Bioenergy and Land Use Change E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Serie: Geophysical Monograph Series
• Sprache: Englisch

Seitenzahl: 675

Veröffentlichungsjahr: 2017

Beschreibung

Although bioenergy is a renewable energy source, it is not without impact on the environment. Both the cultivation of crops specifically for use as biofuels and the use of agricultural byproducts to generate energy changes the landscape, affects ecosystems, and impacts the climate. Bioenergy and Land Use Change focuses on regional and global assessments of land use change related to bioenergy and the environmental impacts. This interdisciplinary volume provides both high level reviews and in-depth analyses on specific topics. Volume highlights include: * Land use change concepts, economics, and modeling * Relationships between bioenergy and land use change * Impacts on soil carbon, soil health, water quality, and the hydrologic cycle * Impacts on natural capital and ecosystem services * Effects of bioenergy on direct and indirect greenhouse gas emissions * Biogeochemical and biogeophysical climate regulation * Uncertainties and challenges associated with land use change quantification and environmental impact assessments Bioenergy and Land Use Change is a valuable resource for professionals, researchers, and graduate students from a wide variety of fields including energy, economics, ecology, geography, agricultural science, geoscience, and environmental science. Read an interview with the editors to find out more: https://eos.org/editors-vox/bioenergys-impacts-on-the-landscape

Leseprobe

Table of Contents

Cover

Title Page

Contributors

Preface

Part I: Bioenergy and Land Use Change

1 Bioenergy and Land Use Change

1.1. INTRODUCTION

1.2. LAND USE CHANGE AND CURRENT RESEARCH

1.3. MODELING EFFORTS AND METHODOLOGIES ESTIMATING LAND USE CHANGE AND BIOENERGY MARKETS

1.4. EMPIRICAL EVIDENCE AND MODELING CHALLENGES

1.5. TECHNOLOGICAL AND POLICY IMPERATIVES AND CERTIFICATION

1.6. MOVING FORWARD

ACKNOWLEDGMENTS

REFERENCES

2 An Exploration of Agricultural Land Use Change at Intensive and Extensive Margins

2.1. INTRODUCTION

2.2. BACKGROUND

2.3. RECENT CHANGES IN CROPLAND COVER AND HARVESTED AREA: STATISTICAL ANALYSIS

2.4. MODEL MODIFICATIONS

2.5. EXPERIMENTS AND RESULTS

2.6. CONCLUSIONS

APPENDIX 2.A. WEDGE BETWEEN CROPLAND AND HARVESTED AREA

APPENDIX 2.B. EXPERIMENTS BASED ON THE 2011 DATABASE AND THE ASSOCIATED MODELS

REFERENCES

3 Effects of Sugarcane Ethanol Expansion in the Brazilian Cerrado

3.1. INTRODUCTION

3.2. CONCEPTUAL FRAMEWORK

3.3. METHODS

3.4. RESULTS

3.5. DISCUSSION

3.6. CONCLUSION

ACKNOWLEDGMENT

REFERENCES

4 Biofuel Expansion and the Spatial Economy

4.1. INTRODUCTION

4.2. SUGARCANE, SOYBEAN, AND CATTLE‐RANCHING EXPANSION

4.3. LAND RENTS AND LAND USE DISPLACEMENT

4.4. DISCUSSION

4.5. CONCLUSIONS

ACKNOWLEDGMENTS

REFERENCES

Part II: Impacts on Natural Capital and Ecosystem Services

5 Toward Life Cycle Analysis on Land Use Change and Climate Impacts from Bioenergy Production

5.1. INTRODUCTION

5.2. BIOMASS PRODUCTION AND LAND USE CHANGE

5.3. LAND USE CHANGE AND CLIMATE IMPACTS

5.4. CLIMATE IMPACTS IN BIOENERGY LIFE CYCLE ANALYSIS

5.5. CONCLUSIONS

ACKNOWLEDGMENTS

REFERENCES

6 Bioenergies impact on Natural Capital and Ecosystem Services

6.1. INTRODUCTION

6.2. NATURAL CAPITAL AND ECOSYSTEM SERVICES IMPACTS OF THE ENERGY SECTOR

6.3. NATURAL CAPITAL AND ECOSYSTEM SERVICES IMPACTS OF BIOENERGY

6.4. NATURAL CAPITAL AND ECOSYSTEM SERVICES IMPACTS OF COAL

6.5. COMPARISON OF IMPACTS AND DISCUSSION

ACKNOWLEDGMENT

REFERENCES

7 Empirical Evidence of Soil Carbon Changes in Bioenergy Cropping Systems

7.1. INTRODUCTION

7.2. SOIL ORGANIC C

7.3. SAMPLING CONSIDERATIONS

7.4. LAND USE EFFECTS ON SOIL ORGANIC CARBON

7.5. LAND MANAGEMENT EFFECTS ON SOIL ORGANIC CARBON

7.6. SUMMARY

REFERENCES

8 The Importance of Crop Residues in Maintaining Soil Organic Carbon in Agroecosystems

8.1. INTRODUCTION

8.2. DEVELOPING CARBON BUDGETS

8.3. DATA USED TO POPULATE SIMULATION MODELS

8.4. RESIDUE HARVESTING IMPACT ON NUTRIENT REMOVAL AND CROP YIELDS

ACKNOWLEDGMENT

REFERENCES

9 Incorporating Conservation Practices into the Future Bioenergy Landscape

9.1. INTRODUCTION

9.2. ANALYTICAL METHODS

9.3. STUDY AREA

9.4. SCENARIOS

9.5. RESULTS AND DISCUSSION

9.6. CONCLUSIONS

REFERENCES

Part III: Data, Modeling, and Uncertainties

10 Uncertainty in Estimates of Bioenergy‐Induced Land Use Change

10.1. INTRODUCTION

10.2. LULC DATA VERSUS LULC CHANGE

10.3. INCONSISTENCY IN CLASSIFICATION OF LAND USE/LAND COVER DATA SETS

10.4. AMBIGUITY DUE TO AGGREGATION OF CLASSES

10.5. UNCERTAINTY DUE TO PIXEL‐TO‐PIXEL AND TWO‐POINT COMPARISON

10.6. CONCLUSIONS

ACKNOWLEDGMENTS

REFERENCES

11 Challenges in Quantifying and Regulating Indirect Emissions of Biofuels

11.1. BACKGROUND

11.2. INDIRECT EMISSIONS OF BIOFUELS

11.3. CHALLENGES IN MANAGING INDIRECT EMISSIONS

11.4. CONCLUSION

ACKNOWLEDGMENTS

REFERENCES

12 Biofuels, Land Use Change, and the Limits of Life Cycle Analysis

12.1. INTRODUCTION

12.2. BIOENERGY AND CLIMATE

12.3. LIFE CYCLE ASSESSMENT

12.4. ESTIMATING THE CLIMATE EFFECTS OF BIOENERGY USE

12.5. CONCLUSION

APPENDIX 12.A. ESTIMATING THE CLIMATE EFFECTS OF BURNING BIOMASS

REFERENCES

13 Lost Momentum of Biofuels

13.1. INTRODUCTION

13.2. INVESTMENT ON BIOFUEL PRODUCTION CAPACITY BEFORE AND AFTER 2010

13.3. IMPACTS OF GLOBAL FOOD CRISIS ON BIOFUEL POLICIES

13.4. ILUC DEBATE AND CHANGE IN BIOFUEL POLICIES

13.5. OIL PRICE AND BIOFUEL MARKET

13.6. DEMAND FOR PETROLEUM PRODUCTS AND PRICE COMPETITIVENESS OF BIOFUELS

13.7. FAILURE OF ADVANCED BIOFUELS’ DELIVERY

13.8. KEY CONCLUSIONS

REFERENCES

Index

End User License Agreement

List of Tables

Chapter 02

Table 2.1 Cropland Cover, Harvested Area, and Their Ratio in 2003 and Their Annual Growth to 2013

Table 2.2 Tuned Parameters According to Observed Trends in the Ratios of HOL and COH in 2003–2013

Table 2.3 Land Use Changes Due to Expansion in Corn Ethanol, Using 2004 Database (in 1000 ha)

Table 2.4 Changes in Cropland and Harvested Area Due to Expansion in Corn Ethanol, Using 2004 Database (in 1000 ha)

Table 2.5 Land Use Changes Due to Expansion in Brazilian Sugarcane Ethanol, U.S. Soybean Biodiesel, and EU Rapeseed Biodiesel, Using 2004 Database (in 1000 ha)

Table 2.6 Land Use Emissions for U.S. Corn Ethanol, Brazilian Sugarcane Ethanol, U.S. Soybean Biodiesel, and EU Rapeseed Biodiesel, Using 2004 Database (in g CO

2

e/MJ)

Table 2.B.1 Land Use Change Due to Expansion in U.S. Corn Ethanol, Using 2011 Database (1000 ha)

Table 2.B.2 Changes in Cropland and Harvested Area Due to Expansion in U.S. Corn Ethanol, Using 2011 Database (1000 ha)

Table 2.B.3 Land Use Changes Due to Expansion in Brazilian Sugarcane Ethanol, U.S. Soybean Biodiesel, and EU Rapeseed Biodiesel, Using 2011 Database (1000 ha)

Table 2.B.4 Land Use Emissions for U.S. Corn Ethanol, Brazilian Sugarcane Ethanol, U.S. Soybean Biodiesel, and EU Rapeseed Biodiesel, Using 2011 Database (in g CO

2

e/MJ)

Chapter 03

Table 3.1 Statistics Summary of Explanatory Variables

Table 3.2 Results for the Statistical Logit Model of Land Use Responses

Table 3.3 Estimation Results for the Marginal Effects, at Means

Chapter 04

Table 4.1 Cattle Herd, Sugar Cane, and Soybean Trend Regression

t

‐Statistics for Selected States

Table 4.2 Price of Pastureland in Regions Dominated by Agriculture and Prices of Forested Lands in Cattle‐Ranching Regions in Amazonia (2011)

Chapter 05

Table 5.1 Various LUC Concepts and Their Climate Impacts Reported Under Relevant Definitions

Table 5.2 Examples of LCAs Used for Biofuel Production that Include LUC Impacts

Chapter 06

Table 6.1 Key Metrics for Quantifying the Impact of, or Consumption by, Contrasting Energy Value Chains on Natural Capital

Table 6.2 List of Ecosystem Services and the Impact of Electricity Generation Using Coal and Biomass as a Fuel

Chapter 08

Table 8.1 The Influence of Residue Harvesting, Yield Zone, and Tillage on the Amount of Relic C Lost,

K

soc

,

K

nhc

, and the Maintenance Requirement

Table 8.2 The Impact on Corn Yields of Removing 60% of Corn Residue Annually

Chapter 09

Table 9.1 Model Performances for Flow, SS, NO

3

, and TP During Calibration and Validation Periods Using Different Statistical Methods at USGS Gauging Station (05451210)

Table 9.2 Study Scenarios

Table 9.3 Changes in the 20 Year Average Annual Flow: Sediment and Nutrient Loading Levels Relative to the BASE Scenario

Chapter 10

Table 10.1 Transitions Between Land Cover Classes from 2006 to 2011 for the Western Corn Belt States, Using CDL Data

Table 10.2 Transitions of Land Cover from 2006 to 2011 for the Western Corn Belt States, Using CDL Data and Trajectories Method [

Lark et al

., 2015]

Table 10.3 Total Area (1000 ha) of Various Grass Cover and Idle Lands as per the CDL Data for 2006, 2011, and 2012 for the Western Corn Belt States

Chapter 12

Table 12.A.1 CO

2

‐Equivalent Global Warming Potentials for 20 and 100 Year Time Horizons

Table 12.A.2 Emissions Factors for Trace Gases and Aerosols for Savanna and Tropical FOREST fires, in Mass of Dry Matter (g kg

−1

) [

Andreae and Merlet

, 2001] and as CO

2

‐Equivalents (g CO

2

e kg

−1

) for 20 and 100 Year Time Horizons Using IPCC AR4 Global Warming Potentials [

Forster et al

., 2007]

List of Illustrations

Chapter 01

Figure 1.1 Word cloud representing the 50 most frequent words in the text analysis.

Figure 1.2 Private forest acreage change for five forest types in Southern United States comprising 13 states under (a) no biomass diverted to energy scenario and (b) moderate consumption of woody biomass for energy scenario.

Chapter 02

Figure 2.1 Area of cropland, harvested area, and their ratio by region in 2003–2013. Three regions including Japan, East Asia, and the Other Europe with limited cropland and harvested area are dropped from this figure.

Figure 2.2 Annual growth rate in crop yield by region in 2003–2013.

Chapter 03

Figure 3.1 Sugarcane fields in the states of Goiás and Mato Grosso do Sul.

Figure 3.2 Distance from sugarcane fields to mills, 2013.

Figure 3.3 Location of paved road and presence of sugarcane fields, 2013.

Figure 3.4 Annual total expansion of sugarcane area and the classification into intensification and extensification responses, in Goiás and Mato Grosso do Sul.

Figure 3.5 Cumulative intensification and extensification promoted by sugarcane expansion in the states of Goiás and Mato Grosso do Sul, 2006–2013: (A) calling attention to the southeast of Goiás, a region with a large presence of intensification; (B) calling attention to the southeast of Mato Grosso do Sul, a region with a large presence of extensification.

Chapter 04

Figure 4.1 Brazilian Amazon forest and states.

Figure 4.2 Sugarcane planted area, production, and geographic distribution in Brazil, 1990–2014.

Figure 4.3 Soybean planted area, production, and geographic distribution in Brazil, 1990–2014.

Figure 4.4 Growth and geographic distribution of the cattle herd in Brazil, 1974–2014.

Figure 4.5 Von Thünen’s conceptual model of locational land rents.

Figure 4.6 ILUC from capital transfers to the extensive margin.

Figure 4.7 ILUC from price elasticity effects.

Chapter 05

Figure 5.1 Land with different uses. (a) Forest in fall, upper peninsula of Michigan. (b) Grassland/pastureland in spring, Indiana. (c) Cropland in fall, South Dakota.

Figure 5.2 Examples of direct and indirect LUC due to bioenergy development. Generally, the land consists of managed (e.g., cropland) and unmanaged land (e.g., forest and grassland) in both (a) domestic and (b) international domains. (c) After bioenergy is introduced, the managed/unmanaged land may be converted to grow crops for energy use (direct LUC). Indirect LUC may occur in a (c) neighboring region or (d) even other countries in response to market shocks.

Figure 5.3 Land use change can affect climate via changes in biogeochemical and biogeophysical processes in the terrestrial ecosystems. The biogeochemical and biogeophysical changes can result in local and global impacts (see upper right legend). AGTP, absolute global temperature change potential; GWP, global warming potential; AGWP, absolute global warming potential. AGWP is cumulative radiative forcing of emission over a given time horizon. Δ indicates change.

Figure 5.4 Direct and indirect N

2

O emissions sourced from agricultural management practices and land use change. Darker colors indicate N

2

O emissions associated with land use change, while lighter ones indicate emissions normally treated as farming‐related emissions. The bar charts reflect relative amount of N

2

O emissions.

Figure 5.5 An example overview of a biofuel life cycle GHG‐emission estimate incorporating biogeochemical and biogeophysical impacts from LUC. Carbon (C) and nitrogen (N) cycling is commonly considered in LUC‐related biogeochemical processes, while albedo is recently considered for LUC‐related biogeophysical processes.

Figure 5.6 An example showing estimated life cycle GHG emissions for corn‐ (C), switchgrass‐ (S) and

Miscanthus

‐based ethanol (M). The estimates are derived from GREET parameters on the basis of

Qin et al

. [2016a] (LCA‐1 and biogeochemical) and

Cai et al

. [2016] (biogeophysical). (a) LCA‐1 is LCA approach without considering LUC effects, while (d) LCA‐3 is LCA considering both (b) biogeochemical and (c) biogeophysical impacts of LUC; see Figure 5.5. The dots show only the approximate range of each estimate in the emission chart. The percentage reduction (%) is relative to gasoline GHG emissions.

Chapter 06

Figure 6.1 A schematic of the processes considered in a life cycle assessment of bioenergy, showing the system boundary and the input and outputs considered in the process.

Figure 6.2 Example of a matrix of likelihood of an event occurring from low to high compared to the severity of the consequence from small to large of that event for four energy‐related example events.

Figure 6.3 Example of the natural capital exchanges involved in generating electricity by a biomass‐fired power station. Black ovals are categories of natural capital, and gray boxes are the processes involved in the energy value chain. Solid lines show interaction, and dotted lines show potential interaction without pollution mitigating equipment.

Figure 6.4 Example of the natural capital exchanges involved in generating electricity by a coal‐fired power station. Black ovals are categories of natural capital, and gray boxes are the processes involved in the energy value chain. Solid lines show interaction, and dotted lines show potential interaction without pollution mitigating equipment.

Chapter 07

Figure 7.1 Global land cover .

Figure 7.2 Measured changes in SOC to 1 m soil depth after land use conversions (means, 95% CI estimated from

Guo and Gifford

[2002]).

Figure 7.3 Estimated marginally productive land area in millions of hectares (Mha); redrawn from

Cai et al

. [2011].

Figure 7.4 Perennial energy crops may be grown as a rotational crop with existing annual crops or continuously within environmentally sensitive areas (e.g., near streambanks and highly erodible areas). Management factors (i.e., conversion practices, crop rotation, and rotation duration) will impact soil organic carbon (SOC) storage more for perennial energy crops grown in a rotation, while soil C storage under continuous perennial crops will be largely dependent on initial soil C.

Chapter 08

Figure 8.1 The influence of tillage intensity on the first‐order rate constants of relic carbon mineralization in seven published studies [

Clay et al

., 2010].

Figure 8.2 Relationship between the root‐to‐shoot ratio and the amount of aboveground biomass that can be harvested while still maintaining the SOC at the current level [

Clay et al

., 2010].

Figure 8.3 Soil‐test results for Iowa from Midwest Laboratories from 1997 to 2013. In this chart, year 0 is 1997.

Chapter 09

Figure 9.1 Location, land elevation, and current land use for agriculture and land cover in South Fork Iowa River.

Figure 9.2 Stream network with riparian buffer and land use change scenarios: (a) RB30, (b) RB30NHD, and (c) SWG and SWG_Stv_CC. Dots represent weather stations, and the star represents a major city.

Figure 9.3 Annual stream flow and sediment loading at the watershed outlet under the five land use and conservation practice scenarios and a comparison with the 20 year average.

Figure 9.4 Annual nitrate and phosphate loadings at the watershed outlet under the five land use and conservation practice scenarios and in comparison with the 20 year average.

Figure 9.5 Impact of land use on monthly water quality during dry (2000) and wet (2007) years.

Figure 9.6 Twenty‐year monthly average sediment, nitrate, and phosphorus loadings in response to land use change.

Figure 9.7 Average monthly precipitation and surface runoff in the SFIR watershed; evapotranspiration and water yield under the five land use scenarios.

Figure 9.8 Twenty‐year monthly surface runoff, lateral flow, groundwater flow, and tile flow under five land use scenarios in the SFIR.

Chapter 10

Figure 10.1 Estimates of grass cover (grass cover is the sum of cover in area classified as pastureland, hay, and cultivated hay) and idle cropland for the Western Corn Belt (i.e., states of Iowa, Minnesota, Nebraska, North Dakota, and South Dakota) from various data sources: Census of Agriculture (COA) available at https://www.agcensus.usda.gov/Publications/2012/, Economic Research Service (ERS) available at https://www.ers.usda.gov/data‐products/major‐land‐uses/, National Land Cover Data (NLCD) available at https://www.mrlc.gov/finddata.php, and Cropland Data Layer (CDL) available at https://www.nass.usda.gov/Research_and_Science/Cropland/Release/index.php

Chapter 11

Figure 11.1 The figure provides a graphical intuition for emissions due to price effects in the short run for biofuels. Panels (a) and (b) depict the impact of an ethanol shock on land (an input) and gasoline (the substitute to ethanol) markets, respectively. The

x

and

y

axes depict, respectively, quantity and price. Upward sloping lines represent the supply function of a commodity, while downward sloping lines denote demand.

P

, price;

L

, quantity of land;

G

, quantity of gasoline;

B

, quantity of biofuel;

D

, demand;

S

, supply. Superscripts 0 and 1 denote pre and post biofuel mandate, respectively. Subscripts

F

,

B

, and

T

denote food, biofuel, and total land, respectively. An ethanol consumption mandate shifts out the demand for land.

Chapter 12

Figure 12.1 CO

2

‐equivalent emissions from burning tropical forest for different subsets of emission species and different analytic time frames. In the three‐gases cases, only CO

2

, CH

4

, and N

2

O are included.

Chapter 13

Figure 13.1 Production trends of biofuels at the global level.

Figure 13.2 Growth rates of biofuel production at the global level.

Figure 13.3 Indices (value for year 2010 = 100) of total biofuel production capacity (represented by cumulative investments) and annual biofuel production.

Figure 13.4 Indices of total biofuel production capacity (represented by cumulative investments) and annual biofuel production.

Figure 13.5 Indices representing trends of annual investments in various renewable energy technologies (value for year 2010 = 100).

Figure 13.6 Indices of petroleum prices and biofuel production (value for 2010 = 100).

Figure 13.7 Indices showing growth of petroleum products, biofuels, and ratio of biofuels to petroleum products for road transportation.

Figure 13.8 Installed capacity of second‐generation biofuels as of 2015 (in million liters and percentage).

Guide

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Geophysical Monograph Series

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Arctic Sea Ice Decline: Observations, Projections, Mechanisms, and Implications

Eric T. DeWeaver, Cecilia M. Bitz, and L.‐Bruno Tremblay (Eds.)

181

Midlatitude Ionospheric Dynamics and Disturbances

Paul M. Kintner, Jr., Anthea J. Coster, Tim Fuller‐Rowell, Anthony J. Mannucci, Michael Mendillo, and Roderick Heelis (Eds.)

182

The Stromboli Volcano: An Integrated Study of the 2002–2003 Eruption

Sonia Calvari, Salvatore Inguaggiato, Giuseppe Puglisi, Maurizio Ripepe, and Mauro Rosi (Eds.)

183

Carbon Sequestration and Its Role in the Global Carbon Cycle

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184

Carbon Cycling in Northern Peatlands

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,

Lisa R. Belyea, Xavier Comas, A. S. Reeve, and Lee D. Slater (Eds.)

185

Indian Ocean Biogeochemical Processes and Ecological Variability

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186

Amazonia and Global Change

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187

Surface Ocean–Lower Atmosphere Processes

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Diversity of Hydrothermal Systems on Slow Spreading Ocean Ridges

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189

Climate Dynamics: Why Does Climate Vary?

De‐Zheng Sun and Frank Bryan (Eds.)

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The Stratosphere: Dynamics, Transport, and Chemistry

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Rainfall: State of the Science

Firat Y. Testik and Mekonnen Gebremichael (Eds.)

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Antarctic Subglacial Aquatic Environments

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Abrupt Climate Change: Mechanisms, Patterns, and Impacts

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Stream Restoration in Dynamic Fluvial Systems: Scientific Approaches, Analyses, and Tools

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Monitoring and Modeling the Deepwater Horizon Oil Spill: A Record‐Breaking Enterprise

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Extreme Events and Natural Hazards: The Complexity Perspective

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Dynamics of the Earth’s Radiation Belts and Inner Magnetosphere

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Lagrangian Modeling of the Atmosphere

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Modeling the Ionosphere‐Thermosphere

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The Mediterranean Sea: Temporal Variability and Spatial Patterns

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Future Earth – Advancing Civic Understanding of the Anthropocene

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Hawaiian Volcanoes: From Source to Surface Rebecca

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Global Vegetation Dynamics: Concepts and Applications in the MCf1 Model

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Terrestrial Water Cycle and Climate Change Natural and Human‐Induced Impacts

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Magnetosphere‐Ionosphere Coupling in the Solar System

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Active Global Seismology

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Climate Extremes

Simon Wang (Ed.)

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Geophysical Monograph 231

Bioenergy and Land Use Change

Zhangcai Qin

Umakant Mishra

Astley Hastings

Editors

This Work is a co‐publication of the American Geophysical Union and John Wiley and Sons, Inc.

This Work is a co‐publication between the American Geophysical Union and John Wiley & Sons, Inc.

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CONTRIBUTORS

Eugenio Y. ArimaDepartment of Geography and the Environment, University of Texas, Austin, Texas, USA

Budhendra BhaduriGeographic Information Science & Technology Group, CSED, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA

Christopher BishopKansas Applied Remote Sensing Program, University of Kansas, Lawrence, Kansas, USA

Renata BlumbergDepartment of Nutrition and Food Studies, Montclair State University, Montclair, New Jersey, USA

J. Christopher BrownDepartment of Geography and Atmospheric Science, University of Kansas, Lawrence, Kansas, USA

Pralhad BurliDepartment of Earth and Environmental Studies, Montclair State University, Montclair, New Jersey, USA

Hao CaiEnergy Systems Division, Argonne National Laboratory, Argonne, Illinois, USA

Marcellus M. CaldasDepartment of Geography, College of Arts and Sciences, Kansas State University, Manhattan, Kansas, USA

Christina E. CanterEnergy Systems Division, Argonne National Laboratory, Argonne, Illinois, USA

David E. ClayDepartment of Agronomy, Horticulture & Plant Science, South Dakota State University, Brookings, South Dakota, USA

Hao CuiDepartment of Agricultural Economics, Purdue University, West Lafayette, Indiana, USA

Rebecca A. EfroymsonEnvironmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA

Gabriel GrancoDepartment of Geography, College of Arts and Sciences, Kansas State University, Manhattan, Kansas, USA

Mi‐Ae HaEnergy Systems Division, Argonne National Laboratory, Lemont, Illinois, USA

Astley HastingsSchool of Biological Sciences, University of Aberdeen, Aberdeen, UK

Virginia L. JinAgroecosystem Management Research Unit, USDA‐ARS, Lincoln, Nebraska, USA

Jude KastensKansas Applied Remote Sensing Program, University of Kansas, Lawrence, Kansas, USA

Keith L. KlineEnvironmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA

Pankaj LalDepartment of Earth and Environmental Studies, Montclair State University, Montclair, New Jersey, USA

Umakant MishraEnvironmental Science Division, Argonne National Laboratory, Argonne, Illinois, USA

Bridget O’BanionDepartment of Environmental Sciences, Ohio State University, Columbus, Ohio, USA

Richard J. PlevinTransportation Sustainability Research Center, University of California, Berkeley, California, USA

Zhangcai QinEnergy Systems Division, Argonne National Laboratory, Argonne, Illinois, USA

Deepak RajagopalInstitute of the Environment and Sustainability, University of California, Los Angeles, California, USA

Aditi RanjanMYMA Solutions LLC, Lincoln Park, New Jersey, USA

Peter RichardsBureau for Food Security, U.S. Agency for International Development, Washington, District of Columbia, USA

Marty R. SchmerAgroecosystem Management Research Unit, USDA‐ARS, Lincoln, Nebraska, USA

Nagendra SinghGeographic Information Science & Technology Group, CSED, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA

Catherine E. StewartSoil Management and Sugarbeet Research, USDA‐ARS, Fort Collins, Colorado, USA

Farzad TaheripourDepartment of Agricultural Economics, Purdue University, West Lafayette, Indiana, USA

Govinda TimilsinaDevelopment Research Group, World Bank, Washington, District of Columbia, USA

Wallace E. TynerDepartment of Agricultural Economics, Purdue University, West Lafayette, Indiana, USA

Robert T. WalkerCenter for Latin American Studies and Department of Geography, University of Florida, Gainesville, Florida, USA

Bernabas WoldeDepartment of Earth and Environmental Studies, Montclair State University, Montclair, New Jersey, USA

May WuEnergy Systems Division, Argonne National Laboratory, Lemont, Illinois, USA

PREFACE

Expanding bioenergy production has raised concerns over potential land use change (LUC) and LUC impacts on the environment. To accommodate new or additional bioenergy feedstock production, the use of land changes in the forms of land cover, land use, and land management. These changes are very likely to affect the biogeochemical and biophysical processes, which shape the environment and ecosystem functions.

The estimations and interpretations of bioenergy‐induced LUC have been uncertain and controversial, primarily due to the limitation of ground‐truth data at scale to identify LUC that is directly or indirectly associated with bioenergy development. In recent years, scientists have made progress in understanding the importance and significance of LUC and LUC impacts. New state‐of‐the‐art techniques have been utilized to clarify certain issues, for instance, remote sensing to quantify LUC observations and economic modeling to relate LUC to specific bioenergy program(s). Various aspects of LUC‐related environmental impacts have been studied in the context of bioenergy production at different scales.

This book covers a variety of interdisciplinary topics related to bioenergy development, LUC and LUC impacts, with contributions from agronomy, economics, energy, geography, earth sciences, atmospheric science, and environmental sciences. The book consists of three major parts: (I) bioenergy and land use change, (II) impacts on natural capital and ecosystem services, and (III) data, modeling, and uncertainties. Each part includes four to five chapters with different focus or perspectives. Part I focuses on bioenergy and LUC‐related definitions, mechanisms, modeling, and estimates. Part II contains individual studies and summaries on various environmental impacts, including carbon stocks, soil health, water quality, and climate impacts. Part III demonstrates methodologies, uncertainties, and challenges. It is not our intention to cover all aspects in this field or to endorse any perspective or statement but rather to report what has been done, what is being debated, and what needs to be further investigated.

This book will be a valuable resource for experts and professionals involved in bioenergy and land use change assessment. It provides both high‐level reviews and in‐depth analyses on multidisciplinary topics; therefore, it could be of interest to researchers and students from a wide variety of fields in energy, economics, and environment.

Finally, we would like to sincerely acknowledge the chapter authors for their valuable contributions and reviewers for their constructive criticisms. We also gratefully thank AGU and Wiley editorial staff, especially Rituparna Bose, Mary Grace Hammond, and Kathryn Corcoran for their excellent support and service.

Zhangcai Qin, Energy Systems Division, Argonne National Laboratory, Argonne, Illinois, USAUmakant Mishra, Environmental Science Division, Argonne National Laboratory, Argonne, Illinois, USAAstley Hastings, School of Biological Sciences, University of Aberdeen, Aberdeen, UK

Part IBioenergy and Land Use Change

1Bioenergy and Land Use Change: An Overview

Pankaj Lal1, Aditi Ranjan2, Bernabas Wolde1, Pralhad Burli1, and Renata Blumberg3

1 Department of Earth and Environmental Studies, Montclair State University, Montclair, New Jersey, USA

2 MYMA Solutions LLC, Lincoln Park, New Jersey, USA

3 Department of Nutrition and Food Studies, Montclair State University, Montclair, New Jersey, USA

Abstract

Bioenergy production can have direct and indirect land use impacts. These impacts have varied implications, ranging from land tenure, commodity production, urbanization, carbon sequestration, and energy independence to several others. In recognition of its broad and intricate impacts, a growing amount of research focuses on this area, hoping to address the controversies and inform the relevant policies in a way that ensures more sustainable outcomes. In this chapter, we provide a summary of the research around land use change economics and modeling. We examine various concerns, as well as their empirical evidences, and outline the conceptual opportunities and challenges involved in measuring both direct and indirect land use change. We also describe a number of modeling methods that have been used in previous studies, including spatially disaggregated modeling approaches, econometric land use change approaches, and integrated environmental economic approaches. The chapter concludes with an analysis of policy imperatives and suggestions that could form the foundation of a more sustainable bioenergy development pathway.

1.1. INTRODUCTION

The United States is the largest consumer of petroleum products in the world, consuming around 7.45 million barrels per day in 2012 [Energy Information Administration (EIA), 2014]. A significant share of these petroleum products is imported from politically unstable regions of the world. This reliance on fossil fuels has led to economic, social, and environmental concerns that have gained public attention. Bioenergy appears to offer hope by reducing the gap between domestic energy supply and demand, diversifying energy sources, reducing greenhouse gas (GHG) emissions, and by providing socioeconomic benefits in the form of additional income and new jobs.

Bioenergy encompasses energy produced from biomass and includes fuels such as sugarcane‐ or corn‐based ethanol, biofuels produced from energy grasses, farm residue, and woody materials, as well as energy obtained from other plant‐based sources. Agricultural and forested biomass‐based energy is considered an option to reduce dependency on fossil fuels, increase the current share of the nation’s renewable energy, and improve the sustainability of forests and marginal lands. Cellulosic biomass‐based energy or second‐generation fuels, for example, fuels produced from energy grasses or woody biomass, have certain advantages over other energy sources, such as first‐generation fuels like corn ethanol, because they limit competition between agricultural food crops and those destined for fuel production [Hill et al., 2006]. While the development of cellulosic biofuels could result in competition for use of resources, including water, labor, carbon storage, and financial resources, one of the most important issues surrounding bioenergy markets is land use impact [Searchinger and Heimlich, 2015]. There are varied answers to the following question: What is the land use impact of using biomass‐based energy, and how does it change over time? Arable land is a scarce resource that is already under pressure from food agriculture, forestry, industry, urban, and other demands. The situation is further complicated by the fact that the land use change (LUC) associated with increased bioenergy production can negate the potential benefits of production and ultimately degrade the environment. The bioenergy market is largely encouraged on the national/supranational scale by government initiatives; countries with notable biofuel initiatives include the member states of the European Union, the United States, and Brazil. Without taking into account both the economic and environmental factors that surround the bioenergy market, these initiatives can lead to poor land use decisions at the local level [Baker et al., 2010; de Oliveira Bordonal et al., 2015; Vasile et al., 2016].

Economic methods of biomass supply estimation are based on the interplay of demand and supply markets of bioenergy. The bioenergy market is shaped by the interplay between global oil fuel, other types for energy sources, price of substitutes, bioenergy production costs, and alternative uses for arable land. A high fuel price not only increases the demand for biofuels by increasing the incentive for the production of alternative sources but also increases the cost of cultivation or harvesting. Bioenergy production also affects potential profit margins for the sale of biofuels, and if costs of production are high, actual production will necessarily drop [Rajcaniova et al., 2014].

As fossil fuel prices rise, the production of bioenergy becomes more profitable and therefore leads to the conversion, or construction, of agricultural land for biofuel production. For example, Piroli and Ciaian [2012] showed that a one‐dollar increase in per‐barrel price of oil could encourage the planting of between 54,000 and 68,000 ha of bioenergy cropland globally. The same study found that increasing oil prices increased agricultural area globally by 35.5 million ha, out of which biofuel feedstocks were 12.12 million ha/year. Volatility in the oil market encourages the use of first‐generation biofuel crops, such as maize, wheat, and soybean, over second‐generation biofuels, like perennial grasses, because of the comparatively low turnaround time of first‐generation bioenergy crops. This variable demand for bioenergy, met by quick switches to energy crops over food, as prices fluctuate, can lead to not only fluctuations in supply for conversion plants but also unsustainable biomass cultivation and/or harvesting practices resulting in modified land use, deforestation, soil degradation, and GHG emissions, among others. Often the first lands to be developed are pasturelands and other croplands. However, other land uses are converted to biofuel production with an increasing demand for biofuels, including forest and swampland [Rajcaniova et al., 2014].

Uncontrolled bioenergy transition can result in increased agricultural runoff because of improper cultivation and harvest methods. This has been observed in the red river basin in the Dakotas. When the primary crops in the area changed to corn with increased biofuel demand, sediments increased by 2.6%, phosphorous by 14.1%, and nitrogen by 9.1% [Lin et al., 2015]. Second‐generation biofuels tend to increase soil organic carbon (SOC) when planted on cropland but tend to decrease SOC when planted on their native counterparts: forests and grasslands [Harris et al., 2015].

There is also apprehension that the conversion of land to bioenergy can lead to more arable land being opened up to raise supply. This increase in agricultural land has the potential of being environmentally deleterious, as more sensitive natural environments may be repurposed. Land use change to agricultural row systems can also cause habitat loss [Jonsell, 2007]. Land use change from natural forests to forest plantations, including short‐rotation woody crops, is an important area of concern from an ecological point of view [Wear et al., 2010]. Fargione et al. [2008] contend that the conversion of lands, such as rainforests, peatlands, and grasslands, to produce crop‐based biofuels in Brazil, Southeast Asia, and the United States could potentially release 17–420 times more CO2 than the annual GHG reductions from the use of these biofuels. Meanwhile, Plevin et al. [2010] estimate emissions associated with indirect land use change for U.S. corn ethanol ranging between 10 and 340 g CO2e MJ−1 for a variety of modeling scenarios and assumptions. Similarly, using a spatially explicit model to project land use changes, Lapola et al. [2010] suggest that indirect land use changes resulting from expansion of biofuel plantations in Brazil could create a carbon debt that would take about 250 years to be repaid. Biomass production might also have negative consequences unless coordinated with breeding and nesting seasons and maintaining cover for overwintering small mammal species [Bies, 2006]. However, interventions focused on ecological restoration or fuel‐reduction activities associated with woody biomass can also benefit wildlife habitat [Janowiak and Webster, 2010].

In the face of a growing bioenergy sector and associated policy incentives, land use analysis is considered critical for the future of bioenergy markets. Many authors have explored this issue; what has been lacking is a systematic analysis of trends, evidence, and complexity in assessing bioenergy market growth and associated land use impacts. Toward this goal, a comprehensive literature review was undertaken. We review the problems, applications of economic techniques, methodological complexities, and certification efforts from the literature, focusing more on the ways in which bioenergy and land use issues have been approached by economists. We focus less on the methodological aspects and rely more on comparative results and outcomes in order to provide the reader a broad understanding of current research in this area.

The rest of the chapter is organized as follows. In the following section, we discuss forest biomass supply assessments, focusing on some of their differences and similarities. We then turn to the modeling efforts and methodologies that have been used to estimate land use change impacts associated with bioenergy markets. In the fourth section, we focus on the empirical evidence of land use change analysis and challenges such as uncertainty and modeling challenges. We discuss technological and policy imperatives and bioenergy certification in the fifth section. Finally, we summarize observations and provide perspectives on the future of bioenergy markets and land use impacts.

1.2. LAND USE CHANGE AND CURRENT RESEARCH

Land use change is an important component of the use of biofuels, and it is important in terms of delineating effect of biofuels on GHG emissions and carbon sequestration by considering the lifetime GHG effects of the fuels as opposed to their effects when they are only burning. It is increasingly being recognized that land use effects of bioenergy production are linked to net GHG reductions. Assuming that energy crops do not lead to land use changes, life cycle analyses of different biofuels (including woody biomass) suggest overall GHG reductions [Birdsey et al., 2006; Blottnitz and Curran, 2007; Eriksson et al., 2007; Gustavsson et al., 2007]. However, Searchinger et al. [2008] argue that life cycle studies have failed to factor in indirect land use change effects and suggest that using U.S. croplands or forestlands for biofuels results in adverse land use effects elsewhere, thus harming the environment rather than helping it.

To this end, researchers and organizations, including the Intergovernmental Panel for Climate Change [Watson et al., 2000], the National Wildlife Federation, and the Union of Concerned Scientists have put considerable effort into defining and studying the effects of land use change in biofuels [Watson et al., 2000; Searchinger et al., 2008; Union of Concerned Scientists, 2008; Plevin et al., 2010; National Wildlife Federation, 2014].

1.2.1. Direct Land Use Change

Direct land use change (DLUC), the direct change of land usage due to increased biofuel production, has the most obvious and measurable effect on the land and surrounding areas. DLUC occurs most commonly when uncultivated areas, such as forests or grasslands, are converted into farmland for the production of biofuel crops. Direct land use change can have considerable consequences for GHG emissions and other environmental concerns [Union of Concerned Scientists, 2008; National Wildlife Federation, 2014]. Destruction of forest ecosystems, for example, causes a significant amount of carbon stored in the forests to be released, which can offset many of the carbon advantages of using biofuels [National Wildlife Federation, 2014]. In addition to this, DLUC can also impact the environmental benefits that these areas could provide, including biodiversity and ecosystem services such as water filtration, erosion control, and ground water recharge. DLUC, when considering these factors, can result in adverse ecosystem trade‐offs.

DLUC is an important consideration for any bioenergy project. The environmental and economic cost of clearing land, planting, and growing the bioenergy plants can also influence net GHG emissions. The emissions vary considerably, depending on the crop and the area in which they are planted, so bioenergy crops should be carefully considered for the potential plantation area before planting to ensure that emissions do not outweigh the benefit of the crop. Fueled by some of the above mentioned concerns, DLUC impacts arising because of biofuel production have come under scrutiny in recent years. Sustainability initiatives, both in the United States and abroad, have made it a priority to understand the consequences of DLUC [Stappen et al., 2011; Jones et al., 2013]. Because of the nature of land use change and the differing nature of bioenergy crops and cultivation practices, these result in different economic and environmental cost and benefits [Natural Resource Defense Council (NRDC), 2014].

Sugarcane, for example, is a bioenergy crop that has gained popularity over the years and is currently a staple in the United States and Brazil, two countries that together account for around 90% of the world’s ethanol production. In addition to potentially reducing GHG emissions by nearly 85% in comparison to fossil fuels, in areas of Brazil, converting land to sugarcane production can potentially lead to positive benefits, such as local climate cooling [de Oliveira Bordonal et al., 2015]. However, sugarcane cultivation practices for bioenergy production, particularly the burning of plant residues during harvest time, can contribute to GHG emissions. De Oliveira Bordonal et al. [2015] sought to quantify some of the effects of DLUC and provide an analysis of the effects of sugarcane production on GHG emission. They found that the expansion of sugarcane plantations contributed to significant GHG emissions from agricultural production, but around 57% of this was offset through carbon uptake in the new biomass. However, calculations of GHG emissions can differ depending on the system boundaries of the assessment and key drivers of the land use change, coupled with factors such as ecosystem services and carbon pools and sinks, among others.

It is also important to note that the GHG emissions from land use can differ wildly, depending on the location in which they are grown. A study by Bailis and McCarthy [2011] found that there was a considerable difference in the carbon debt between jatropha plantations in India and Brazil as a result of the differing climates and soil types. The study by Stappen et al. [2011] confirms this by finding a considerable difference in net GHG emissions between soy grown in the United States (27%) and Argentina (−568%). This has important implications as it indicates that there is no one‐size‐fits‐all bioenergy crop that has superior performance compared to all others in all places and at all times. As such, bioenergy should be considered for each area individually to fully understand how it will perform and how much work in the form of emissions and labor will be produced in their growth.

1.2.2. Indirect Land Use Change

Indirect land use change (ILUC), a secondary consideration in land use change for bioenergy crops, includes the effects that are related to, but not immediately caused by, the cultivation of bioenergy crops. Because it is difficult to define where ILUC begins and ends, it is far more difficult to calculate than DLUC. Though some studies attempt to approximate ILUC, it is perhaps more important to understand some of the dynamic factors that influence the proliferation of ILUC and get a more complete understanding as to how a bioenergy operation will affect the surrounding land. Understanding the possible extent of ILUC is critical for making management decisions that can accurately and successfully reduce carbon emissions while simultaneously protecting local ecosystems and ensuring that the operation does not produce an unhealthy amount of GHGs.

The ILUC impact of biofuel feedstocks was brought to the forefront during the “food versus fuel” debate. This common criticism of bioenergy refers to the conflict between using food crops for fuel instead of food [Naylor et al., 2007]. The price of crops, such as corn, that are used for both food and fuel increased in some instances because more of it was being used for fuel. This can often extend to land use change elsewhere, as displaced food‐growing operations in one part of the world may result in land being diverted for growing bioenergy crop elsewhere [Doornbosch and Steenblik, 2008]. Such a change may happen in geographically disconnected areas, as it is largely a result of market mechanisms, and therefore can encourage a significant change in land use that cannot be traced firmly back to any singular bioenergy operation. This proliferation of changing land use as a result of bioenergy operations can result in increased GHG emissions if carbon‐rich ecosystems are converted to farmlands [Fritsche et al., 2010]. What makes the problem “wicked” is the fact that ILUC is more difficult to quantify, and it is more difficult to identify the drivers resulting in adverse bioenergy‐based land use change.

Because ILUC is difficult to quantify, there is some confusion and even skepticism in the scientific community over its relevance. Finkbeiner [2014] posits that ILUC quantification methods are still in their infancy and that there exists no proven method to accurately convey how ILUC affects GHG emissions. He cites wildly varying estimates (from −200% to over 1700%) among studies that try to quantify the effects of this change, far more than other scientific studies of its type, and notes that there are no relevant standards for this type of study at this time. He notes that major international standards, such as the EU Product Environmental Footprint Methodology and ILCD Handbook, do not include ILUC in their calculations, bringing their relevance and reliability into question. He also points out that adding ILUC emissions into the emissions of major biofuels may be misleading and may result in a lopsided comparison with fossil fuels; ILUC can also occur with the production of fossil fuels, but they are never included in fossil fuel emission calculations. Moreover, methodologies including comprehensive life cycle assessment (LCA) and input‐output models can potentially address these challenges by accounting for direct and indirect land use change more precisely [Liang et al., 2012; Marvuglia et al., 2013; Dilekli and Duchin, 2016]. Though ILUC can represent an important piece of the land use change emissions, it is important to consider the concerns brought up by this study and others as this field of study matures. Only then can the indirect effects of biofuels be accurately represented. Though the study of its effects is imperfect, ILUC can be deemed important to understanding the complete effect of biofuel production on the environment.

1.2.3. Current Research Trends in Bioenergy and Land Use Literature

Though the production of biofuels has advanced considerably in recent years, it has also led to some concerns. This is evident from our word‐cloud analysis whereby we quantitatively analyzed large collections of textual information to evaluate some of the most widely cited publications of the previous decade. Text mining has become more sophisticated and has benefitted from computational and technological advances in linguistics, computer science, and statistics [Meyer et al., 2008]. We analyzed 46 papers published since January 2006 to August 2016, encompassing the broad theme of bioenergy and land use. These publications were cumulatively cited over 3000 times according to citation statistics compiled using Google Scholar. The analysis was performed using text‐mining features in the programming language R [Williams, 2016].

The word cloud provides a visual of the 50 most frequently used words in the papers analyzed in the text analysis and illustrates some of the important focus areas in previously published land use and bioenergy literature (Figure 1.1). Within the text‐mining algorithm, we exclude numbers, as well as commonly used conjunctions, prepositions, and “stopwords” such as “all,” “almost,” and “largely,” among others. It is interesting to note that along with the obvious focus on biomass, land, and bioenergy, the text analysis identifies “food,” “crops,” “agriculture,” “forest,” and “feedstock” as keywords with a relatively high frequency. This suggests that the food versus fuel argument and conversion of forestland for cultivating bioenergy feedstocks emerge as an important story line. Finally, “oil,” “gas,” “ethanol,” and “fuel” also feature in many publications. While it is plausible to infer that the frequency of words is correlated with the number of publications included in the analysis, enhanced computational and analytical capacities allow us to decipher important trends in the literature in relatively less amount of time.

Figure 1.1 Word cloud representing the 50 most frequent words in the text analysis.

1.3. MODELING EFFORTS AND METHODOLOGIES ESTIMATING LAND USE CHANGE AND BIOENERGY MARKETS

Several approaches, including land tenure, urbanization, energy production and consumption, climate change, economic growth, and population growth, have been used to model land use [Irwin and Geoghegan, 2001; Lambin et al., 2001]. These approaches are crucial in helping us understand, quantify, and predict likely social, economic, and environmental outcomes, all of which are valuable in informing relevant decisions and in minimizing potential adverse outcomes while maximizing the potential positive outcomes. The results are useful for making informed decisions regarding land use planning and policy.

In order to quantify land use changes, LCA is often used. LCA is a methodology that attempts to bring all the factors of a crop’s life cycle into the equation, from its conception to its disposal, to fully understand their effects. Though many different crops have different carbon‐emission savings when compared to fossil fuels and this number can be calculated [Stappen et al., 2011], large differences in the LCA of these crops generally come from differences in how direct and indirect land use changes are assessed [Marvuglia et al., 2013]. For example, generally more land use contributes to higher GHG emissions and therefore can cause a net GHG increase; studies that assess a larger area of land use, likely by including more land from indirect use, may appear to have higher emissions and therefore lower net savings [Finkbeiner, 2014]. Furthermore, different types of LCA may change the net balance; Marvuglia et al. [2013], for example, describe the difference between LCA, which aims to describe the impacts of the human economy on the global environment, and consequential LCA (CLCA), which aims to show how the environment will respond to possible decisions. All of these factors can ultimately lead to considerably different results, and thus the major factors, namely, land use change, must be assessed in order to understand how they change the models.

The way land use change is modeled depends partly on the drivers, including social, economic, technological, biophysical, political, and demographic, and the implications of land use change one aims to model. The subject matter, actual use, applicability, and type of information available to the model developer also affect the way one models land use change [Adams et al., 1999]. On the basis of the techniques adopted and end use, these approaches can be sorted into various groups, the operational classification for this study being (i) spatially disaggregated approach, (ii) economic approach, and (iii) integrated environmental economic approach.

Each of these approaches is best suited to handle a specific set of land use change drivers and relevant implications by addressing a given problem from different specialized points of view. Given their unique perspective, these approaches differ in their unit of analyses, land use of interest, intended user, contagion criteria, temporal and spatial considerations, and their ability to model emergent behavior [Agarwal et al., 2002]. Their respective scopes can be local, regional, national, or even international, and they can either project future trends or describe the processes that resulted in the change that has already occurred [Drummond and Loveland, 2010]. The given approaches and the specific models used can follow a probabilistic or deterministic transition rule for conversions among given land use classes [Agarwal et al., 2002].

While the specific perspective may differ, the models are not always mutually exclusive. For instance, the statistical models and transitional matrices are comparable, whereas hybrid models build on the strengths of one model and overcome inherent weaknesses [Briassoulis, 2000; Gibson et al., 2000]. Quality control of the results can be assessed through the realism in capturing relevant underlying processes, precision of results in describing data, and the generalizability or replicability of the results in other settings [Grimm et al., 2005].

The models also share some common constraints, including the uncertainty and limited availability of historical land use information, which is compounded while modeling feedback loops [Kim and Dale, 2009]. Other factors being the same, variations in input data and related assumptions may affect the estimated land use and cover change and associated impacts [Center for BioEnergy Sustainability (CBES), 2009]. In what follows, we describe each approach and give examples of specific cases where the said approaches are used.

1.3.1. Spatially Disaggregated Modeling Approaches

This approach explicitly accounts for the spatial heterogeneity of the area of study. It can also account for the various land use options available to a given land, allowing for the inclusion of relevant neighborhood conditions such as presence and proximity of developed sites, roads, and land features. This is important in assessing correlating land uses and changes, and it increases the chances of correctly predicting the amount, probability of conversion between different land use classes, and type and stability of land use change over time [Brown, 2002]. Moreover, current and historical land use patterns and the contagion specification help to validate model prediction and check the reliability of the results [Clarke et al., 1997; Pontius and Schneider, 2001]. The unit of analyses affects the data requirement, precision of results, and relevance of the results to varying stakeholders [Walsh et al., 2001].

In the context of bioenergy, regional differences exist in the biomass yield along with the corresponding biofuel yield, both positive and negative. Moreover, different feedstocks have varying agronomic conditions and input requirements [Varvel et al., 2008]. This approach can prove useful in accounting for such variations while modeling the extent and outcomes of land use change associated with feedstock production. However, the scale and quality of remote‐sensing data and other types of data have to be uniform, making it challenging to model land cover and land use change effects from biomass regrowth [Schulze, 2000]. The reliance on existing and historic land uses is also intractable for emerging land use developments, distant future projections magnifying the problem and showing the need for reasonable and flexible thresholds systems for given land uses [Agarwal et al., 2002].

The more complex spatial models, including spatially representative and spatially interactive models, can incorporate or produce data at up to three spatial dimensions. The area base model, for instance, predicts land use proportions among farmland, forests, and urban or other types of land uses [Hardie and Parks, 1997]. Using counties as units of analyses, it attempts to predict and explain the coexistence of several land uses and conversion among them by using their respective heterogeneous attributes.