of Agronomy 20: 247-264. Brentrup, F., J. Küsters, J. Lammel and H. Kuhlmann (2002). “Life cycle impact assessment of land use based on the hemeroby concept.” International Journal of Life Cycle Assessment 7(6): 339-348. Broch, A., S. K. Hoekman and S. Unnasch (2013). “A review of variability in indirect land use change assessment and modeling in biofuel policy.” Environmental Science & Policy 29: 147-157. 185
Buckland, H. (2005). The oil for ape scandal: How palm oil is threatening orang-utan survival, Friends of the Earth, The Ape Alliance, The Borneo Orangutan Survival Foundation, The Orangutan Foundation (UK) and the Sumatran Orangutan Society. Bullock, D. G. (1992). “Crop rotation.” Critical Reviews in Plant Sciences 11(4): 309-326. Burke, I. C., C. M. Yonker, W. J. Parton, C. V. Cole, K. Flach and D. S. Schimel (1989). “Texture, climate and cultivation effects on soil organic matter content in U.S. grassland soils.” Soil Sci. Soc. Am. J. 53: 800-805. Buyanovsky, G. A., J. R. Brown and G. H. Wagner (1996). Sanborn Field: Effect of 100 Years of Cropping on Soil Parameters Influencing Productivity. Soil Organic Matter in Temperate Agroecosystems: Long-Term Experiments in North America. E. A. Paul, E. T. Elliott, K. Paustian and C. V. Cole. Boca Raton, Florida, US, CRC Press: 205-224. Carroll, J. (2013). Exxon at least 25 years away from making fuel from algae. Bloomberg. Castanheira, É. G. and F. Freire (2013). “Greenhouse gas assessment of soybean production: implications of land use change and different cultivation systems.” Journal of Cleaner Production 54: 49-60. Cavalett, O. and E. Ortega (2010). “Integrated environmental assessment of biodiesel production from soybean in Brazil.” Journal of Cleaner Production 18(1): 55-70. Chapman, L. (2007). Transport and Climate Change: A Review. Chaudhary, A. and T. M. Brooks (2018). “Land use intensity-specific global characterization factors to assess product biodiversity footprints.” Environmental Science & Technology. Chaudhary, A., F. Verones, L. de Baan and S. Hellweg (2015). “Quantifying land use impacts on biodiversity: Combining species-area models and vulnerability indicators.” Environmental Science & Technology 49(16): 9987-9995. Cherubini, F., N. D. Bird, A. Cowie, G. Jungmeier, B. Schlamadinger and S. Woess-Gallasch (2009). “Energy- and greenhouse gas-based LCA of biofuel and bioenergy systems: Key issues, ranges and recommendations.” Resources, Conservation and Recycling 53(8): 434-447.
Cherubini, F., G. P. Peters, T. Berntsen, A. H. Strømman and E. Hertwich (2011). “CO2 emissions from biomass combustion for bioenergy: atmoshperic decay and contribution to global warming.” GCB Bioenergy 3(1): 413-426. Cherubini, F. and A. H. Strømman (2011). “Life cycle assessment of bioenergy systems: State of the art and future challenges.” Bioresource Technology 102(2): 437-451. Cody, M. L. (1981). “Habitat selection in birds: The roles of vegetation structure, competitors, and productivity.” BioScience 31(2): 107-113. Conant, R. T., K. Paustian and E. T. Elliott (2001). “Grassland management and conversion into grassland: Effects on soil carbon.” Ecological Applications 11(2): 343-355. Cook, W. M. and S. H. Faeth (2006). “Irrigation and land use drive ground arthropod community patterns in an urban desert.” Environmental Entomology 35(6): 1532-1540. Correa, D. F., H. L. Beyer, H. P. Possingham, S. R. Thomas-Hall and P. M. Schenk (2017). “Biodiversity impacts of bioenergy production: Microalgae vs. first generation biofuels.” Renewable and Sustainable Energy Reviews 74: 1131-1146. 186 Literature
Creutzig, F., N. H. Ravindranath, G. Berndes, S. Bolwig, R. Bright, F. Cherubini, H. Chum, E. Corbera, M. Delucchi, A. Faaij, J. Fargione, H. Haberl, G. Heath, O. Lucon, R. Plevin, A. Popp, C. Robledo-Abad, S. Rose, P. Smith, A. Stromman, S. Suh and O. Masera (2014). “Bioenergy and climate change mitigation: an assessment.” GCB Bioenergy 7(5): 916- 944. Curran, M., L. De Baan, A. M. De Schryver, R. Van Zelm, S. Hellweg, T. Köllner, G. Sonnemann and M. A. J. Huijbregts (2011). “Toward meaningful end points of biodiversity in life cycle assessment.” Environmental Science & Technology 45: 70-79. Dale, V. H., K. L. Kline, J. Wiens and J. Fargione (2010). Biofuels: Implications for land use and biodiversity. Biofuels and Sustainability Reports. Washington, DC, U.S.A., ESA: 13. Dalgaard, R., J. Schmidt, N. Halberg, P. Christensen, M. Thrane and W. Pengue (2008). “LCA of soybean meal.” International Journal of Life Cycle Assessment 13(3): 240-254. Danielsen, F., H. Beukema, N. D. Burgess, F. Parish, C. A. Brühl, P. F. Donald, D. Murdiyarso, B. Phalan, L. Reijnders, M. Struebig and E. B. Fitzherbert (2009). “Biofuel plantation on forested lands: Double jeopardy for biodiversity and climate.” Conservation Biology 23(2): 348-358. Daroch, M., S. Geng and G. Wang (2013). “Recent advances in liquid biofuel production from algal feedstocks.” Applied Energy 102: 1371-1381. Davis, S. C., W. J. Parton, S. J. D. Grosso, C. Keough, E. Marx, P. R. Adler and E. H. DeLucia (2012). “Impact of second-generation biofuel agriculture on greenhouse-gas emissions in the corn-growing regions of the US.” Frontiers in Ecology and the Environment 10(2): 69-74.
Davis, S. J., K. Caldeira and H. D. Matthews (2010). “Future CO2 emissions and climate change from existing energy infrastructure.” Science 329(5997): 1330. De Baan, L., R. Alkemade and T. Köllner (2013). “Land use impacts on biodiversity in LCA: A global approach.” International Journal of Life Cycle Assessment 18(6): 1216-1230. De Baan, L., C. L. Mutel, M. Curran, S. Hellweg and T. Köllner (2013). “Land use in Life Cycle Assessment: Global characterization factors based on regional and global potential species extinction.” Environmental Science & Technology 47(16): 9281-9290. De Schryver, A. M., K. W. Brakkee, M. J. Goedkoop and M. A. J. Huijbregts (2009). “Characterization factors for global warming in Life Cycle Assessment based on damages to humans and ecosystems.” Environmental Science & Technology 43(6): 1689-1695. De Schryver, A. M., M. J. Goedkoop, R. S. E. W. Leuven and M. A. J. Huijbregts (2010). “Uncertainties in the application of the species area relationship for characterisation factors of land occupation in life cycle assessment.” International Journal of Life Cycle Assessment 15: 682-691. De Snoo, G. R. (1997). “Arable flora in sprayed and unsprayed crop edges.” Agriculture, Ecosystems and Environment 66: 223-230. 187
De Souza, D. M., D. F. B. Flynn, F. DeClerck, R. K. Rosenbaum, H. De Melo Lisboa and T. Köllner (2013). “Land use impacts on biodiversity in LCA: Proposal of characterization factors based on functional diversity.” International Journal of Life Cycle Assessment 18(6): 1231-1242. De Souza, S. P., S. Pacca, M. T. De Avila and J. L. B. Borges (2010). “Greenhouse gas emissions and energy balance of palm oil biofuel.” Renewable Energy 35(11): 2552-2561. Derpsch, R., T. Friedrich, A. Kassam and L. Hongwen (2010). “Current status of adoption of no-till farming in the world and some of its main benefits.” International Journal of Agriculture and Biological Engineering 3(1): 1-26. Don, A., B. Osborne, A. Hastings, U. Skiba, M. S. Carter, J. Drewer, H. Flessa, A. Freibauer, N. Hyvönen, M. B. Jones, G. J. Lanigan, Ü. Mander, A. Monti, S. N. Djomo, J. Valentine, K. Walter, W. Zegada-Lizarazu and T. Zenone (2011). “Land-use change to bioenergy production in Europe: implications for the greenhouse gas balance and soil carbon.” GCB Bioenergy 4(4): 372-391. Don, A., J. Schumacher and A. Freibauer (2011). “Impact of tropical land-use change on soil organic carbon stocks - a meta-analysis.” Global Change Biology 17: 1658-1670. Donato, D. C., J. B. Kauffman, D. Murdiyarso, S. Kurnianto, M. Stidham and M. Kanninen (2011). “Mangroves among the most carbon-rich forests in the tropics.” Nature Geoscience 4(5): 293-297. Dutta, K., A. Daverey and J.-G. Lin (2014). “Evolution retrospective for alternative fuels: First to fourth generation.” Renewable Energy 69(1): 114-122. Eggert, H. and M. Greaker (2014). “Promoting second generation biofuels: Does the first generation pave the road?” Energies 7(7): 4430. Elshout, P. M. F., R. Van Zelm, J. Balkovic, M. Obersteiner, E. Schmid, R. Skalský, M. Van der Velde and M. A. J. Huijbregts (2015). “Greenhouse-gas payback times for crop-based biofuels.” Nature Climate Change 5(6): 604-610. Elshout, P. M. F., R. van Zelm, R. Karuppiah, I. J. Laurenzi and M. A. J. Huijbregts (2014). “A spatially explicit data-driven approach to assess the effect of agricultural land occupation on species groups.” International Journal of Life Cycle Assessment 19(4): 758-769. Estrada, A. and R. Coates-Estrada (2005). “Diversity of Neotropical migratory landbird species assemblages in forest fragments and man-made vegetation in Los Tuxtlas, Mexico.” Biodiversity and Conservation 14: 1719-1734. Ewert, F., M. D. A. Rounsevell, I. Reginster, M. J. Metzger and R. Leemans (2005). “Future scenarios of European agricultural land use: Estimating changes in crop productivity.” Agriculture, Ecosystems & Environment 107(2–3): 101-116. Faaij, A. P. C. (2006). “Bio-energy in Europe: changing technology choices.” Energy Policy 34(3): 322-342. FAO, IIASA, ISRIC, ISSCAS and JRC (2012). Harmonized World Soil Database (version 1.2). Rome, Italy and IIASA, Laxenburg, Austria, FAO. Fargione, J., J. Hill, D. Tilman, S. Polasky and P. Hawthorne (2008). “Land clearing and the biofuel carbon debt.” Science 319: 1235-1238. 188 Literature
Fearnside, P. M. (2001). “Soybean cultivation as a threat to the environment in Brazil.” Environmental Conservation 28(1): 23-28. Fernando, A. L., M. P. Duarte, J. Almeida, S. Boléo and B. Mendes (2010). “Environmental impact assessment of energy crops cultivation in Europe.” Biofuels, Bioproducts and Biorefining 4(6): 594-604. Fitzherbert, E. B., M. J. Struebig, A. Morel, F. Danielsen, C. A. Br¸hl, P. F. Donald and B. Phalan (2008). “How will oil palm expansion affect biodiversity?” Trends in Ecology & Evolution 23(10): 538-545. Flohre, A., C. Fischer, T. Aavik, J. Bengtsson, F. Berendse, R. Bommarco, P. Ceryngier, L. W. Clement, C. Dennis, S. Eggers, M. Emmerson, F. Geiger, I. Guerrero, V. Hawro, P. Inchausti, J. Liira, M. B. Morales, J. J. Oñate, T. Pärt, W. W. Weisser, C. Winqvist, C. Thies and T. Tscharntke (2011). “Agricultural intensification and biodiversity partitioning in European landscapes comparing plants, carabids, and birds.” Ecological Applications 21(5): 1772-1781. Flynn, H. C., L. Milà i Canals, E. Keller, H. King, S. Sim, A. Hastings, S. Wang and P. Smith (2012). “Quantifying global greenhouse gas emissions from land-use change for crop production.” Global Change Biology 18(5): 1622-1635. Foley, J. A., R. DeFriest, G. P. Asner, C. Barford, G. Bonan, S. R. Carpenter, F. S. Chapin, M. T. Coe, G. C. Daily, H. K. Gibbs, J. H. Helkowski, T. Holloway, E. A. Howard, C. J. Kucharik, C. Monfreda, J. A. Patz, I. C. Prentice, N. Ramankutty and P. K. Snyder (2005). “Global consequences of land use.” Science 309: 570-574. Friend, A. D., W. Lucht, T. T. Rademacher, R. Keribin, R. Betts, P. Cadule, P. Ciais, D. B. Clark, R. Dankers, P. D. Falloon, A. Ito, R. Kahana, A. Kleidon, M. R. Lomas, K. Nishina, S. Ostberg, R. Pavlick, P. Peylin, S. Schaphoff, N. Vuichard, L. Warszawski, A. Wiltshire and F. I. Woodward (2014). “Carbon residence time dominates uncertainty in
terrestrial vegetation responses to future climate and atmospheric CO2.” Proceedings of the National Academy of Sciences 111(9): 3280-3285. GAIN (2011). EU-27 - Oilseeds and Products Annual - Modest Rebound in EU-27 Oilseeds Production, USDA Foregin Agricultural Service, Global Agricultural Information Network GAIN Report Number E60016. GAIN (2015). Biofuels Overview 2015: Italy, USDA Foregin Agricultural Service, Global Agricultural Information Network GAIN Report Number IT1526. GAIN (2015). Poland - Rapeseed and Products Annual - Spring 2015, USDA Foregin Agricultural Service, Global Agricultural Information Network Gaines, H. R. and C. Gratton (2010). “Seed predation increases with ground beetle diversity in a Wisconsin (USA) potato agroecosystem.” Agriculture, Ecosystems and Environment 137: 329-336. Gardiner, M. A., J. K. Tuell, R. Isaacs, J. Gibbs, J. S. Ascher and D. A. Landis (2010). “Implications of three biofuel crops for beneficial arthropods in agricultural landscapes.” BioEnergy 3: 6-19. 189
Gardner, T. A., J. Barlow, R. Chazdon, R. M. Ewers, C. A. Harvey, C. A. Peres and N. S. Sodhi (2009). “Prospects for tropical biodiversity in a human-modified world.” Ecology Letters 12: 561-582. Gerbens-Leenes, W., A. Y. Hoekstra and T. H. van der Meer (2009). “The water footprint of bioenergy.” Proceedings of the National Academy of Sciences of the United States of America 106(25): 10219-10223. Gerber, J. S., K. M. Carlson, D. Makowski, N. D. Mueller, I. Garcia de Cortazar-Atauri, P. Havlík, M. Herrero, M. Launay, C. S. O’Connell, P. Smith and P. C. West (2016).
“Spatially explicit estimates of N2O emissions from croplands suggest climate mitigation opportunities from improved fertilizer management.” Global Change Biology 22(10): 3383-3394. Germer, J. and J. Sauerborn (2008). “Estimation of the impact of oil palm plantation establishment on greenhouse gas balance.” Environment, Development and Sustainability 10(6): 697-716. Geyer, R., L. J.P., D. M. Stoms, F. W. Davis and B. Wittstock (2010). “Coupling GIS and LCA for biodiversity assessments of land use. Part 2: Impact assessment.” International Journal of Life Cycle Assessment 15: 692-703. Gibbs, H. K., M. Johnston, J. A. Foley, T. Holloway, C. Monfreda, N. Ramankutty and D. Zaks (2008). “Carbon payback times for crop-based biofuel expansion in the tropics: The effects of changing yield and technology.” Environmental Research Letters 8(034001): 1-10. Gibbs, H. K., S. Yui and R. J. Plevin (2014). New estimates of soil and biomass carbon stocks for global economic models. GTAP Technical Paper No.33. Gibon, T., E. G. Hertwich, A. Arvesen, B. Singh and F. Verones (2017). “Health benefits, ecological threats of low-carbon electricity.” Environmental Research Letters 12(3): 034023. Gilbert, R. A., J. M. Shine, J. D. Miller, R. W. Rice and C. R. Rainbolt (2006). “The effect of genotype, environment and time of harvest on sugarcane yields in Florida, USA.” Field Crops Research 95(2): 156-170. Gnansounou, E., A. Dauriat, J. Villegas and L. Panichelli (2009). “Life cycle assessment of biofuels: Energy and greenhouse gas balances.” Bioresource Technology 100(21): 4919- 4930. Goglio, P., W. N. Smith, B. B. Grant, R. L. Desjardins, B. G. McConkey, C. A. Campbell and T. Nemecek (2015). “Accounting for soil carbon changes in agricultural life cycle assessment (LCA): a review.” Journal of Cleaner Production 104: 23-39. Goh, C. S. and M. Junginger (2013). Sustainable biomass and bioenergy in the Netherlands: Report 2013. Utrecht, the Netherlands, NL Agency, NL Energy and Climate Cange. Goldemberg, J. (2017). “How much could biomass contribute to reaching the Paris Agreement goals?” Biofuels, Bioproducts and Biorefining 11(2): 237-238. Goldemberg, J., S. T. Coelho and O. Lucon (2004). “How adequate policies can push renewables.” Energy Policy 32(9): 1141-1146. 190 Literature
Goldemberg, J., F. F. C. Mello, C. E. P. Cerri, C. A. Davies and C. C. Cerri (2014). “Meeting the global demand for biofuels in 2021 through sustainable land use change policy.” Energy Policy 69(0): 14-18. Gomiero, T., D. Pimentel and M. G. Paoletti (2011). “Environmental Impact of Different Agricultural Management Practices: Conventional vs. Organic Agriculture.” Critical Reviews in Plant Sciences 30(1-2): 95-124. Gotelli, N. J. and R. K. Colwell (2001). “Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness.” Ecology Letters 4(4): 379-391. Grace, J., J. San José, P. Meir, H. S. Miranda and R. A. Montes (2006). “Productivity and carbon fluxes of tropical savannas.” Journal of Biogeography 33(3): 387-400. Groom, M. J., E. M. Gray and P. A. Townsend (2008). “Biofuels and biodiversity: Principles for creating better policies for biofuel production.” Conservation Biology 22(3): 602- 609. Guindé, L., F. Jacquet and G. Millet (2008). “Impacts du développement des biocarburants sur la production française des grandes cultures.” Revue d’Etudes en Agriculture et Environnement 89(4): 55-81. Guo, L. B. and R. M. Gifford (2002). “Soil carbon stocks and land use change: a meta analysis.” Global Change Biology 8(4): 345-360. Guyomard, H., A. Forslund and Y. Dronne (2011). Biofuels and World Agricultural Markets: Outlook for 2020 and 2050. Economic Effects of Biofuels. M. A. Dos Santos Bernardes. Rijeka, Croatia, InTech. Haberl, H. (2013). “Net land-atmosphere flows of biogenic carbon related to bioenergy: towards an understanding of systemic feedbacks.” GCB Bioenergy 5(4): 351-357. Halleux, H., S. Lassaux, R. Renzoni and A. Germain (2008). “Comparative Life Cycle Assessment of two biofuels. Ethanol from sugar beet and rapeseed methyl ester.” International Journal of Life Cycle Assessment 13(3): 184-190. Hallgren, W., C. A. Schlosser, E. Monier, D. Kicklighter, A. Sokolov and J. Melillo (2013). “Climate impacts of a large-scale biofuels expansion.” Geophysical Research Letters 40(8): 1624-1630. Hanafiah, M. M., A. J. Hendriks and M. A. J. Huijbregts (2012). “Comparing the ecological footprint with the biodiversity footprint of products.” Journal of Cleaner Production 37: 107-114. Hanssen, S. V., A. S. Duden, M. Junginger, V. H. Dale and F. Hilst (2017). “Wood pellets, what else? Greenhouse gas parity times of European electricity from wood pellets produced in the south-eastern United States using different softwood feedstocks.” GCB Bioenergy 9(9): 1406-1422. Havlík, P., U. W. Schneider, E. Schmid, H. Böttcher, S. Fritz, R. Skalský, K. Aoki, S. De Cara, G. Kindermann, F. Kraxner, S. Leduc, I. McCallum, A. Mosnier, T. Sauer and M. Obersteiner (2011). “Global land-use implications of first and second generation biofuel targets.” Energy Policy 39(10): 5690-5702. 191
Havlin, J. L., D. E. Kissel, L. D. Maddux, M. M. Claassen and J. H. Long (1990). “Crop rotation and tillage effects on soil organic carbon and nitrogen.” Soil Science Society of America Journal 54: 448-452. Heaton, E. A., F. G. Dohleman and S. P. Long (2008). “Meeting US biofuel goals with less land: the potential of Miscanthus.” Global Change Biology 14(1): 2000-2014. Helmut, H., E. Karl-Heinz, K. Fridolin, R. Steve, D. S. Timothy and W. K. Smith (2013). “Bioenergy: how much can we expect for 2050?” Environmental Research Letters 8(3): 031004. Helwig, T., R. Jannasch, R. Samson, A. DeMaio and D. Caumartin (2002). Agricultural Biomass Redisude Inventories and Conversion Systems for Energy Production in Eastern Canada. Quebec, Canada, Resource Efficient Agricultural Production (REAP). Henle, K., D. Alard, J. Clitherow, P. Cobb, L. Firbank, T. Kull, D. McCracken, R. F. Moritz, J. Niemelä and M. Rebane (2008). “Identifying and managing the conflicts between agriculture and biodiversity conservation in Europe–A review.” Agriculture, Ecosystems & Environment 124(1): 60-71. Hennenberg, K. J., C. Dragisic, S. Haye, J. Hewson, B. Semroc, C. Savy, K. Wiegmann, H. Fehrenbach and U. R. Fritsche (2010). “The Power of Bioenergy-Related Standards to Protect Biodiversity.” Conservation Biology 24(2): 412-423. Herrera, L. P., J. L. Panigatti, M. P. Barral and B. D.E. (2013). Biocombustibles en Argentina. Impactos de la producción de soja sobre los humedales y el agua. Buenos Aires, Argentina, Fundación Humedales / Wetlands International. Hertel, T. W. (2011). “The global supply and demand for agricultural land in 2050: A perfect storm in the making?” American Journal of Agricultural Economics 93(2): 259-275. Hoefnagels, R., E. Smeets and A. Faaij (2010). “Greenhouse gas footprints of different biofuel production systems.” Renewable and Sustainable Energy Reviews 14(7): 1661- 1694. Hole, D. G., A. J. Perkins, J. D. Wilson, I. H. Alexander, P. V. Grice and A. D. Evans (2005). “Does organic farming benefit biodiversity?” Biological Conservation 122: 113-130. Holland, J. M. (2004). “The environmental consequences of adopting conservation tillage in Europe: reviewing the evidence.” Agriculture, Ecosystems and Environment 103: 1-25. Holly, K. G., J. Matt, A. F. Jonathan, H. Tracey, M. Chad, R. Navin and Z. David (2008). “Carbon payback times for crop-based biofuel expansion in the tropics: the effects of changing yield and technology.” Environmental Research Letters 3(3): 034001. Huijbregts, M. A. J., Z. J. N. Steinmann, P. M. F. Elshout, G. Stam, F. Verones, M. D. M. Vieira, A. Hollander, M. Zijp and R. Van Zelm (2016). ReCiPe 2016: A harmonized life cycle impact assessment method at midpoint and endpoint level. Report I: Characterization. Bilthoven, The Netherlands, National Institute for Public Health and the Environment (RIVM). IEA (2015). Medium-Term Renewable Energy Market Report 2015, OECD/IEA, Paris. IEA (2018). Market Report Series: Renewables 2017, OECD/IEA, Paris: 211. Immerzeel, D. J., P. A. Verweij, F. Hilst and A. P. C. Faaij (2013). “Biodiversity impacts of bioenergy crop production: a state-of-the-art review.” GCB Bioenergy 6(3): 183-209. 192 Literature
Informa Economics (2012). Impact of the U.S. Biodiesel Industry on the U.S. Soybean Complex. Memphis, Tennessee, Informa Economics. IPCC (2001). Climate Change 2001: The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate. Cambridge, Cambridge University Press.
IPCC (2006). Guidelines for National Greenhouse Gas Inventories. Chapter 11: N2O
emissions from managed soils, and CO2 emissions from lime and urea application. Cambridge, United Kingdom and New York, NY, USA, Cambridge University Press: 54 pp. IPCC (2006). Guidelines for National Greenhouse Gas Inventories. Volume 4: Agriculture, Forestry and Other Land Use. Chapter 5: Cropland. Cambridge, United Kingdom and New York, NY, USA, Cambridge University Press: 66 pp. IPCC (2013). Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Invergovernmental Panel on Climate Change. Cambridge, United Kingdom and New York, NY, USA, Cambridge University Press: 1535 pp. IPCC (2014). Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Reprt of the Intergovernmental Panel on Climate Change. Cambridge, United Kingdom and New York, NY, USA, Cambridge University Press. ISO (1997). Environmental Management - Life Cycle Assessment - Principles and Framework, ISO 14040, First Edition, June. IUCN (2015). Habitat Classification Scheme, version 3.1. Cambridge, UK, Internatioanl Union for Conservation of Nature. Izaurralde, R. C., J. R. Williams, W. B. McGill, N. J. Rosenberg and M. C. Quiroga Jakas (2006). “Simulating soil C dynamics with EPIC: Model description and testing against long- term data.” Ecological Modelling 192(3-4): 362-384. Jaggard, K. W., A. Qi and E. S. Ober (2010). “Possible changes to arable crop yields by 2050.” Philosophical Transactions of the Royal Society B 365(1554): 2835-2851. Jansen, J. C. (2003). Policy Support for Renewable Energy in the European Union, Energy Research Centre of the Netherlands. Jauhiainen, J., H. Takahashi, J. E. P. Heikkinen, P. J. Martikainen and H. Vasander (2005). “Carbon fluxes from a tropical peat swamp forest floor.” Global Change Biology 11(10): 1788-1797. Johnson, J. A., C. F. Runge, B. Senauer, J. Foley and S. Polasky (2014). “Global agriculture and carbon trade-offs.” Proceedings of the National Academy of Sciences 111(34): 12342- 12347. Joos, F., R. Roth, J. S. Fuglestvedt, G. P. Peters, I. G. Enting, W. von Bloh, V. Brovkin, E. J. Burke, M. Eby, N. R. Edwards, T. Friedrich, T. L. Frölicher, P. R. Halloran, P. B. Holden, C. Jones, T. Kleinen, F. T. Mackenzie, K. Matsumoto, M. Meinshausen, G. K. Plattner, A. Reisinger, J. Segschneider, G. Shaffer, M. Steinacher, K. Strassmann, K. Tanaka, A. Timmermann and A. J. Weaver (2013). “Carbon dioxide and climate impulse response 193
functions for the computation of greenhouse gas metrics: a multi-model analysis.” Atmos. Chem. Phys. 13(5): 2793-2825. Jungbluth, N. (2007). Erdöl. Sachbilanzen von Energiesystemen: Grundlagen für den ökologischen Vergleich von Energiesystemen und den Einbezug von Energiesystemen in Ökobilanzen für die Schweiz. Dübendorf, Switzerland, Swiss Centre for Life Cycle Inventories. Kadam, K. L. (2002). “Environmental benefits on a life cycle basis of using bagasse- derived ethanol as a gasoline oxygenate in India.” Energy Policy 30(5): 371-384. Kaltschmitt, M., G. A. Reinhardt and T. Stelzer (1997). “Life cycle analysis of biofuels under different environmental aspects.” Biomass and Bioenergy 12(2): 121-134. Karlen, D. (2004). “Rain-fed maize-soybean rotations of North America.” Dekker Encyclopedia of Plant of Crop Science: 358-362. Keeney, R. and T. W. Hertel (2009). “The indirect land use impacts of United States biofuel policies: The importance of acreage, yield and bilateral trade responses.” American Journal of Agricultural Economics 91(4): 895-909. Keith, H., B. G. Mackey and D. B. Lindenmayer (2009). “Re-evaluation of forest biomass carbon stocks and lessons from the world’s most carbon-dense forests.” Proceedings of the National Academy of Sciences 106(28): 11635-11640. Kellndorfer, J., W. Walker, E. LaPoint, J. Bishop, T. Cormier, G. Fiske, M. Hoppus, K. Kirsch and J. Westfall (2012). NACP Aboveground Biomass and Carbon Baseline Data (NBCD 2000). ORNL DAAC, Oak Ridge, Tennessee, U.S.A. Kendall, A. (2012). “Time-adjusted global warming potentials for LCA and carbon footprints.” International Journal of Life Cycle Assessment 17(8): 1042-1049. Kendall, A. and B. Chang (2009). “Estimating life cycle greenhouse gas emissions from corn-ethanol: A critical review of current U.S. practices.” Journal of Cleaner Production 17: 1175-1182. Kessler, M., S. Abrahamczyk, M. Bos, D. Buchori, D. Dwi Putra, S. R. Gradstein, P. Hõhn, J. Kluge, F. Orend, R. Pitopang, S. Saleh, C. H. Schulze, S. G. Sporn, I. Steffan-Dewenter, S. S. Tjitrosoedirdjo and T. Tscharntke (2009). “Alpha and beta diversity of plants and animals along a tropical land-use gradient.” Ecological Applications 19(8): 2142-2156. Kevin, R. F., S. T. Margaret, H. O. H. Michael and M. K. Daniel (2010). “Accounting for the water impacts of ethanol production.” Environmental Research Letters 5(1): 014020. Khoury, F. and M. Al-Shamlih (2006). “The impact of intensive agriculture on the bird community of a sand dune desert.” Journal of Arid Environments 64: 448-459. Kim, D.-H., J. O. Sexton and J. R. Townshend (2015). “Accelerated deforestation in the humid tropics from the 1990s to the 2000s.” Geophysical Research Letters 42(9): 3495- 3501. Kim, H., S. Kim and B. E. Dale (2009). “Biofuels, land use change, and greenhouse gas emissions: Some unexplored variables.” Environmental Science & Technology 43(3): 961-967. 194 Literature
Kim, S. and B. E. Dale (2002). “Allocation procedure in ethanol production system from corn grain i. system expansion.” International Journal of Life Cycle Assessment 7(4): 237. Kim, S. and B. E. Dale (2005). “Life cycle assessment of various cropping systems utilized for producing biofuels: Bioethanol and biodiesel.” Biomass and Bioenergy 29(6): 426- 439. Kim, S. and B. E. Dale (2008). “Effects of nitrogen fertilizer application on greenhouse gas emissions and economics of corn production.” Environmental Science & Technology 42(16): 6028-6033. Kim, S. and B. E. Dale (2008). “Life cycle assessment of fuel ethanol derived from corn grain via dry milling.” Bioresource Technology 99: 5250-5260. Kim, S. and B. E. Dale (2009). “Regional variations in greenhouse gas emissions of biobased products in the United States—corn-based ethanol and soybean oil.” International Journal of Life Cycle Assessment 14(6): 540-546. Kim, S. and B. E. Dale (2011). “Indirect land use change for biofuels: Testing predictions and improving analytical methodologies.” Biomass and Bioenergy 35(7): 3235-3240. Kløverpris, J., H. Wenzel and P. H. Nielsen (2007). “Life cycle inventory modelling of land use induced by crop consumption. Part 1: Conceptual analysis and methodological proposal.” International Journal of Life Cycle Assessment 13(1): 13-21. Koh, L. P. (2007). “Potential habitat and biodiversity losses from intensified biodiesel feedstock production.” Conservation Biology 21(5): 1373-1375. Koizumi, T. (2014). Biofuels and Food Security: Biofuel Impact on Food Security in Brazil, Asia and Major Producing Countries. Cham, Heidelberg, New York, Dordrecht, London, Springer. Köllner, T. (2000). “Species-pool effect potentials (SPEP) as a yardstick to evaluate land- use impacts on biodiversity.” Journal of Cleaner Production 8: 293-311. Köllner, T. and R. W. Scholz (2007). “Assessment of land use impacts on the natural environment. Part 1: An analytical framework for pure land occupation and and land use change.” International Journal of Life Cycle Assessment 12: 16-23. Köllner, T. and R. W. Scholz (2008). “Assessment of land use impacts on the natural environment. Part 2: Generic characterization factors for local species diversity in central Europe.” International Journal of Life Cycle Assessment 13: 32-48. Kousoulidou, M., G. Fontaras, L. Ntziachristos and Z. Samaras (2010). “Biodiesel blend effects on common-rail diesel combustion and emissions.” Fuel 89(11): 3442-3449. Kromp, B. (1999). “Carabid beetles in sustainable agriculture: A review on pest control efficacy, cultivation impacts and enhancement.” Agriculture, Ecosystems and Environment 74: 187-228. Kruskal, W. H. and W. A. Wallis (1952). “Use of ranks in one-criterion variance analysis.” Journal of the American Statistical Association 47(260): 583-621. Lal, R. (2004). “Carbon emission from farm operations.” Environment international 30(7): 981-990. 195
Lambin, E. F. and P. Meyfroidt (2011). “Global land use change, economic globalization, and the looming land scarcity.” Proceedings of the National Academy of Sciences 108(9): 3465-3472. Lamers, P. and M. Junginger (2013). “The ‘debt’ is in the detail: A synthesis of recent temporal forest carbon analyses on woody biomass for energy.” Biofuels, Bioproducts and Biorefining7 : 373-385. Lamers, P., M. Junginger, C. C. Dymond and A. Faaij (2013). “Damaged forests provide an opportunity to mitigate climate change.” GCB - Bioenergy 6(1): 44-60. Lang, A., J. Filser and J. R. Henschel (1999). “Predation by ground beetles and wolf spiders on herbivorous insects in a maize crop.” Agriculture, Ecosystems and Environment 72: 189-199. Lange, M. (2011). “The GHG balance of biofuels taking into account land use change.” Energy Policy 39(5): 2373-2385. Lapola, D. M., R. Schaldach, J. Alcamo, A. Bondeau, J. Koch, C. Koelking and J. A. Priess (2010). “Indirect land-use changes can overcome carbon savings from biofuels in Brazil.” Proceedings of the National Academy of Sciences 107(8): 3388. Larsen, F. W., J. Bladt, A. Balmford and C. Rahbek (2012). “Birds as biodiversity surrogates: Will supplementing birds with other taxa improve effectiveness?” Journal of Applied Ecology 49: 349-356. Lashof, D. A. and D. R. Ahuja (1990). “Relative contributions of greenhouse gas emissions to global warming.” Nature 344: 529. Laurance, W. F., J. Sayer and K. G. Cassman (2014). “Agricultural expansion and its impacts on tropical nature.” Trends in Ecology & Evolution 29(2): 107-116. Lewandowski, I., J. M. O. Scurlock, E. Lindvall and M. Christou (2003). “The development and current status of perennial rhizomatous grasses as energy crops in the US and Europe.” Biomass and Bioenergy 25(4): 335-361. Li, S.-Z. and C. Chan-Halbrendt (2009). “Ethanol production in (the) People’s Republic of China: Potential and technologies.” Applied Energy 86(Supplement 1): S162-S169. Lindeijer, E. (2000). “Biodiversity and life support impacts of land use in LCA.” Journal of Cleaner Production 8: 313-319. Lindeijer, E. (2000). “Review of land use impact methodologies.” Journal of Cleaner Production 8: 273-281. Lindeijer, E., R. Müller-Wenk and B. Steen (2002). Impact assessment of resources and land use. Life Cycle Impact Assessment: Striving towards best practice. H. A. Udo de Haes, G. Finnveden, M. Goedkoop et al. Pensacola, USA, Society of Environmental Toxicology and Chemistry (SETAC). Liska, A. J., H. S. Yang, V. R. Bremer, T. J. Klopfenstein, D. T. Walters, G. E. Erickson and K. G. Cassman (2009). “Improvements in Life Cycle Energy Efficiency and Greenhouse Gas Emissions of Corn-Ethanol.” Journal of Industrial Ecology 13(1): 58-74. Luo, L., E. van der Voet and G. Huppes (2009). “Life cycle assessment and life cycle costing of bioethanol from sugarcane in Brazil.” Renewable and Sustainable Energy Reviews 13(6): 1613-1619. 196 Literature
Luo, L., E. Van der Voet, G. Huppes and H. A. Udo de Haes (2009). “Allocation issues in LCA methodology: A case study of corn stover-based fuel ethanol.” International Journal of Life Cycle Assessment 14(6): 529-539. Macedo, I. C., J. E. A. Seabra and J. E. A. R. Silva (2008). “Green house gases emissions in the production and use of ethanol from sugarcane in Brazil: The 2005/2006 averages and a prediction for 2020.” Biomass and Bioenergy 32(7): 582-595. Malça, J., A. Coelho and F. Freire (2014). “Environmental life-cycle assessment of rapeseed- based biodiesel: Alternative cultivation systems and locations.” Applied Energy 114: 837-844. Malça, J. and F. Freire (2011). “Life-cycle studies of biodiesel in Europe: A review addressing the variability of results and modeling issues.” Renewable and Sustainable Energy Reviews 15(1): 338-351. Mann, H. B. and D. R. Whitney (1947). “On a test of whether one of two random variables is stochastically larger then the other.” Annals of Mathematical Statistics 18(1): 50-60. Manna, M. C., A. Swarup, R. H. Wanjari, B. Mishra and D. K. Shahi (2007). “Long-term fertilization, manure and liming effects on soil organic matter and crop yields.” Soil and Tillage Research 94(2): 397-409. Matson, P. A., W. J. Parton, A. G. Power and M. J. Swift (1997). “Agricultural intensification and ecosystem properties.” Science 277: 504-509. Matt, J., R. Licker, J. Foley, T. Holloway, N. D. Mueller, C. Barford and C. Kucharik (2011). “Closing the gap: global potential for increasing biofuel production through agricultural intensification.” Environmental Research Letters 6(3): 034028. Matthew, K., W. M. Griffin and H. S. Matthews (2009). “Indirect land use change and biofuel policy.” Environmental Research Letters 4(3): 034008. Mattson, B., C. Cederberg and L. Blix (2000). “Agricultural land use in life cycle assessment (LCA): case studies of three vegetable oil crops.” Journal of Cleaner Production 8: 283-292. McGeoch, M. A. (1998). “The selection, testing and application of terrestrial insects as bioindicators.” Biological Reviews 73(2): 181-201. McLaughlin, A. and P. Mineau (1995). “The impact of agricultural practices on biodiversity.” Agriculture, Ecosystems and Environment 55: 201-212. Meek, B., D. Loxton, T. Sparks, R. Pywell, H. Pickett and M. Nowakowski (2002). “The effect of arable field margin composition on invertebrate biodiversity.” Biological Conservation 106(2): 259-271. Melillo, J. M., J. M. Reilly, D. W. Kicklighter, A. C. Gurgel, T. W. Cronin, S. Paltsev, B. S. Felzer, X. Wang, A. P. Sokolov and C. A. Schlosser (2009). “Indirect emissions from biofuels: How important?” Science 326: 1397-1399. Menéndez, R., A. G. Megías, J. K. Hill, B. Braschler, S. G. Willis, Y. Collingham, R. Fox, D. B. Roy and C. D. Thomas (2006). “Species richness changes lag behind climate change.” Proceedings of the Royal Society B: Biological Sciences 273(1593): 1465. Michelsen, O. (2008). “Assessment of land use impact on biodiversity. Proposal of a new methodology exemplified with forestry operations in Norway.” International Journal of Life Cycle Assessment 13(1): 22-31. 197
Mielke, E., L. Diaz Anadon and V. Narayanamurti (2010). Water consumption of energy resource extraction, processing, and conversion. A review of the literature for estimates of water intensity of energy-resource extraction, processing to fuels, and conversion to electricity. Cambridge, MA, USA, Belfer Center for Science and International Affairs, Harvard Kennedy School, Harvard University. NO 2010-15. Milà i Canals, L., C. Bauer, J. Depestele, A. Dubreuil, R. Freiermuth Knuchel, G. Gaillard, O. Michelsen, R. Müller-Wenk and B. Rydgren (2007). “Key elements in a framework for land use impact assessment within LCA.” International Journal of Life Cycle Assessment 12(1): 5-15. Millennium Ecosystem Assessment (2005). Ecosystems and human well-being: Biodiversity synthesis. Washington, DC, World Resources Institute. Milliken, J., F. Joseck, M. Wang and E. Yuzugullu (2007). “The Advanced Energy Initiative.” Journal of Power Sources 172(1): 121-131. Mitchell, D. (2008). “A note on rising food prices.” World Bank Policy Research Working Paper Series. Morris, E. K., T. Caruso, F. Buscot, M. Fischer, C. Hancock, T. S. Maier, T. Meiners, C. Müller, E. Obermaier, D. Prati, S. A. Socher, I. Sonnemann, N. Wäschke, T. Wubet, S. Wurst and M. C. Rillig (2014). “Choosing and using diversity indices: insights for ecological applications from the German Biodiversity Exploratories.” Ecology and Evolution 4(18): 3514-3524. Mukherjee, I. and B. K. Sovacool (2014). “Palm oil-based biofuels and sustainability in southeast Asia: A review of Indonesia, Malaysia, and Thailand.” Renewable and Sustainable Energy Reviews 37: 1-12. Müller-Wenk, R. and M. Brandão (2010). “Climatic impact of land use in LCA - carbon transfers between vegetation/soil and air.” International Journal of Life Cycle Assessment 15: 172-182. Müller, C., L. De Baan and T. Köllner (2013). “Comparing direct land use impacts on biodiversity of conventional and organic milk - based on a Swedish case study.” International Journal of Life Cycle Assessment. Mulugeta, D., D. E. Stoltenberg and C. M. Boerboom (2001). “Weed species-area relationships as influenced by tillage.” Weed Science 49(2): 217-223. Murdiyarso, D., K. Hergoualc’h and L. V. Verchot (2010). “Opportunities for reducing greenhouse gas emissions in tropical peatlands.” Proceedings of the National Academy of Sciences 107(46): 19655-19660. Naik, S. N., V. V. Goud, P. K. Rout and A. K. Dalai (2010). “Production of first and second generation biofuels: A comprehensive review.” Renewable and Sustainable Energy Reviews 14(2): 578-597. Nemecek, T. and T. Kägi (2007). Life Cycle Inventories of Agricultural Production Systems. Ecoinvent report No.15. Swiss Centre for Life Cycle Inventories, Zürich, Switzerland. Nepstad, D. C., A. Veríssimo, A. Alencar, C. Nobre, E. Lima, P. Lefebvre, P. Schlesinger, C. Potter, P. Moutinho, E. Mendoza, M. Cochrane and V. Brooks (1999). “Large-scale impoverishment of Amazonian forests by logging and fire.” Nature 398: 505-508. 198 Literature
Newbold, T., L. N. Hudson, S. L. L. Hill, S. Contu, I. Lysenko, R. A. Senior, L. Borger, D. J. Bennett, A. Choimes, B. Collen, J. Day, A. De Palma, S. Diaz, S. Echeverria-Londono, M. J. Edgar, A. Feldman, M. Garon, M. L. K. Harrison, T. Alhusseini, D. J. Ingram, Y. Itescu, J. Kattge, V. Kemp, L. Kirkpatrick, M. Kleyer, D. L. P. Correia, C. D. Martin, S. Meiri, M. Novosolov, Y. Pan, H. R. P. Phillips, D. W. Purves, A. Robinson, J. Simpson, S. L. Tuck, E. Weiher, H. J. White, R. M. Ewers, G. M. Mace, J. P. W. Scharlemann and A. Purvis (2015). “Global effects of land use on local terrestrial biodiversity.” Nature 520(7545): 45-50. Nordborg, M., C. Cederberg and G. Berndes (2014). “Modeling potential freshwater ecotoxicity impacts due to pesticide use in biofuel feedstock production: The cases of maize, rapeseed, salix, soybean, sugar cane, and wheat.” Environmental Science & Technology 48(19): 11379-11388. Obidzinski, K., K. Kusters and S. Gnych (2015). “Taking the bitter with the sweet: Sugarcane’s return as a driver of tropical deforestation.” Conservation Letters 8(6): 449-455. Olson, D. M., E. Dinerstein, E. D. Wikramanayake, N. D. Burgess, G. V. N. Powell, E. C. Underwood, J. A. D’Amico, I. Itoua, H. E. Strand, J. C. Morrison, C. J. Loucks, T. F. Allnut, T. H. Ricketts, Y. Kura, J. F. Lamoreux, W. W. Wettengel, P. Hedao and K. R. Kassem (2001). “Terrestrial ecoregions of the world: A new map of life on Earth.” Bioscience 51(11): 933-938. Olson, K. R., A. N. Gennadiyev, A. P. Zhidkin and M. V. Markelov (2012). “Impacts of land- use change, slope, and erosion on soil organic carbon retention and storage.” Soil Science 177(4): 269-278. Ottonetti, L., L. Tucci, F. Frizzi, G. Chelazzi and G. Santini (2010). “Changes in ground- foraging ant assemblages along a disturbance gradient in a tropical agricultural landscape.” Ethology Ecology & Evolution 22(1): 73-86. Ouchtati, N., S. Doumandji and P. Brandmayr (2012). “Comparison of ground beetle (Coleoptera: Carabidae) assembages in cultivated and natural steppe biotopes of the semi-arid region of Algeria.” African Entomology 20(1): 134-143. Panichelli, L., A. Dauriat and E. Gnansounou (2009). “Life cycle assessment of soybean- based biodiesel in Argentina for export.” International Journal of Life Cycle Assessment 14(2): 144-159. Panichelli, L. and E. Gnansounou (2008). “Estimating greenhouse gas emissions from indirect land-use change in biofuel production: concepts and exploratory analysis for soybean-based biodiesel.” Journal of Scientific and Industrial Research 67(11): 1017-1030. Pehnelt, G. and C. Vietze (2012). Uncertainties about the GHG Emissions Saving of Rapeseed Biodiesel. Jena, Germany, Jena Economic Research Papers. 2012-039. Pimentel, D. and T. W. Patzek (2005). “Ethanol production using corn, switchgrass, and wood; Biodiesel production using soybean and sunflower.” Natural Resources Research 14(1): 65-76. 199
Pimentel, D. and T. W. Patzek (2008). Ethanol Production: Energy and Economic Issues Related to U.S. and Brazilian Sugarcane. Biofuels, Solar and Wind as Renewable Energy Systems: Benefits and Risks. D. Pimentel. Dordrecht, Springer Netherlands: 357-371. Plourde, J. D., B. C. Pijanowski and B. K. Pekin (2013). “Evidence for increased monoculture cropping in the Central United States.” Agriculture, Ecosystems & Environment 165: 50-59. Popp, A., F. Humpenoder, I. Weindl, B. L. Bodirsky, M. Bonsch, H. Lotze-Campen, C. Muller, A. Biewald, S. Rolinski, M. Stevanovic and J. P. Dietrich (2014). “Land-use protection for climate change mitigation.” Nature Climate Change 4(12): 1095-1098. Prendergast, J. R. (2006). “Species richness covariance in higher taxa: Empirical tests of the biodiversity indicator concept.” Ecography 20(2): 210-216. Prince, S. D., J. Haskett, M. Steininger, H. Strand and R. Wright (2001). “Net primary production of U.S. Midwest croplands from agricultural harvest yield data.” Ecological Applications 11: 1194-1205. Qiu, H., J. Huang, J. Yang, S. Rozelle, Y. Zhang, Y. Zhang and Y. Zhang (2010). “Bioethanol development in China and the potential impacts on its agricultural economy.” Applied Energy 87(1): 76-83. Reeves, D. W. (1997). “The role of soil organic matter in maintaining soil quality in continuous cropping systems.” Soil and Tillage Research 43(1–2): 131-167. Reijnders, L. and M. A. J. Huijbregts (2008). “Biogenic greenhouse gas emissions linked to the life cycles of biodiesel derived from European rapeseed and Brazilian soybeans.” Journal of Cleaner Production 16(18): 1943-1948. Reijnders, L. and M. A. J. Huijbregts (2008). “Palm oil and the emission of carbon-based greenhouse gases.” Journal of Cleaner Production 16: 477-482. Renewable Energy Directive. (2009). from http://ec.europa.eu/energy/en/topics/ renewable-energy/biofuels. Renó, M. L. G., E. E. S. Lora, J. C. E. Palacio, O. J. Venturini, J. Buchgeister and O. Almazan (2011). “A LCA (Life Cycle Assessment) of the methanol production from sugarcane bagasse.” Energy 36(6): 3716-3726. Renouf, M. A., R. J. Pagan and M. K. Wegener (2011). “Life Cycle Assessment of Australian sugarcane products with a focus on cane processing.” International Journal of Life Cycle Assessment 16(2): 125-137. Rice, R. W., R. A. Gilbert and J. M. McCray (2006). Nutritional requirements for Florida sugarcane. Florida Sugarcane Handbook. Gainesville, Florida, USA, University of Florida. SS-AGR-228. Riechert, S. E. and L. Bishop (1990). “Prey control by an assemblage of generalist predators: Spiders in garden test systems.” Ecology 71(4): 1441-1450. Righelato, R. and D. V. Spracklen (2007). “Carbon mitigation by biofuels or by saving and restoring forests?” Science 317(5840). 200 Literature
Rodrigues, A. S. L. and T. M. Brooks (2007). “Shortcuts for biodiversity conservation planning: The effectiveness of surrogates.” Annual Review of Ecology, Evolution, and Systematics 38: 713-737. Rosenzweig, C., J. Elliott, D. Deryng, A. C. Ruane, C. Müller, A. Arneth, K. J. Boote, C. Folberth, M. Glotter, N. Khabarov, K. Neumann, F. Piontek, T. A. M. Pugh, E. Schmid, E. Stehfest, H. Yang and J. W. Jones (2014). “Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison.” Proceedings of the National Academy of Sciences 111(9): 3268-3273. Ruesch, A. and H. K. Gibbs (2008). New IPCC Tier-1 Global Biomass Map for the Year 2000. Oak Ridge National Laboratory, Oak Ridge, Tennessee, Available online from the Carbon Dioxide Information Analysis Center [http://cdiac.ornl.gov]. Sala, O. E., F. Stuart Chapin, Iii, J. J. Armesto, E. Berlow, J. Bloomfield, R. Dirzo, E. Huber- Sanwald, L. F. Huenneke, R. B. Jackson, A. Kinzig, R. Leemans, D. M. Lodge, H. A. Mooney, M. n. Oesterheld, N. L. Poff, M. T. Sykes, B. H. Walker, M. Walker and D. H. Wall (2000). “Global biodiversity scenarios for the year 2100.” Science 287(5459): 1770. Sanz Requena, J. F., A. C. Guimaraes, S. Quirós Alpera, E. Relea Gangas, S. Hernandez- Navarro, L. M. Navas Gracia, J. Martin-Gil and H. Fresneda Cuesta (2011). “Life Cycle Assessment (LCA) of the biofuel production process from sunflower oil, rapeseed oil and soybean oil.” Fuel Processing Technology 92(2): 190-199. Schmid, E., J. Balkovič and R. Skalský (2007). Biophysical impact assessment of crop land management stratiegies in EU25 using EPIC. Carbon Sink Enhancement in Soils of Europe: Data, Modeling, Verification. V. Stolbovoy, L. Montanarella and P. Panagos. European Communities, JRC Scientific and Technical Reports: 160-183. Schmidt, J. H. (2008). “Development of LCIA characterisation factors for land use impacts on biodiversity.” Journal of Cleaner Production 16: 1929-1942. Schmidt, J. H. (2010). “Comparative life cycle assessment of rapeseed oil and palm oil.” International Journal of Life Cycle Assessment 15: 183-197. Schmidt, M. H., I. Roschewitz, C. Thies and T. Tscharntke (2005). “Differential effects of landscape and management on diversity and density of ground-dwelling farmland spiders.” Journal of Applied Ecology 42: 281-287. Schulze, C. H., M. Waltert, P. J. A. Kessler, R. Pitopang, Shahabudding, D. Veddeler, M. Mühlenberg, S. R. Gradstein, C. Leuschner, I. Steffan-Dewenter and T. Tscharntke (2004). “Biodiversity indicator groups of tropical land-use systems: Comparing plants, birds, and insects.” Ecological Applications 14(5): 1321-1333. Searchinger, T., S. P. Hamburg, J. Melillo, W. Chameides, P. Havlík, D. M. Kammen, G. E. Likens, R. N. Lubowski, M. Obersteiner, M. Oppenheimer, G. P. Robertson, W. H. Schlesinger and G. D. Tilman (2009). “Fixing a critical climate accounting error.” Science 326: 527-528. Searchinger, T., C. Hanson, J. Ranganathan, B. Lipinski, R. Waite, R. Winterbottom, A. Dinshaw and R. Heimlich (2013). Creating a Sustainable Food Future: A Menu of Solutions to Sustainaby Feed More Than 9 Billion People by 2050. Washington D.C., World Resources Institute, World Bank, UNEP and UNDP. World Resources Report 2013-14: Interim Findings. 201
Searchinger, T., R. Heimlich, R. A. Houghton, F. Dong, A. Elobeid, J. Fabiosa, S. Tokgoz, D. Hayes and T.-H. Yu (2008). “Use of U.S. croplands for biofuels increases greenhouse gases through emissions from land-use change.” Science 319(5867): 1238. Shcherbak, I., N. Millar and G. P. Robertson (2014). “Global metaanalysis of the nonlinear
response of soil nitrous oxide (N2O) emissions to fertilizer nitrogen.” Proceedings of the National Academy of Sciences 111(25): 9199-9204. Sheffield, J., G. Goteti and E. F. Wood (2006). “Development of a 50-year high-resolution global dataset of meteorological forcings for land surface modeling.” Journal of Climate 19: 3088-3111. Sierra, C. A., J. I. Del Valle, S. A. Orrego, F. H. Morenso, M. E. Harmon, M. Zapata, G. J. Colorado, M. A. Herrera, W. Lara, D. E. Restrepo, L. M. Berrouet, L. M. Loaiza and J. F. Benjumea (2007). “Total carbon stocks in a tropical forest landscape of the Porce region, Colombia.” Forest Ecology and Management 243(2-3). Sinabell, F. and E. Schmid (2008). Die Produktion von Biomasse zur energetischen Verwertung in Österreich. Monatsberichte 7/2008. Vienna, Austria, WIFO. Singh, A., P. S. Nigam and J. D. Murphy (2011). “Renewable fuels from algae: An answer to debatable land based fuels.” Bioresource Technology 102(1): 10-16. Slade, R. and A. Bauen (2013). “Micro-algae cultivation for biofuels: Cost, energy balance, environmental impacts and future prospects.” Biomass and Bioenergy 53: 29-38. Smith, D. M., N. G. Inman-Bamber and P. J. Thorburn (2005). “Growth and function of the sugarcane root system.” Field Crops Research 92(2–3): 169-183. Snyder, C. S., T. W. Bruulsema, T. L. Jensen and P. E. Fixen (2009). “Review of greenhouse gas emissions from crop production systems and fertilizer management effects.” Agriculture, Ecosystems & Environment 133(3): 247-266. Sodhi, N. S., L. P. Koh, B. W. Brook and P. K. L. Ng (2004). “Southeast Asian biodiversity: An impeding disaster.” Trends in Ecology & Evolution 19: 654-660. Solomon, B. D. and J. B. Barnett (2017). The Changing Landscape of Biofuels. The Routledge Research Companion to Energy Geographies. S. Bouzarovski, M. J. Pasqualetti and V. Castán Broto. London, UK, Routledge. Sorda, G., M. Banse and C. Kemfert (2010). “An overview of biofuel policies across the world.” Energy Policy 38(11): 6977-6988. Sørensen, T. (1948). “A method of establishing groups of equal amplitude in plant sociology based on similarity of species content.” K Dan Vidensk Selsk Biol Skr 5: 1-34. Soybean & Corn Website. “http://www.soybeansandcorn.com/news/Jun7_10-7-of-Brazils- Soybeans-Used-for-Biodiesel-Production.” Retrieved 11/3, 2015. Strona, G., S. D. Stringer, G. Vieilledent, Z. Szantoi, J. Garcia-Ulloa and S. A Wich (2018). “Small room for compromise between oil palm cultivation and primate conservation in Africa.” Proceedings of the National Academy of Sciences of the United States of America 115(35): 8811-8816. Teixeira, R. F. M., D. Maia de Souza, M. P. Curran, A. Antón, O. Michelsen and L. Milà i Canals (2016). “Towards consensus on land use impacts on biodiversity in LCA: UNEP/SETAC Life Cycle Initiative preliminary recommendations based on expert contributions.” Journal of Cleaner Production 112: 4283-4287. 202 Literature
Tenenbaum, D. J. (2008). “Food vs. fuel: Diversion of crops could cause more hunger.” Environmental Health Perspectives 116(6): A254-A257. The New Climate Economy (2014). Better Growth, Better Climate. Chapter 3: Land Use, Global Commission on the Economy and Climate. Thompson, W., J. Whistance and S. Meyer (2011). “Effects of US biofuel policies on US and world petroleum product markets with consequences for greenhouse gas emissions.” Energy Policy 39(9): 5509-5518. Tilman, D., J. Hill and C. Lehman (2006). “Carbon-negative biofuels from low-input high- diversity grassland biomass.” Science 314(5805): 1598-1600. Timilsina, G. R. (2014). “Biofuels in the long-run global energy supply mix for transportation.” Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences 372(2006). Tomm, G. O., O. Smiderle, R. Raposo, G. A. Dalmago, A. Soares, E. Figer, A. Luft, E. Batistella, V. Scarantti and F. Benin (2011). Can canola be produced in tropical areas? 13th International Rapeseed Congress, Prague, Czech Republic, International Consultative Group for Research on Rapeseed. Trompiz, G. (2017, 18 December). Rapeseed to decline in EU as biofuel wanes, exports to boost wheat: report. Commodities. London, United Kingdom, Reuters. Tubeileh, A., T. J. Rennie and M. J. Goss (2016). “A review on biomass production from C4 grasses: yield and quality for end-use.” Current Opinion in Plant Biology 31(1): 172-180. Tuomisto, H. L., I. D. Hodge, P. Riordan and D. W. Macdonald (2012). “Does organic farming reduce environmental impacts? – A meta-analysis of European research.” Journal of Environmental Management 112: 309-320. Ulber, L., H.-H. Steinmann, S. Klimek and J. Isselstein (2009). “An on-farm approach to investigate the impact of diversified crop rotations on weed species richness and composition in winter wheat.” Weed Research 49: 534-543. UNEP (2017). Global Guidance for Life Cycle Impact Assessment Indicators - Volume 1. Paris, UNEP (United Nations Environmental Programme). Urban, M. C. (2015). “Accelerating extinction risk from climate change.” Science 348(6234): 571. US EPA. (2005). “Renewable Fuel Standard Program.” from https://www.epa.gov/ renewable-fuel-standard-program. US EPA (2010). Renewable Fuel Standard Program (RFS2) Regulatory Impact Analysis. USDA ERS (2015). Feed Grains Database. USDA ERS (2018). Feed Grains Yearbook. USDA ERS (2018). Oil Crops Yearbook. Van Der Velde, M., F. Bouraoui and A. Aloe (2009). “Pan-European regional-scale modelling of water and N efficiencies of rapeseed cultivation for biodiesel production.” Global Change Biology 15(1): 24-37. Van Groenigen, K. J., X. Qi, C. W. Osenberg, Y. Luo and B. A. Hungate (2014). “Faster
decomposition under increased atmospheric CO2 limits soil carbon storage.” Science 344(6183): 508-509. 203
Van Zelm, R., P. A. N. Muchada, M. Van der Velde, G. Kindermann, M. Obersteiner and
M. A. J. Huijbregts (2014). “Impacts of biogenic CO2 emissions on human health and terrestrial ecosystems: the case of increased wood extraction for bioenergy production on a global scale.” GCB Bioenergy 7(4): 608-617. Vandewalle, M., F. de Bello, M. P. Berg, T. Bolger, S. Dolédec, F. Dubs, C. K. Feld, R. Harrington, P. A. Harrison, S. Lavorel, P. Martins da Silva, M. Moretti, J. Niemelä, P. Santos, T. Sattler, J. P. Sousa, M. T. Sykes, A. J. Vanbergen and B. A. Woodcock (2010). “Functional traits as indicators of biodiversity response to land use changes across ecosystems and organisms.” Biodiversity and Conservation 19: 2921-2947. Verones, F., S. Hellweg, L. B. Azevedo, A. Chaudhary, N. Cosme, P. Fantke, M. Goedkoop, M. Hauschild, A. Laurent, C. L. Mutel, S. Pfister, T. Ponsioen, Z. Steinmann, R. Van Zelm, M. Vieira and M. A. J. Huijbregts (2016). LC-IMPACT version 0.5: A spatially differentiated life cycle impact assessment approach. Verones, F., D. Moran, K. Stadler, K. Kanemoto and R. Wood (2017). “Resource footprints and their ecosystem consequences.” Scientific Reports 7: 40743. Verones, F., S. Pfister, R. van Zelm and S. Hellweg (2017). “Biodiversity impacts from water consumption on a global scale for use in life cycle assessment.” International Journal of Life Cycle Assessment 22(8): 1247-1256. Verstegen, J. A., F. Hilst, G. Woltjer, D. Karssenberg, S. M. Jong and A. P. C. Faaij (2015). “What can and can’t we say about indirect land-use change in Brazil using an integrated economic – land-use change model?” GCB Bioenergy 8(3): 561-578. Vitousek, P. M., H. A. Mooney, J. Lubchenco and J. M. Melillo (1997). “Human domination of Earth’s ecosystems.” Science 277: 494-499. Vogtländer, J. G., E. Lindeijer, J. P. M. Witte and C. Hendriks (2004). “Characterizing the change of land-use based on flora: application for EIA and LCA.” Journal of Cleaner Production 12: 47-57. Von Blottnitz, H. and M. A. Curran (2007). “A review of assessments conducted on bio-ethanol as a transportation fuel from a net energy, greenhouse gas, and environmental life cycle perspective.” Journal of Cleaner Production 15(7): 607-619. Vörösmarty, C. J., P. B. McIntyre, M. O. Gessner, D. Dudgeon, A. Prusevich, P. Green, S. Glidden, S. E. Bunn, C. A. Sullivan, C. R. Liermann and P. M. Davies (2010). “Global threats to human water security and river biodiversity.” Nature 467: 555. Walther, G.-R., E. Post, P. Convey, A. Menzel, C. Parmesan, T. J. C. Beebee, J.-M. Fromentin, O. Hoegh-Guldberg and F. Bairlein (2002). “Ecological responses to recent climate change.” Nature 416(6879): 389-395. Wang, M., H. Huo and S. Arora (2011). “Methods of dealing with co-products of biofuels in life-cycle analysis and consequent results within the U.S. context.” Energy Policy 39(10): 5726-5736. Ward, K. E. and R. N. Ward (2001). “Diversity and abundance of carabid beetles in short- rotation plantings of sweetgum, maize and switchgrass in Alabama.” Agroforestry Systems 53(3): 261-267. 204 Literature
Warner, E., Y. Zhang, D. Inman and G. Heath (2014). “Challenges in the estimation of greenhouse gas emissions from biofuel-induced global land-use change.” Biofuels, Bioproducts and Biorefining 8(1): 114-125. Weidema, B. P., C. Bauer, R. Hischier, C. Mutel, T. Nemecek, J. Reinhard, C. O. Vadenbo and G. Wernet (2013). Overview and methodology. Data quality guideline for the ecoinvent database version 3. St. Gallen, The Ecoinvent Centre. Ecoinvent Report 1 (v3). Weidema, B. P. and E. Lindeijer (2001). Physical impacts of land use in product life cycle assessment Final report of the EURENVIRON-LCAGAPS subproject on land use. Lyngby, Denmark, Technical University of Denmark. Wengenmayr, R. and T. Bührke (2013). Renewable Energy: Sustainable Concepts for the Energy Change. Weinheim, WILEY-VCH Verlag GmbH & Co. Wernet, G., C. Bauer, B. Steubing, J. Reinhard, E. Moreno-Ruiz and B. Weidema (2016). “The ecoinvent database version 3 (part I): Overview and methodology.” International Journal of Life Cycle Assessment 9. West, T. O. and W. M. Post (2002). “Soil organic carbon sequestration rates by tillage and crop rotation.” Soil Science Society of America Journal 66(6): 1930-1946. Wiens, J., J. Fargione and J. Hill (2011). “Biofuels and biodiversity.” Ecological Applications 21(4): 1085-1095. Wijffels, R. H. and M. J. Barbosa (2010). “An outlook on microalgal biofuels.” Science 329(1): 796-799. Wilcoxon, F. (1945). “Individual comparisons by ranking methods.” Biometrics Bulletin 1(6): 80-83. Williams, J. R. (1995). The EPIC model. Computer Models of Watershed Hydrology. V. P. Singh. Highlands Ranch, Water Resources Publication. Wilson, J. D., M. J. Whittingham and R. B. Bradbury (2005). “The management of crop structure: A general approach to reversing the impacts of agricultural intensification on birds?” Ibis 147: 453-463. Wise, T. A. (2012). The Cost to Developing Countries of U.S. Corn Ethanol Expansion, Tufts University, Medford MA, USA. Wright, S. J. and H. C. Muller-Landau (2006). “The future of tropical forest species.” Biotropica 38(3): 287-301. WWF (2010). Ecoregion. Encyclopedia of Earth. C. J. Cleveland. Washington, D.C. Yan, X., O. R. Inderwildi, D. A. King and A. M. Boies (2013). “Effects of ethanol on vehicle energy efficiency and implications on ethanol life cycle greenhouse gas analysis.” Environmental Science & Technology 47(11): 5535-5544. Yang, Q. and G. Q. Chen (2012). “Nonrenewable energy cost of corn-ethanol in China.” Energy Policy 41(Supplement C): 340-347. Yang, Q. and G. Q. Chen (2013). “Greenhouse gas emissions of corn–ethanol production in China.” Ecological Modelling 252(Supplement C): 176-184. Yang, Y. and S. Suh (2014). “Marginal yield, technological advances, and emissions timing in corn ethanol’s carbon payback time.” International Journal of Life Cycle Assessment: 1-7. 205
Yost, M. A., J. A. Coulter and M. P. Russelle (2013). “First-year corn after alfalfa showed no response to fertilizer nitrogen under no-tillage.” Agronomy Journal 105: 208-214. You, L., U. Wood-Sichra, S. Fritz, Z. Guo, L. See and J. Koo (2014). Spatial Production Allocation Model (SPAM) 2005 v2.0. May 22, 2015. Available from http://mapspam. info. Yüksel, F. and B. Yüksel (2004). “The use of ethanol–gasoline blend as a fuel in an SI engine.” Renewable Energy 29(7): 1181-1191. Zan, C. S., J. W. Fyles, P. Girouard and R. A. Samson (2001). “Carbon sequestration in perennial bioenergy, annual corn and uncultivated systems in southern Quebec.” Agriculture, Ecosystems & Environment 86(2): 135-144. Zilberman, D., G. Hochman, D. Rajagopal, S. Sexton and G. Timilsina (2013). “The impact of biofuels on commodity food prices: Assessment of findings.” American Journal of Agricultural Economics 95(2): 275-281.
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Summary
Fossil fuels have been the world’s primary power source since the industrial revolution. Such large-scale use of fossil fuels has led to a tremendous increase of greenhouse gases (GHGs) in the atmosphere, and has become a major cause of climate change. In an effort to reduce climate change, cleaner alternative energy sources have been developed. For road traffic, this includes various types of biofuels made from sugar- and oil-rich crops, such as corn, soybean and rapeseed. Combustion of biofuels emits less net greenhouse gases than fossil fuels, given that any carbon emitted during combustion was first taken from the atmosphere during crop growth. Whether biofuels perform better than fossil fuels over their complete life cycle remains, however to be seen. If demand for biofuels increases, new agricultural lands may be created by destroying natural ecosystems. This may lead to the (local) extinction of species that lose their habitats and the release of carbon to the atmosphere due to destruction of biomass and soil disturbance. Once the croplands are in use, additional impacts can be expected by the use of farming equipment, the production and application of fertilizers, and irrigation by surface and ground water. This way biofuels may end up having larger climate and biodiversity impacts than fossil fuels. Various studies have shown that it may depend on the type of biofuel (and feedstock) and the location of cultivation whether or not the use of biofuels is environmentally preferable over fossil fuels. A comprehensive GHG and biodiversity impact assessment of biofuels on large spatial scales is, however, lacking. The overall goal of this thesis was to assess the impact of biofuel production on GHG emissions and biodiversity at the global scale.
In Chapter 2, the spatial variation in biofuel GHG balances was assessed due to location- specific factors like climate, soil type, farm management strategy, and the type of natural ecosystem that was removed in favor of the cropland. This was done specifically for the case of corn-based bioethanol and soybean-based biodiesel production in the US. As sources of GHG emissions, I included removal of natural biomass upon initial land transformation, change of soil organic carbon and nitrogen stocks upon initial land transformation, application of nitrogen fertilizers, the use of energy (e.g. by agricultural machinery and irrigation) and material inputs (e.g. seeds, pesticides and fertilizers).
Biodiesel from soybean has an average GHG footprint of 52 kg CO2-eq per GJ, but varies from -0.8 to 120 kg CO2-eq per GJ [95% range ]. For bioethanol from corn the average GHG footprint is 63 kg CO2-eq per GJ, with a variation of -85 to 310 kg CO2-eq per GJ [95% range].
The GHG footprint of both biofuels depended most heavily on the CO2 emissions resulting from the destruction of the natural biomass with large variations depending on the location considered and crop cultivation period assumed. Still, variation in soil carbon and nitrogen stocks, fertilizer requirements, and crop yields can lead to significant variation in local GHG balances. Our results highlights the importance of taking into account land transformation in the assessment of agricultural products, and the added value of using spatially explicit models in these assessments. 208 Summary
Chapter 3 quantifies the GHG payback time (GPBT) of five types of crop-based biofuels, namely bioethanol from corn, sugarcane and winter wheat, and biodiesel from rapeseed and soybean, for all possible locations at the global scale. The GPBT equals the number of years before the climatic benefit of displacing fossil fuels with biofuels compensates for the loss of carbon and nitrogen stocks from the original ecosystem during land transformation. GPBTs were calculated using potential yields from the EPIC model, spatially explicit data on biogenic carbon dioxide and dinitrogen oxide emissions from natural biomass and soil, dinitrogen oxide emissions from nitrogen fertilizer application, and country-specific data on fossil GHG emissions from machinery use and irrigation. The five biofuel crops grown under two farm management strategies resulted in a 95% range in GPBTs from -10 years to 417 years. Growing the feedstocks under intensive management reduced the GPT of the biofuels, as fertilization and irrigation resulted in an increased crop yield that offset the negative effects of increased GHG emissions from farming activities, particularly fertilizer application. However, the variation in GPBTs was mostly driven by the location of feedstock cultivation, while differences in crop type and management strategy were less important.
In Chapter 4, GPBTs were derived for the actual production of biofuel from corn, sugarcane, rapeseed and soybean. Rather than using models of the potential feedstock cultivation around the world, as in chapter 3, the focus of this work was on the impact of actual feedstock cultivation in biofuel producing countries. The production of feedstocks in Brazil, Italy, Austria and China requires agricultural lands to be used consecutively for many decades to several centuries before the GHG debt from land transformation is repaid. Biofuel production from rapeseed in Germany, soybean in Argentina, and corn or soybean from the USA, was found to be a more preferable alternative from a GHG emission point of view, with an average GPT of less than 40 years.
In Chapter 5, the impact of crop cultivation on local biodiversity via habitat loss was assessed. Data on species richness in natural ecosystems and croplands were collected via an extensive literature search. The relative species loss of four species groups (arthropods, birds, mammals, and vascular plants) was calculated for four crop groups (oil palm, low crops, small Pooideae grasses and large Panicoideae grasses) grown throughout six biomes. Vascular plant richness was found to be more sensitive to agricultural land use than the species richness of arthropods. Regarding the differences between world regions, the impact of agricultural land use was lower in boreal forests and taiga than in temperate and tropical regions. Crop type had little influence on the extent of relative species loss. Analysis of a subset of data showed that the impact of conventional farming was generally larger than the impact of low-input farming.
Chapter 6 converts GHG emissions, habitat loss and water use into impact on terrestrial relative global species richness of biofuels compared to fossil fuels. The focus is on four types of first generation biofuels, located in the main biofuel-producing countries. 209
Applying a plantation time of 30 years and addressing the impacts of GHG emissions over a time horizon of 100 years, the median relative species loss of all studied biofuel production systems was a factor 30 to 128 higher compared to fossil gasoline and diesel. Bioethanol from Chinese corn showed on average the highest biodiversity impacts, while biodiesel from European rapeseed had on average a relatively low impact. Habitat loss was the dominating cause of the predicted relative species loss. In almost all scenarios, replacing fossil fuels with biofuels leads to an increased loss of relative species richness. Only when adopting a long-term time horizon of 1000 years, a small number of rapeseed- cultivating locations in the EU would cause less relative species loss compared to fossil fuels.
In Chapter 7, the results across the chapters are compared and main conclusions of this PhD thesis are drawn. These are:
– GHG footprints and GHG payback times show the same insights if biogenic carbon emissions dominate the results. If fossil emissions dominate, however, these two metrics do not linearly correlate. Both metrics are useful for a comparison of GHG life cycle emissions between biofuels and fossil fuels. – GHG footprints and biodiversity footprints of biofuels show no correlation, as the biodiversity footprints are dominated by the effects of habitat loss rather than GHG emissions. – Increasing crop yields through fertilizer application typically outweigh the GHG emissions from fertilizer production, and thereby reduce the GHG footprint of biofuels. – Displacing fossil fuels with first generation biofuels is often not preferable both from a GHG footprint as well as biodiversity footprint point of view. 210 Samenvatting
Samenvatting
Fossiele brandstoffen zijn sinds de industriële revolutie de belangrijkste energiebron ter wereld. Het grootschalig gebruik van fossiele brandstoffen heeft geleid tot een grote toename van broeikasgassen (BKG’s) in de atmosfeer dat leidt tot klimaatverandering. In een poging om de klimaatverandering te verminderen, zijn alternatieve energiebronnen ontwikkeld. Voor het wegverkeer omvat dit verschillende soorten biobrandstoffen gemaakt van suiker- en olierijke gewassen, zoals maïs, soja en koolzaad. De verbranding van biobrandstoffen stoot netto minder broeikasgassen uit dan fossiele brandstoffen, aangezien alle koolstof die vrijkomt bij de verbranding eerst uit de atmosfeer is gehaald tijdens de groei van het gewas. Of biobrandstoffen werkelijk beter presteren dan fossiele brandstoffen gedurende de gehele levenscyclus is echter de vraag, aangezien de teelt van de gewassen vruchtbare grond, bemesting en irrigatie vereist. Naarmate de vraag naar biobrandstoffen toeneemt, kunnen nieuwe landbouwgronden de plek van natuurlijke ecosystemen innemen. Dit kan leiden tot het (lokaal) uitsterven van soorten die hun leefgebied verliezen. Bovendien slaat de biomassa en de bodem van natuurlijke ecosystemen grote hoeveelheden koolstof op, die bij verstoring van het ecosysteem in de atmosfeer terechtkomen. Zodra de landbouwgronden in gebruik zijn, zullen ook BKG’s worden uitgestoten door landbouwmachines en tijdens de productie en verspreiding van meststoffen. Op deze manier kunnen biobrandstoffen uiteindelijk zelf klimaatverandering versterken en de biodiversiteit verminderen. Verschillende studies hebben laten zien dat het mogelijk afhankelijk is van het type biobrandstof (en het gewas waarvan deze gemaakt wordt) en de locatie van de teelt, of de productie van biobrandstoffen schoner is dan de productie van fossiele brandstoffen vanuit een BKG-oogpunt. Een schematische analyse van de mogelijke effecten van de productie van biobrandstoffen op mondiale schaal is echter nog niet uitgevoerd. Het doel van dit proefschrift was om de impact van de productie van biobrandstoffen op de uitstoot van broeikasgassen en biodiversiteit te beoordelen op wereldschaal.
In hoofdstuk 2 werd de ruimtelijke variatie in BKG-balansen van biobrandstoffen beoordeeld, veroorzaakt door regio-specifieke factoren zoals klimaat, bodemtype, landbouw intensiteit en het type natuurlijk ecosysteem dat werd verwijderd ten gunste van het akkerland. Dit werd specifiek gedaan voor de productie van bioethanol op basis van maïs en biodiesel op basis van soja in de Verenigde Staten van Amerika. Een aantal belangrijke bronnen van BKG-uitstoot werden beschouwd, waaronder verwijdering van natuurlijke biomassa en organische koolstof- en stikstofvoorraden in natuurlijke bodems door het geschikt maken van natuurlijke gronden voor landbouw, de productie van materialen voor de landbouw (bijv. zaden, pesticiden en meststoffen), gebruik van stikstofkunstmest, en het energieverbruik door landbouwmachines en irrigatie. De productie van biodiesel op
basis van soja grotere levert een BKG-emissies voetafdruk op van 52 kg CO2-eq per GJ met
een range van -0.8 tot 120 kg CO2-eq per GJ [95% range]. De productie van bioethanol op 211
basis van maïs heeft een gemiddelde BKG-voetafdruk van 63 kg CO2-eq per GJ met een range van -85 tot 310 kg CO2-eq per GJ [95% range]. De BKG-balansen van beide biobrandstoffen waren het sterkst afhankelijk van de CO2-uitstoot als gevolg van de vernietiging van de natuurlijke biomassa en de keuze van de totale periode van de teelt. Daarnaast leiden lokale variatie in de koolstof- en stikstofvoorraden, de behoeften aan meststoffen en de gewasopbrengsten tot aanzienlijke verschillen in de BKG-voetafdruk van biobrandstoffen. Onze resultaten laten zien dat het belangrijk is om land transformatie mee te nemen in de risicobeoordeling van landbouwproducten, en de toegevoegde waarde van het gebruik van ruimtelijk-expliciete modellen in deze risicobeoordelingen.
Hoofdstuk 3 behandelt de zogenaamde BKG-terugverdientijd (BKG-TVT) van vijf soorten eerste generatie biobrandstoffen, namelijk bio-ethanol uit maïs, suikerriet en wintertarwe en biodiesel uit koolzaad en sojabonen. De BKG-TVT is het aantal jaren voordat het klimaatvoordeel van het vervangen van fossiele brandstoffen met biobrandstoffen het verlies van koolstof- en stikstofvoorraden uit het oorspronkelijke ecosysteem tijdens landtransformatie compenseert. Deze BKG-TVT’s zijn berekend met behulp van ruimtelijk expliciete gegevens over biogene koolstofdioxide- en distikstofoxide-emissies van natuurlijke biomassa en bodem, de uitstoot van distikstofoxide door de toepassing van stikstofmeststoffen, potentiële opbrengsten van gewassen, en land-specifieke gegevens over fossiele broeikasgasemissies door gebruik van machines en irrigatie. De vijf biobrandstofgewassen die onder twee landbouwstrategieën worden geteeld, resulteerden in een variatie binnen BKG-TVT’s van -10 jaar tot 417 jaar [95% range]. Het telen van de grondstoffen mét fertilisatie en irrigatie verminderde de BKG-TVT van de biobrandstoffen, omdat bemesting en irrigatie resulteerden in een verhoogde gewasopbrengst die de negatieve effecten van verhoogde broeikasgasemissies van landbouwactiviteiten compenseerde. De variatie in BKG-TVT’s werd echter voornamelijk bepaald door de locatie van de gewasteelt, terwijl verschillen in gewastype en de landbouwintensiteit minder belangrijk bleken.
In hoofdstuk 4 zijn ook BKG-TVT’s afgeleid voor de productie van biobrandstoffen uit maïs, suikerriet, koolzaad en soja. In plaats van het gebruik van modellen voor het simuleren van potentiële opbrengsten van biobrandstofgewassen over de hele wereld, lag de focus van dit hoofdstuk op de BKG-TVT’s van de huidige teelt in biobrandstof- producerende landen. De resultaten laten zien dat voor de productie van gewassen in Brazilië, Italië, Oostenrijk en China landbouwgronden gedurende vele decennia tot enkele eeuwen moeten worden gebruikt voordat de broeikasgasschuld van landtransformatie is terugbetaald. Biobrandstofproductie uit koolzaad in Duitsland, soja in Argentinië en maïs of soja uit de VS bleek een geschikter alternatief te zijn, met een gemiddelde BKG-TVT van minder dan 40 jaar.
In hoofdstuk 5 is de impact van teelt van landbouwgewassen op lokale biodiversiteit via habitatverlies gekwantificeerd. Gegevens over soortenrijkdom in natuurlijke ecosystemen 212 Samenvatting
en akkers werden verzameld middels een uitgebreid literatuuronderzoek. Het relatieve soortverlies van vier soorten groepen (geleedpotigen, vogels, zoogdieren en vasculaire planten) werd berekend voor vier gewasgroepen (oliepalmen, lage gewassen, granen en grote grasgewassen) in zes wereldregio’s. De soortenrijkdom van vasculaire planten bleek gevoeliger te zijn voor landbouwgrondgebruik dan de soortenrijkdom van geleedpotigen. Wat de verschillen tussen de regio’s in de wereld betreft, was de impact van het gebruik van landbouwgrond lager in boreale bossen en taiga dan in gematigde en tropische regio’s. Het gewastype had weinig invloed op de mate van relatief soortenverlies. Analyse van een deel van de gegevens toonde aan dat de impact van conventionele landbouw over het algemeen groter was dan de impact van landbouw met een meer organische aanpak.
Hoofdstuk 6 combineert de kennis over BKG-emissies en habitatverlies en kwantificeert de impact van het vervangen van fossiele brandstoffen met biobrandstoffen op de terrestrische biodiversiteit. De nadruk ligt op vier soorten biobrandstoffen van de eerste generatie, gelegen in de belangrijkste biobrandstof-producerende landen, en de relatieve mondiale soortenrijkdom is gebruikt als een indicator voor de biodiversiteit. Locatie- specifieke impacts zijn afgeleid met behulp van ruimtelijk expliciete gegevens over de gewasproductie en ecoregio-specifieke impactfactoren voor habitatverlies. Door een cultivatietijd van 30 jaar toe te passen en de impact van BKG’s over een tijdshorizon van 100 jaar mee te rekenen, was het relatieve soortverlies van alle bestudeerde biobrandstofproductiesystemen een factor 30 tot 128 hoger vergeleken met fossiele benzine en diesel. Bio-ethanol uit Chinese maïs presteerde het slechtst en biodiesel, terwijl Europees raapzaad het minst slecht presteerde. Habitatverlies domineerde als veroorzaker van het voorspelde relatieve soortenverlies door de productie van biobrandstoffen. Het vervangen van fossiele brandstoffen door biobrandstoffen leidt in veruit de meeste gevallen tot een verhoogd verlies van relatieve soortenrijkdom. Alleen wanneer lange termijn effecten tot 1000 jaar worden meegenomen, leidt de productie en gebruik van biobrandstoffen in een klein aantal koolzaad-produceren locaties in de EU tot minder relatief soortenverlies in vergelijking tot fossiele brandstoffen.
De belangrijkste conclusies van dit proefschrift zijn:
– BKG-balansen en BKG-TVT’s bieden dezelfde inzichten wanneer biogene koolstofemissies de resultaten domineren. Echter, wanneer de fossiele emissies de resultaten domineren, dan is er geen lineaire correlatie tussen beiden metrieken. Beide indicatoren zijn nuttig om te gebruiken om de BKG-effecten van biobrandstoffen te beoordelen. – Er is geen correlatie gevonden tussen de BKG- en de biodiversiteitsvoetafdruk, omdat de biodiversiteitsvoetafdruk wordt gedomineerd door de effecten van habitatverlies en niet door BKG-emissies. 213
– BKG-emissies veroorzaakt door bemesting worden veelal gecompenseerd door de daaropvolgende stijging van de gewasopbrengst, waardoor de BKG-balans van biobrandstoffen daalt. – Het vervangen van fossiele brandstoffen door biobrandstoffen van de eerste generatie leidt eerder tot een stijging dan tot een daling van klimaatverandering en effecten op biodiversiteit.
215
Dankwoord
Tijdrovende veranderingen in artikelen, slepende reviewprocessen, trage Excel- berekeningen, allerlei sociale verplichtingen, een fikse huwelijksreis, en een carrièreswitch met bijbehorende studie, en opeens ben je 7,5 jaar bezig met je thesis. Maar de aanhouder wint, getuigen het boekje dat je nu in handen hebt. Hoewel alleen mijn naam op de kaft staat, ben ik niet de enige die credits voor dit werk zou moeten krijgen. Er zijn een aantal mensen die ik wil bedanken voor hun hulp en ondersteuning gedurende dit lange traject.
Allereerst moet ik Mark bedanken, zonder wie dit proefschrift er nooit was geweest. Het hele promotietraject werd geïnitieerd toen jij me in het najaar van 2011 belde met de vraag of ik, na mijn Master, nog wat langer op de afdeling wilde blijven. Het onderzoeksvoorstel had je al liggen, maar je zocht nog iemand die ervaring had met verzamelen van data uit wetenschappelijk literatuur. Ik wist destijds niet of ik daadwerkelijk het onderzoek in wilde, maar vier jaar extra bedenktijd was erg aanlokkelijk. Toen de afronding van het traject enkele jaren later wat stroef begon te lopen omdat ik toch voor een carrièreswitch koos, wist je precies welke afspraken je met mij moest maken. Dit zorgde ervoor dat ik netjes (vrijwel) wekelijks naar Nijmegen kwam om aan het boekje te werken. Natuurlijk was je ook inhoudelijk onmisbaar, en wist je altijd een originele draai te geven aan een onderzoek wanneer de nieuwswaarde mij op het eerste gezicht niet duidelijk was. Ik heb ontzettend veel van je geleerd, en ook veel met je kunnen lachen. Dank voor je hulp, vertrouwen en de leuke tijd!
Daarna wil ik mijn twee copromotoren bedanken. Rosalie, jij was altijd degene die de haalbaarheid van een nieuw idee bewaakte. Daarbij was je altijd een fijn aanspreekpunt, zowel voor inhoudelijke vragen als voor een praatje. Gedeelde smart is halve smart, dus in die zin was het mooi dat we gezamenlijk konden klagen over hoe lastig het soms was om voor onze data afhankelijk te zijn van derden.
Marijn, jij hield altijd in de gaten of een onderzoek voldoende toegevoegde waarde had, ook voor mensen buiten de universitaire onderzoekswereld. Je andere invalshoek en resultaatgerichte feedback heeft mijn papers naar een hoger niveau gebracht. Zo was jij de eerste die het woord Nature in de mond durfde te nemen, en kijk eens waar dat avontuur geeindigd is.
Next, I would like to thank Ramkumar and Aranya from ExxonMobil, for giving me the opportunity to start this adventure. Visiting and working at Exxon for two months has been a very special experience that I will treasure forever. 216 Dankwoord
Vervolgens wil ik graag mijn collega’s van de afdeling Milieukunde bedanken, door wie ik altijd met veel plezier afreisde naar Nijmegen. Enkele (ex-)collega’s verdienen het om in het bijzonder te worden genoemd, juist omdat ik dankzij hen niet altijd aan werken toe kwam, en die daardoor mijn tijd bij Milieukunde werkelijk onvergetelijk hebben gemaakt. Rik, mijn KanJam partner op Europees topniveau. Wat waren wij goed. Helaas kunnen we niet meer samen trainen, maar met ons talent moet een gouden medaille toch eens haalbaar zijn. Greg, a.k.a. Joris. Uit het oog, maar zeker niet uit het hart. Het voelt als een eeuwigheid geleden dat je de afdeling verrijkte met je droge humor en je tomeloze inzet, die je perfect wist te maskeren met een schil van lamlendigheid. Wat heb ik veel kunnen lachen met je. Frank, Remon en Steef. Bedankt voor de vele goede, relativerende gesprekken, de wandelingen, het gamen en het pranken. Veel succes met jullie eigen promoties! Thomas. Tja. Lisette. Soms voelde het alsof wij lotgenoten waren; gestrand in een onderzoekswereld die niet geheel bij ons paste. Bedankt voor de fijne, openhartige gesprekken, het lachen, het stappen, en je gastvrijheid. Was er buiten werktijd nog iets te doen in Nijmegen, dan kon ik bij jou terecht voor een heerlijke maaltijd of een gespreid logeerbed. Martijn, Mad Man. Degene die qua humor beangstigend vaak op de dezelfde golflengte zat. Jij hebt de afgelopen jaren gezorgd voor de broodnodige geestelijke én lichamelijke ontspanning. Ik weet niet of we kunnen blijven fitnessen (lees: pingpongen), maar ik vrees dat onze hardnekkige vriendschap sowieso zal voortbestaan.
En dan natuurlijk ook mijn paranimfen:
Zoran. Jouw bijdrage aan dit proefschrift is onmiskenbaar, want je bent verantwoordelijk voor haast de helft van de figuren in dit proefschrift. Je offerde er zelfs een zondagmiddag voor op om bij te springen wanneer ik dezelfde dag een manuscript opnieuw moest indienen, en Mark last minute had bedacht dat er extra figuren nodig waren. Naast de inhoudelijke hulp was het ook altijd prettig ontspannen met jou: Vogels spotten rondom het Huygensgebouw, in Wijchen of in de Millingerwaard, eindeloos ouwehoeren over vogels, vakanties, Pokémon of wat dan ook. En dan waren we ook nog een aantal keer reisgenoten. Samen ExxonMobil “ervaren”, te voet New York doorkruizen, tailgaten in New Jersey, stappen in Trondheim; we hebben onvergetelijke dingen meegemaakt. Als dank voor alles wil ik je graag twee tips meegeven die onmisbaar zijn voor je verdere carrière als wetenschapper: Laat u niet op uwe kop kakken en have a good one, sir!
Jelle. Ik zou je kunnen vertellen hoe blij ik ben dat je er dit keer wél bij was, maar ik vind dat we geen oude koeien uit de sloot moeten halen. Jij was mijn maatje op de afdeling. Altijd bereid om het werk/dumpert/9gag/nu.nl/wielrennen te onderbreken voor een korte (of lange) wandelpauze en altijd beschikbaar als luisterend oor. Dit is zeer waardevol voor mij geweest, zowel voor mijn carrière als privé. Verder wil ik ook jou bedanken voor de mooie reisjes die we hebben gemaakt naar Londen en Trondheim, en je hulp met de statistiek. Na jouw promotie eind vorig jaar, en de mijne vandaag, komt een tijdperk ten einde, maar laten we ervoor zorgen dat we elkaar nog regelmatig blijven zien. 217
Ook thuis heb ik mij altijd gesteund geweten.
Pap. Jij hebt vroeger nooit de kans gekregen om te studeren, maar hebt mij altijd gestimuleerd om dat wel te doen. Dat de familie nu zijn eerste doctor krijgt is dus ook aan jou te danken. Dat je bovendien ook van onschatbare waarde was tijdens het praktische werk van mijn Master moge duidelijk zijn. Ik denk dat de hagedissen nog steeds praten over die man die het ene moment met een wandelstok door de hei struinde om zich vervolgens bovenop hen te storten als een luipaard op een antilope. Ik hoop dat we nog lange tijd samen kunnen blijven genieten van de natuur.
Mam. Sorry dat je voor mij je dierbare Brabants Dagblad hebt moeten inruilen voor de meer wetenschappelijk-onderlegde Volkskrant. Bedankt voor de steun, niet in de laatste plaats door het mogelijk maken van mijn reis naar Nijmegen door het continu uitlenen van je auto.
Roos, lieve schat. Gedurende het lange traject van deze promotie is er best veel veranderd in onze levens, en het was niet altijd even makkelijk. Ook al heb je zelf nog niet alles op de rit, voor mij geld je als de stabiele factor in mijn leven die er voor zorgt dat ik me kan focussen op werk, promotie en hobby’s. Dank je wel dat je er voor me bent.
219
Curriculum vitae
On September 22nd 1987 I was born in Waalwijk, The Netherlands. I grew up in the nearby town of Kaatsheuvel. In 2012, I moved back to Waalwijk, where I still live today. After graduating in 2005, I started studying Biology at the Radboud University in Nijmegen, where I received my master’s degree in Environmental Sciences in 2011. In January 2012, I started working at the Department of Enivironmental Science, on a project on uncertainty and variability in land use modelling for Life Cycle Assessment. The first two years of my PhD trajectory were funded by ExxonMobil, and I worked at their research lab for two months. After that I worked primarily for a project called DESIRE (DEvelopment of a System of Indicator for a Resource efficient Europe). After four years of research, I decided to switch to a career as Biology teacher in secondary education. I received my master’s degree in Education in 2017. Thereafter, I worked at secondary schools in Uden, Helmond and Waalwijk, before getting my current job at Graaf Engelbrecht in Breda. In the meantime, I finalized my PhD thesis.
221
Publications
Peer-review publications
Azevedo, L.B., Van Zelm, R., Elshout, P.M.F., Hendriks, A.J., Leuven, R.S.E.W., Struijs, J., De Zwart, D. & Huijbregts, M.A.J. (2013) Species richness-phosphorus relationships for lakes and streams worldwide. Global Ecology and Biogeography 22: 1304-1314
Elshout, P.M.F., Dionisio Pires, L.M., Leuven, R.S.E.W., Wendelaar Bonga, S.E. & Hendriks, A.J. (2013) Low oxygen tolerance of different life stages of temperate freshwater fish species. Journal of Fish Biology 83: 190-206
Elshout, P.M.F., Van Zelm, R., Karuppiah, R., Laurenzi, I.J. & Huijbregts, M.A.J. (2014) A spatially explicit data-driven approach to assess the effect of agricultural land occupation on species groups. International Journal of Life Cycle Assessment 19(4): 758-769
Elshout, P.M.F., Van Zelm, R., Balkovic, J., Obersteiner, M., Schmid, E., Skalsky, R., Van der Velde, M. & Huijbregts, M.A.J. (2015) Greenhouse-gas payback times for crop-based biofuels. Nature Climate Change 5: 604-610
Elshout, P.M.F., Van Zelm, R., Van der Velde, M., Steinmann, Z. & Huijbregts, M.A.J. (2019) Global relative species loss due to first generation biofuel production for the transport sector. GCB Bioenergy https://doi.org/10.1111/gcbb.12597
Van Zelm, R., Van der Velde, M., Balkovic, J., Čengić, M., Elshout, P.M.F., Koellner, T., Núñez, M., Obersteiner, M., Schmid, E. & Huijbregts, M.A.J. (2018) Spatially explicit life cycle impact assessment for soil erosion from global crop production. Ecosystem Services 30B: 220-227
Non peer-reviewed publications
Huijbregts, M.A.J., Steinmann, Z.J.N., Elshout, P.M.F., Stam, G., Verones, F., Vieira, M.D.M., Hollander, A., Zijp, M. & Van Zelm, R. (2016) ReCiPe 2016: A harmonized life cycle impact assessment method at midpoint and endpoint level. Report I: Characterization. Climate and biodiversity impacts of crop-based biofuels
Climate and biodiversity impacts of crop-based biofuels Pieter Elshout
Pieter Elshout