NREL/TP-6A2-46209 

Posted with permission. 

Assessment of Biomass Resources from 

Marginal Lands in APEC Economies 

Energy Working Group 

August 2009
 
 

This report was prepared for the APEC Energy Working Group under EWG 11/2008A 

by: 

Anelia Milbrandt 

National Renewable Energy Laboratory (NREL) 

Golden, Colorado, USA 

Web site: www.nrel.gov 

Dr. Ralph P. Overend 

NREL Research Fellow (Retired) 

Ottawa, Ontario, Canada 

APEC#209-RE-01.4 
Acknowledgments 

The authors would like to acknowledge and thank the project overseer, Mr. Jeffrey Skeer 

(Department of Energy, USA and chair of the APEC Biofuels Task Force), for his support, 

review, and guidance throughout this project. We also thank Mr. Mark Stumborg from 

Agriculture and Agri-Food Canada for his review and recommendations. 

i 
Table of Contents 

ACKNOWLEDGMENTS ................................................................................................................ I 

TABLE OF CONTENTS................................................................................................................ II 

LIST OF FIGURES ...................................................................................................................... III 

LIST OF TABLES.........................................................................................................................III 

ABBREVIATIONS AND ACRONYMS......................................................................................... IV 

UNITS OF MEASURE ................................................................................................................ IV 

EXECUTIVE SUMMARY .............................................................................................................. 1 

INTRODUCTION .......................................................................................................................... 3 

MARGINAL LANDS IN APEC ECONOMIES................................................................................4 

BIOMASS POTENTIAL ON MARGINAL LANDS IN APEC ECONOMIES ...................................8 

CONCLUSIONS.......................................................................................................................... 20 

REFERENCES ........................................................................................................................... 23 

AUSTRALIA ................................................................................................................................ 27 

BRUNEI DARUSSALAM.............................................................................................................28 

CANADA ..................................................................................................................................... 29 

CHILE ......................................................................................................................................... 30 

CHINA......................................................................................................................................... 31 

INDONESIA ................................................................................................................................ 32 

JAPAN ........................................................................................................................................ 33 

REPUBLIC OF KOREA .............................................................................................................. 34 

MALAYSIA .................................................................................................................................. 35 

MEXICO...................................................................................................................................... 36 

NEW ZEALAND .......................................................................................................................... 37 

PAPUA NEW GUINEA................................................................................................................ 38 

PERU .......................................................................................................................................... 39 

PHILIPPINES.............................................................................................................................. 40 

RUSSIA....................................................................................................................................... 41 

CHINESE TAIPEI........................................................................................................................ 42 

THAILAND .................................................................................................................................. 43 

UNITED STATES........................................................................................................................ 44 

VIET NAM ................................................................................................................................... 45 

ii 
List of Figures 

Figure 1 Methodology for Assessing Available Marginal Lands in APEC Economies ..................5
 

Figure 2 Marginal Lands in APEC Economies..............................................................................6
 

Figure 3 General Outline of Crop Production Modeling ..............................................................12
 

Figure 4 Annual Net Primary Productivity on Marginal Lands in APEC Economies ...................16
 

Figure 5 Marginal Lands in Australia ..........................................................................................27
 

Figure 6 Marginal Lands in Brunei Darussalam..........................................................................28
 

Figure 7 Marginal Lands in Canada............................................................................................29
 

Figure 8 Marginal Lands in Chile ................................................................................................ 30
 

Figure 9 Marginal Lands in China ............................................................................................... 31
 

Figure 11 Marginal Lands in Indonesia.......................................................................................32
 

Figure 12 Marginal Lands in Japan.............................................................................................33
 

Figure 13 Marginal Lands in Republic of Korea..........................................................................34
 

Figure 14 Marginal Lands in Malaysia ........................................................................................35
 

Figure 15 Marginal Lands in Mexico ...........................................................................................36
 

Figure 16 Marginal Lands in New Zealand .................................................................................37
 

Figure 17 Marginal Lands in Papua New Guinea .......................................................................38
 

Figure 18 Marginal Lands in Peru............................................................................................... 39
 

Figure 19 Marginal Lands in the Philippines ...............................................................................40
 

Figure 20 Marginal Lands in Russia ...........................................................................................41
 

Figure 10 Marginal Lands in Chinese Taipei ..............................................................................42
 

Figure 21 Marginal Lands in Thailand.........................................................................................43
 

Figure 22 Marginal Lands in the United States...........................................................................44
 

Figure 23 Marginal Lands in Viet Nam........................................................................................45
 

List of Tables 

Table 1 Marginal Lands in APEC Economies Part 1 of 2 .............................................................7
 

Table 7 Ethanol Potential from Marginal Lands, Share of Current Gasoline Consumption and 

Table 8 Ethanol Potential from Existing Biomass Resources, Share of Current Gasoline 

Table 2 Marginal Lands in APEC Economies Part 2 of 2 .............................................................7
 

Table 3 Marginal Lands Productivity in APEC Economies Part 1 of 2........................................17
 

Table 4 Marginal Lands Productivity in APEC Economies Part 2 of 2........................................17
 

Table 5 Average Biofuels Potential on Marginal Lands in APEC Economies.............................18
 

Crude Oil Import in APEC Economies ........................................................................................25
 

Consumption and Crude Oil Import in APEC Economies ...........................................................26
 

iii 
Abbreviations and Acronyms 

APEC - Asia-Pacific Economic Cooperation 

CO2 – Carbon Dioxide 

ENSO - El Nino Southern Oscillation 

FAO - Food and Agriculture Organization 

GAEZ – Global Agro-Ecological Zones 

GHG – Greenhouse gases 

GIS – geographic information system 

IIASA - International Institute for Applied Systems Analysis 

NPP – Net Primary Productivity 

NREL - National Renewable Energy Laboratory (of the U.S. Department of Energy) 

PNG - Papua New Guinea 

RS – remote sensing 

UNEP – United Nations Environmental Programme 

USDA – United States Department of Agriculture 

Units of Measure 

g – gram 

Gt – Gigatonne (109

 tonne) 

ha – hectare 

hm3 – cubic hectometer or 1,000,000 m3

 or 109 L, described in some sources as a GL 

(Gigaliter) 

m2

 – square meter 

m3 – cubic meter 

MJ – Megajoule (106 joule) 

Mt – Megatonne (106 tonne) 

km2 – square kilometer 

Tonne (t) - metric ton 

iv 
Executive Summary 

Marginal lands have received an increased attention by the bioenergy industry as an alternative 

to cropland for feedstock supply that could help to address the food vs. fuel debate challenging 

the industry’s further development. Literature suggests that Asia-Pacific Economic Cooperation 

(APEC) economies have vast marginal lands available, which may hold potential for the 

production of biofuels feedstock. However, the term “marginal land” has been used quite loosely 

without a concrete definition. Moreover, in many economies, the extent and characteristics of 

these lands have not been systematically assessed, nor has their suitability for biofuels crop 

production been evaluated. 

The goal of this study is to examine the marginal lands in APEC economies and evaluate their 

biomass productivity potential. Twelve categories of marginal lands are identified using the 

Global Agro-Ecological Zones system of the United Nations Food and Agriculture Organization. 

Numerous exclusions are applied using state-of-the-art geographic information systems (GIS) to 

estimate the available marginal lands in APEC economies. The analysis estimates that about 4 

million km2

 (400 million ha) of marginal lands are available in the APEC region, representing 

6.5% of the total land area. Economies with the largest marginal lands include Australia (13.5% 

of total land area); Canada (4%); China (5%); Russia (2%); and the United States (13%). 

The study’s biomass production assessment methodology takes into account a combination of 

the current vegetation cover and a calculation of the net primary productivity (NPP). The NPP 

incorporates a fixed middle range of radiation use efficiency and adjusts for air temperature. It is 

derived from extensive data sets and translated into the annual yield of a lignocellulosic 

biomass, typical of grasses such as switchgrass and other thin-stemmed perennials or thickstemmed grasses in tropical areas. Other lignocellulosics that would be similar are short-rotation 

tree species with willow and poplar as examples in temperate regions and eucalyptus in more 

tropical environments. These crop types meet the criteria of non-edible crops and will give the 

highest biomass potential. 

This study estimates the total annual biomass resource potential on marginal lands in APEC 

economies to be about 1.3 Gt, which converts roughly into 540 hm3

 of ethanol fuel or 260 Mt of 

gasoline equivalent. By comparison, APEC uses about 621 Mt of gasoline and crude oil import 

1 
is about 1.3 Gt annually (IEA 2006). So the ethanol potential from marginal lands could displace 

two-fifths of the region’s gasoline consumption and one-fifth of its crude oil imports. 

Many economies could substitute a substantial volume of their current gasoline consumption 

and crude oil imports with ethanol from marginal lands, as illustrated in the Appendix. These 

include Australia; Chile; New Zealand; Peru; and Viet Nam. But there could be technical or 

economic restrictions on the use of these lands, which suggests that only a portion of this 

potential can be realized. This portion would be different in each economy and could be 

determined by a detailed techno-economic feasibility assessment, which is beyond the scope of 

this study. 

Previous work by the authors (Milbrandt, A., Overend, R. 2008) evaluated the ethanol potential 

from existing lignocellulosic biomass (primarily crop and forest residues) in the APEC region. 

That study estimated a similar volume of ethanol potential, 509 hm3

 of ethanol fuel or 245 Mt of 

gasoline equivalent, enough to displace two-fifths of the region’s gasoline consumption and onefifth of its crude oil import. Comparing the results from both studies at an economy level, it 

appears that some economies (such as China; Indonesia; Japan; Korea; Malaysia; the 

Philippines; Thailand; and Viet Nam) have large biomass potential from farm and forest 

residues, but limited potential from marginal lands (Appendix Tables 7 and 8). Other economies 

(Australia; Canada; Chile; Mexico; New Zealand; Russia; and United States) have major 

production potential on marginal lands as well as from residues. A combination of feedstock— 

lignocellulosic biomass from residues and biomass grown on marginal lands—would assure a 

sustainable resource supply for biofuels production in these economies. Such a supply could 

substantially displace current import and consumption of petroleum products. 

The present study evaluates only the ethanol potential on marginal lands. Biodiesel could 

present very good opportunities for some of these lands in tropical regions, so future work 

should evaluate the biodiesel potential on marginal lands in APEC economies and compare it 

with the results from the previous study. More detailed assessments at a sub-regional or an 

economy level, and a finer than 100 km2

 spatial resolution, would also provide a better 

understanding of the biofuels potential on marginal lands in the APEC region. Given that the 

interest in biofuels is in large part driven by greenhouse gas mitigation policies, it would seem 

reasonable that future work be linked to climate modeling, which is now starting to have the 

ability to predict regional changes. 

2 
Introduction 

Land is a precious commodity and more so today in the light of dynamic land use and land 

cover change driven by economic, environmental, social, and political factors. Availability of land 

to supply the emerging biofuels industry with enough biomass is a challenge shared by all 

APEC economies. In addition, the use of conventional crops for biofuels production has recently 

been of concern because they can compete with food materials. These considerations have 

encouraged the APEC economies to search for an alternative approach to biofuels feedstock 

supply—growing non-edible crops on marginal lands. Using these lands to grow energy crops, 

even though the lands are less productive, can provide some additional environmental and 

social benefits, including restoration of degraded land, carbon sequestration, and job creation. 

Literature suggests that APEC economies have vast marginal lands available, which may hold 

potential for the production of biofuels feedstock. However, the term “marginal land” has been 

used quite loosely without a concrete definition. Moreover, in many economies, the extent and 

characteristics of marginal lands have not been systematically assessed, and their suitability for 

biofuels crop production has not been evaluated. 

The goal of this study is to assess the biomass resource potential on marginal lands in the 

APEC region. To achieve this goal, the following two project objectives were defined: 

1. Identify available marginal lands in the APEC economies and 

2. Evaluate their biomass production potential. 

To accomplish the first objective, the project utilizes state of the art geographic information 

systems (GIS) technology—a computer-based information technology used to create, 

manipulate, analyze, and visualize geographic information. To accomplish the second objective, 

the study uses a crop production model that considers various inputs to simulate plant 

development. 

This study intends to provide policy makers and industry developers with a general 

understanding of the biofuels potential on marginal lands in the APEC economies. It also aims 

to outline steps for further, more detailed analysis to guide their future strategic decisions. 

3 
 

Marginal Lands in APEC Economies 

Definition of Marginal Lands 

Marginal lands are characterized by poor climate, poor physical characteristics, or difficult 

cultivation. They include areas with limited rainfall, extreme temperatures, low quality soil, steep 

terrain, or other problems for agriculture. Examples include deserts, high mountains, land 

affected by salinity, waterlogged or marshy land, barren rocky areas, and glacial areas. 

Evidently, not all of these areas are suitable for agriculture. The authors evaluated various 

marginal land categories and identified the following as suitable for this study: 

•	 Bare and herbaceous areas (not in use or with only moderately intensive pastoralism). 

Lands with intensive and extensive pastoralism are not included. 

•	 Lands with moderate (8-16%) and steep (16-30%) slope. 

•	 Lands with soil problems: 

- Shallow soils (depth < 50 cm) 

- Poorly and imperfectly drained soils 

- Soils with low to moderate natural fertility 

- Coarse textured or sandy soils (Arenosols, Regosols, and Vitric Andosols with 

coarse texture, and all soils with petric and stony phase) 

- Soils with heavy cracking clays (Vertisols and vertic sub-groups) 

- Salt-affected soils (Solonchaks, Solonetz, and Solodic Planosols) 

- Soils with gypsic horizon1

 (Gypsic Xerosols and Gypsic Yermosols) 

- Acid soils [pH is strongly (5.5 - 4.5) to extremely acid (<4.5)] 

- Soils with high calcium level or Calcisols 

- Peat soils (Histosols). 

Data Information 

Most of the marginal lands data for this study were obtained in a geospatial format from the 

Global Agro-Ecological Zones (GAEZ) system. The GAEZ program was developed by the Food 

and Agriculture Organization of the United Nations (FAO) and the International Institute for 

Applied Systems Analysis (IIASA). GAEZ evaluates climatic parameters, topography, soil, and 

land cover to estimate crop suitability and land productivity potentials for each 5-minutelatitude/longitude grid-cell (roughly each 10km x 10km or 100km2

 of land area) for the whole 

1 Gypsic soils are soils that contain sufficient quantities of gypsum (calcium sulphate). 

4 
world. This study uses soil constraints, climatic constraints, and topography datasets from 

GAEZ. Land use and dominant soils data (with the same spatial resolution of 100 km2

), as well 

as railroads and wetlands data, came from FAO’s GeoNetwork, a geospatial data 

clearinghouse. Several datasets, such as Calcisols and land stress data, were provided by the 

United States Department of Agriculture. Data on protected areas were extracted from the 

World Database on Protected Areas, a joint product of the United Nations Environmental 

Program and the International Union for Conservation of Nature. Some reference data layers 

(administrative boundaries, cities, etc.) were obtained from the Environmental Systems 

Research Institute, while others (roads) were gathered from the Digital Chart of the World 1991­

1992. 

Analytic Methodology 

To assess the available marginal lands in the APEC region, land use/cover and environmental 

exclusions have to be applied. These are areas where no conventional agriculture for food 

crops is recommended or possible. GIS technology is particularly useful for this type of analysis, 

considering the various tools it offers to support it. These tools include spatial overlay, 

intersection of data layers, and elimination of certain features. The marginal lands data were 

incorporated into the GIS system for spatial manipulation and analysis. The methodology for 

assessing the available marginal lands in APEC economies, using GIS, is illustrated in Figure 1. 

Figure 1 Methodology for Assessing Available Marginal Lands in APEC Economies 

5 
The results of this analysis are presented in both graphic and tabular format (Figure 2 and Table 

1-2). The study estimates the available marginal lands in APEC economies at about 4 million 

km2

 or 6.5% of total land area. Economies with the largest total share of marginal lands include 

United States (13% of total land area); Australia (13.5%); China (5%); Canada (4%); and Russia 

(2%). Hong Kong and Singapore economies were excluded from this study due to their 

predominantly urban landscape, which would not be able to support crop development. 

Figure 2 Marginal Lands in APEC Economies 

6 
Table 1 Marginal Lands in APEC Economies Part 1 of 2 

Table 2 Marginal Lands in APEC Economies Part 2 of 2 

Select land use categories include bare and herbaceous areas (not in use or with only moderately intensive pastoralism). It doesn’t 

include lands with intensive and extensive pastoralism. Total marginal lands don’t correspond to the sum of all categories due to 

geographic overlap among land types. 

7 
 

Biomass Potential on Marginal Lands in APEC Economies 

The prediction of crop yields in farm systems and of the net rate of carbon fixation by the 

biosphere has become very important as humanity attempts to balance the needs of society 

against the maintenance of the ecosystems that support life on the planet. Using the benefits of 

crop growth simulation and GIS techniques, this study estimates the potential yield of energy 

crops on marginal lands that are either abandoned or not used in mainstream agriculture, due to 

either economic or environmental challenges. 

Crop yields have been estimated for the last 50 years, though significant advances did not really 

appear until the widespread availability of satellite sensor data after the 1970s. The initial 

estimation methods grew out of the development of crop growth models, which attempted to 

relate plant physiology to the seasonal growing conditions and the optimization of the growth 

parameters. Major contributions to this field were made at the University of Wageningen in the 

Netherlands. There, in the 1970s, Professor C.T. de Wit established academic courses 

addressing both simulation techniques and the mechanisms of crop physiology (Goudriaan, J. 

and H. H. van Laar 1994). 

At the time, simulation techniques were in their infancy, but crop physiology had by then 

established the major features of the control of plant yield. In order of importance, these are (1) 

the energy input determined by both the supply of solar radiation and the radiation use 

efficiency (RUE), (2) water availability, measured by water vapor pressure which is a product of 

the physics of water transport in the plant and the nature of the stomatal openings2

 in the leaf 

that let CO2 in, and (3) nutrient availability, especially nitrogen, which is needed to produce the 

nucleic acids, DNA and protein of the plant and its seeds. 

The RUE is typically 1 – 2 g of dry matter production per MJ of light intercepted. If the plant has 

an extensive area of leaf coverage, i.e. a Leaf Area Index (LAI) of 4 m2

 of leaf surface per m2 of 

land area, the daily dry matter accumulation at 12 MJ/m2

 irradiation will be about 20 g/m2/day. 

Over a 100 day growing season at this rate, the harvestable above-ground biomass will be 20 

t/ha (20 g/m2

/day x 100 x 104 m2/ha x 10-6 t/g). This assumes that there is no water deficit or 

2 In botany, a stoma is a pore, found in the leaf and stem epidermis that is used for gas exchange. 

8 
nutrient limitation. At around 500+ mm of water per year evenly distributed in the growing 

season, this is usually the case. 

In parallel with the work on crop growth simulation and with fundamental studies in 

photosynthesis, the International Geophysical Year of 1965 saw the first integrated perspectives 

on how the entire biosphere interacts with the atmosphere and indirectly with the oceans 

through the carbon dioxide (CO2) cycle. This work also became more important as there was 

growing awareness of how changes in the greenhouse gases (GHG) levels in the atmosphere 

were likely to alter the climate. The climate work of course is focused on the two most important 

GHG (CO2 and water vapor) and their interaction with the biosphere with respect to the net flow 

of carbon from the atmosphere to the plant biomass and the stability of these processes with 

respect to maintaining the atmosphere in equilibrium. Climate modeling increased the 

investment in this area significantly and the high priority of the topic led to the addition of much 

more detailed mapping and the application of GIS to interpret the data. 

The energy crops of the future are not yet known. There is a general concern that the use of 

food crops could conflict with the supply of food and animal feed for the growing human 

population. It is also recognized that crops for energy may have to meet more stringent 

sustainability criteria than existing food and feed crops. Obviously both the food and the energy 

crop systems have to be sustainable. However, there is already a burden of proof in some 

jurisdictions that energy crop systems should be capable of offsetting fossil carbon emissions by 

more than half. Criteria for sustainable carbon offset are being developed by the U.S. 

Environmental Protection Agency and required by Directive 2009/28/EC of the European 

Parliament. 

Carbon displacement and net energy efficiency are measured using the tools of life cycle 

assessment (LCA) in what are often described as well-to-wheel (WTW) analyses. Because 

energy crops are used for heat, combined heat and power (CHP), electricity, and synthetic 

natural gas (SNG), as well as liquid fuels for transportation, this project uses the results from full 

LCA, rather than just WTW analysis, to describe the assessment of the energy crop cycles in 

determining the optimum crop and product strategy. 

LCA has identified several important criteria for an energy crop. Perhaps the most basic 

criterion is that the energy yield has to be high (i.e. the solar energy captured in the biomass 

9 
 

has to be maximized). Obviously the harvested energy crop obviously has to have 

characteristics that are suitable for the envisioned end use. For thermal energy applications, the 

maximum yield criterion suggests that lignocellulosics such as wood and herbaceous species 

would give the highest yield, as these are closest in chemical composition to the photosynthate 

produced in the leaf and thus minimize transformation losses inside the growing grass or tree 

species. For liquid transportation biofuels, volumetric energy density is important. Such fuels as 

ethanol and biodiesel are more reduced than the simple photosynthate, and there is competition 

between plants that naturally produce hydrocarbons such as waxes, lipids, or triacylglycerides 

(TAG) and the use of industrial processes in chemical plants or biorefineries to convert 

lignocellulosics, starches, and sugars into the final product. LCAs also suggest that the plants 

should be perennials to avoid the energy and material costs of an annual cycle of tillage, 

planting (of seeds or cuttings), tending, and harvesting. Likewise, the issue of tillage and the 

loss of soil carbon is an important criterion in crop evaluation. 

Another important criterion is the issue of land use change (LUC), which has two facets that are 

called direct and indirect. Direct LUC is the change in status of the area that was previously in 

either a crop or abandoned land, to an energy crop. The other concern is of indirect LUC as it is 

argued that increasing land in energy crops will require that food cultivation will have to be 

increased elsewhere, either by converting other land into agricultural (food) production, 

intensifying existing land use for food, or reducing human food consumption. The measurement 

of indirect LUC is complex and is being strongly debated. At a minimum understanding, this 

issue will require complex economic models of world food production linked to the energy 

system. 

Land clearing and preparation for planting, as well as tillage, will cause carbon fixed in the soil 

to be released, possibly creating a deficit in carbon balance that will need to be repaid by the 

fossil energy displacement of the energy crop in order for biofuels production to be 

environmentally sustainable. This deficit can be just one year or less for the cultivation of 

marginal lands, as well as for most cropland under cultivation and for many types of grassland. 

But it is substantially greater, typically in the order of a few decades, when woody savanna or 

secondary or degraded forest is involved, and it can range up to several centuries for most 

tropical forest or even several millennia for tropical peat forest. Hence, the development of 

energy crops on marginal lands, provided that they are reasonably productive, has a major 

10 
advantage over development of these crops on croplands from the standpoint of carbon impacts 

of direct land use change. 

A number of candidate energy crops are being tested around the world. Many are lignocellulosic 

crops including tree species such as poplar, willow, and eucalyptus and herbaceous crops such 

as switchgrass and Miscanthus. Oil bearing shrubs and trees such as Jatropha Curcas and 

Pongamia are examples of in planta biochemical conversions to biofuel precursors as their 

seeds provide TAG for processing into diesel fuels such as fatty acid methyl esters or 

renewable diesel. 

Because of the diversity of potential energy crops and their evolving sustainability criteria, we 

have chosen to provide an estimate of the productivity of potential bioenergy crop lands in terms 

of the annual Net Primary Productivity or NPP. Readers can then choose their own crop and 

factor in its allocation of NPP carbon into the energy crop product that they need— be it 

branches and above ground stem for lignocellulosic or the TAG yield for an oil crop. The annual 

NPP values are a valuable indicator of the optimum locations within the available land classes 

for further investigation. 

In order to explain the process used, we will first discuss crop modeling as this is the foundation 

of estimating the NPP. We will then explain how we have brought into the NPP assessment the 

GIS information on actual vegetation cover status to more accurately reflect the actual site 

potential. 

Plant Growth Modeling 

The seminal work of the University of Wageningen has been widely adopted around the world in 

crop production modeling, and a relatively simple model SUCROS is described below that 

elucidates how crop modeling proceeds. The general outline of SUCROS-like models is 

illustrated in Figure 3. Flows of energy and material (solid lines with arrows) as well as control 

signals (dotted) are illustrated. 

11 
Figure 3 General Outline of Crop Production Modeling 

The key physiological inputs required for the simulation of crop growth, such as Leaf Area 

Index, are available for many crop species. The crop growth pattern is called the phenology of 

the plant and identifies the different stages of growth pattern with energy and time input over the 

growing season. Time and energy are interlinked and the phenology in the field can be tracked 

against daily heat units, a crude measure of the average daily temperature. For example, to 

produce a mature winter wheat variety, about 2,200 daily heat units are required. This means 

that it would take 147 calendar days to produce a winter wheat crop if the average daily 

temperature was a constant 15°C. In the early stages, the plant develops leaves and more 

stems (tillers). As the leaves develop, more energy is intercepted and the wheat moves through 

to so-called boot development, which leads to flowering and then the filling of the grain. 

The other part of SUCROS is the incorporation of actual weather data for the growing site. This 

determines the energy input as solar energy on an hourly and daily basis which is then used to 

calculate the assimilation of CO2. The assimilated CO2 is then passed as photosynthate to 

carbohydrate production (photosynthate = CH2O, the monomer of carbohydrates), which in turn 

passes into a module for dry matter partitioning between the various plant components. Part of 

the photosynthate is consumed in “powering” the growth and transport processes. This is called 

12 
 

maintenance respiration. This factor is very temperature-dependent and accounts for the poor 

performance of plant species at high temperatures. 

The descendents of SUCROS for the estimation of crop yields then require extensive weather 

data records for the solar input, temperature, and rainfall. They also require knowledge of the 

soil properties, particularly with respect to water-holding properties. And, as described above, 

they require a detailed phenological3

 development of the chosen species/cultivar. Such process 

models have been extensively developed for U.S. agriculture. These models are currently in 

their third decade. CENTURY (and its sibling DayCent, a daily version) originated at the 

Colorado State University’s Natural Resources Ecology Laboratory in the 1980s with National 

Science Foundation funding and U.S. Department of Agriculture support. Many versions later, 

William J. Parton continues to lead its development into the biogeochemical behavior of carbon, 

nitrogen, phosphorus and sulphur in the arable crop environment, as well as grasslands, 

savanna, and forests. The European model WOFOST is a direct descendent of SUCROS and 

has been further developed by the Joint Research Centre of the European Commission to 

predict current annual crop yields and compare them with historical data to determine trends 

related to climate change. Information on these and many more models can be found on the 

Register of Ecological Models maintained by Ecobas.org. 

As the need for modeling has moved away from crops about which detailed phenology is 

available and into regions of the world without detailed time series weather data, the general 

principles described above have been adapted. In some instances, the need to grow known 

crops in new locations has been met by means of synthetic climate generators. Given some 

inputs and possibly GIS data obtained by Satellite, stochastic time series can be generated for a 

given location for input into the models. Likewise, the all important solar input can now be 

calculated at any point on the surface of the globe using tools developed by the U.S. National 

Aeronautics and Space Administration and the U.S. Department of Energy’s National 

Renewable Energy Laboratory. 

We have chosen to use data generated by a combined model by VITO (Flemish Institute for 

Technological Research). This model is primarily based on radiation with satellite sensor data 

for the fraction of the photosynthetically active radiation (PAR) absorbed at a given location. The 

3 Phenology is the scientific study of periodic biological phenomena, such as flowering, breeding, and migration, in relation to 

climatic conditions. 

13 
model is used with SPOT satellite images and National Oceanic and Atmospheric 

Administration data at a 1 km2

 pixel resolution to provide 10 day average values of NPP starting 

on the 1st, 10th, and 21st day of each month. To generate the NPP for an entire year, the 36 sets 

of data are combined to give an annual value. The process used calculates three values: 

Gross Primary Productivity: GPPd = fAPARd . ε . CO2fert . pa (Td). c .Sgd (1) 

Net Primary Productivity: NPPd = GPPd. (1 - Ad) (2) 

Net Ecosystem Productivity: NEPd = NPPd - Rd = NPPd - ky . ph (Td) (3) 

In Equation 1, the daily flux of carbon captured by photosynthesis in g/m2

/day is based on the 

Montief method, developed by Phillippe Montief in the late 1880s. Sgd is the daily incoming 

global solar radiation (MJ/m2

/day) calculated from a solar irradiance model. This is multiplied by 

“c,” a factor of 0.48 which is the fixed proportion of PAR between 400 – 700 nm. GPP is also 

dependent on the air temperature (average daily value Td), a non-linear effect captured by the 

term pa (Td). The CO2 fertilization factor, CO2fert, computes the effect of the increasing CO2 

levels on the performance of the enzyme Rubisco which captures the CO2 in the plant leaf. 

fAPARd is the fraction of PAR captured in the canopy, which is based on the relation between 

this and the Normalized Difference Vegetation Index (NDVI) derived from the satellite based 

remote sensing. The term ε is the previously described radiation use efficiency (RUE) which is 

fixed in this model as 1.1 g of carbon mass in biomass per MJ of insolation. 

Equation 2 calculates NPP by adjusting for the respiration that takes place in the plant and 

returns carbon to the atmosphere. This is portrayed in the model by a linear dependence on the 

air temperature (Veroustraete F. and Sabbe H. 2000). 

We do not use the third equation in this study, as it concerns the annual carbon flux and 

calculates the return of carbon from the soil at the end of the growing cycle. Clearly this is 

important in the calculation of the carbon flux from the atmosphere to the ecosystems of the 

world. In our case, however, the key carbon flux is the harvest of biomass for use as either 

bioenergy or as feedstock for the production of biofuels. 

NPP as carbon uptake on an annual basis was measured for the growth year 1999-2000 at a 

spatial resolution of 100km2

 grid-cell using GeoSuccess data (VITO 2009). Data for the Northern 

Hemisphere were for the calendar year 1999, while those for the Southern Hemisphere were 

14 
 

 

taken from June 1999 through May 2000. Ideally, the average of several years should be taken 

into account, but for this project it was only possible to take this single annual snapshot. 

The marginal lands’ productivity in APEC economies is illustrated in Figure 4. The range of NPP 

values run from zero (no carbon capture) to values greater than 20 t/ha/yr. As can be seen, the 

marginal lands in APEC economies have low productivity. The predominant NPP value is less 

than 6 t/ha/yr. The values are indicative in that they are a single value shown at a 100 km2 gridcell for the terrestrial carbon flux as NPP. An additional uncertainty is that the data are 

calculated for a RUE of 1.1 g/MJ. Plant breeding has improved this value to almost 2 g/MJ in 

crops such as sugarcane growing under high-input irrigated conditions, but such conditions 

might not be typical on marginal lands (Sinclair, T.R., Muchow, R.C.1999). 

The results are presented in terms of the mass of carbon fixed by the plants. However, biomass 

can also be estimated in terms of harvestable above-ground lignocellulosics. This is done by 

multiplying the carbon value by 1.25—going from carbon to lignocellulosic mass—and assuming 

that one third of the biomass is below ground and non recoverable4

. Tables 3 and 4 present the 

biomass resource potential on marginal lands in APEC economies, as well as the associated 

biofuels potential. The NPP values are aggregated to country level using standard GIS 

techniques, considering the marginal land hectares in each 100 km2

 cell and the NPP per hectare 

associated with individual cells. Economies with the highest biomass resource potential include 

Australia; United States; Russia; China; and Canada due to their large marginal land areas. But 

such economies vary significantly in terms of their biomass yield per unit of area and thus in the 

portion of the potential that can be practically obtained. 

NPP measured as carbon includes both above- and below-ground biomass. We assumed from the literature that the aboveground is 2/3 of the total carbon fixed. The chemical formula of lignocellulosic biomass, averaged across many species, is 

somewhere around CH1.4 O0.56. In other words, 1.4 hydrogen atoms (with a mass of 1) and 0.56 oxygen atoms (with a mass of 16) 

for each atom of carbon (with a mass of 12). Therefore, the total molecular weight of a typical lignocellulosic feedstock, relative to an 

atom of carbon, is (1.4x1 + 0.56x16 + 1x12) / 12 or about 22.5 / 12 or 1.88. Multiplying this ratio of lignocellulose to carbon, 1.88, by 

the portion of carbon above ground, 2/3, we find that the ratio of lignocellulose to total carbon is 1.88 x 2/3 = 1.25 

15 

4 
Figure 4 Annual Net Primary Productivity on Marginal Lands in APEC Economies 

IIASA, in their studies of the European land potential, have classified area yields of biomass as 

follows: Very Suitable (VS) 13 – 17 t/ha; Suitable (S) 10 – 13 t/ha; Moderately Suitable (MS) 7 – 

10 t/ha; marginally suitable (ms) 3-5 t/ha; and Not Suitable (NS) less than 2 t/ha. The 

classification incorporates economic principles that relate to harvesting effort and the necessary 

radius of collection, as the harvested areas increase rapidly with decreasing productivity and 

suitability classification. The gold standard, which energy crops such as switchgrass or tree 

species are hoped to reach, has already been demonstrated in trials with yields of 23 – 27 t/ha of 

lignocellulosic biomass on good land with adequate rainfall and a long growing season (BRDB 

2008). 

16 
Table 3 Marginal Lands Productivity in APEC Economies Part 1 of 2 

Table 4 Marginal Lands Productivity in APEC Economies Part 2 of 2 

NPP – Net Primary Productivity (carbon); conversion of NPP to above ground biomass that can be harvested: multiply by 1.25; biomass conversion to volume: assumed t

efficiency to an ethanol liquid fuel with an energy density of 27 MJ/kg and a physical density of 789 kg m3 

17 
Taking the average productivity across the entire individual APEC economy, while ignoring 

possible hot spots of better potential, and applying IIASA’s classification of land suitability 

described above, Table 5 illustrates that marginal lands are indeed marginal with respect to 

biofuels feedstock potential. 

Table 5 Average Biofuels Potential on Marginal Lands in APEC Economies

 Note: Biofuels refer to ethanol fuel; columns two and three are derived from Table 4 to obtain biofuels 

density; * 0.39 m3 per tonne biomass; NS – not suitable; ms – marginally suitable; MS – moderately suitable; 

S – suitable. 

Of the nineteen economies analyzed here, only four (New Zealand; Peru; Chinese Taipei; and 

Thailand) have Suitable or Moderately Suitable yields on average. These economies generally 

have better soils, warm tropical climate, and adequate rainfall on their marginal lands. Twelve 

economies are on average marginally suitable and three are unsuitable for biofuels feedstock 

production, though this overview could conceal some portions of these economies with a higher 

potential. 

18 
The preponderance of the large area economies (six economies with more than 250,000 km2

 of 

marginal lands) in the lowest classes of suitability demonstrates that while they account for 80% 

of the estimated total ethanol potential on such lands, the economic costs will be high. Note also 

that this study did not attempt to estimate the existing above ground biomass or the carbon 

currently in the soil. As a result, it is not certain that such low productivity lands would 

necessarily pay back their carbon debt if converted to biomass for biofuel production. This 

situation was evaluated using the GorCam model for forest lands and clearly demonstrated that 

there is a productivity threshold to having a positive carbon balance over a reasonable number 

of years (Schlamadinger and Marland 1996). 

Our conclusions are somewhat similar to those of Campbell J.E., et al. (2008), who found that 

the highest classifications are with tropical abandoned grass lands ranging from Moderately 

Suitable to Very Suitable. But these are hot spots and the global weighted area average of all of 

the abandoned agricultural lands was found to be marginally suitable for above ground biomass. 

19 
Conclusions 

Marginal lands present severe constraints to plant mass production. Non-optimum conditions of 

pH, poor soils, inadequate or excessive water availability, and steep terrain all present major 

challenges to sustainable biomass production without major investments. 

Amelioration of these lands is possible through investment in irrigation and conservation 

schemes, but is often very expensive. Examples abound of irrigation projects in areas of 

dryness such as the Red Flag (Hongi) canal in the Taihang Mountains of China or the extensive 

schemes of the Murray-Darling Basin of Australia. Similarly, the management of both slope and 

water in the ancient rice terraces of the Ifuago in the cordillera of northern Luzon in the 

Philippines is an example of overcoming limitations in a sustainable way. Many of the other 

APEC economies have examples of such amelioration. 

The goal of this study was to examine the marginal lands in APEC economies and evaluate 

their biomass productivity potential. The marginal lands were identified using the GAEZ system 

of the FAO. The study’s biomass production assessment methodology was to take the defined 

areas and to look at their status today. It used a combination of assessment of the current 

vegetation cover and a calculation of the net primary productivity. This is an assessment of the 

potential of a given land parcel on an “as-is” basis, without any attempt to estimate the potential 

under conditions that would prevail if improvements such as irrigation were undertaken. A note 

of caution is that it is based on a snapshot of a particular year: the northern hemisphere growing 

season of 2000 and the southern hemisphere season of 2000 -2001. This growth year window 

was a La Nina period of the El Nino Southern Oscillation (ENSO) in the Pacific region, a period 

of relatively high rainfall in Australia prior to the extended drought that started in 2002-3 and has 

continued to the present. On the other hand, the assumptions about the rate at which plants 

convert sunlight to biomass are quite conservative in view of agricultural advancements. 

Many of the marginal lands in APEC economies are in regions that are relatively dry, as 

illustrated at a glance in Figure 2. These include regions with rain deficit adjacent to the major 

mountain chains of North and South America, Asia and Australia. Data for the tropical regions 

such as the Asian Islands are unlikely to be as affected as much by the time window chosen, 

though even so the ENSO still has some effects. 

20 
The net primary productivity, in tonnes of carbon, was derived from extensive data sets and 

translated into annual yield of lignocellulosic biomass, typical of grasses such as switchgrass 

and other thin-stemmed perennials, or thick-stemmed grasses in tropical areas. Other 

lignocellulosics that would be similar are short-rotation tree species with willow and poplar as 

examples in temperate regions and eucalyptus in more tropical environments. These crop types 

meet the criteria of non-edible crops and will give the highest biomass potential. 

The biomass yield was translated into suitability bands, calculated for each economy on 

average and not for each 100 km2

 cell separately. Very Suitable (VS) and Suitable (S) lands 

with yields greater than 10 t/ha/yr can be considered economically viable as the areas needed 

are compact and the harvesting effort is minimized. Moderately suitable (MS) lands of 7-10 

t/ha/yr may also have significant economic potential. As biomass productivity declines, the 

areas for a given output increase; harvesting effort not only increases, but the efficiency goes 

down, more equipment is needed and the logistics of biomass delivery to the biofuel processing 

plants become more challenging. The marginal suitable (ms) category is a yield of 3 - 5 t/ha/yr 

and the unsuitable areas (NS) have a productivity of less than 2 t/ha/yr. The ms and NS 

categories do not preclude the development and use of biomass for local markets, but would not 

offer an economic proposition in terms of large scale biofuel production for domestic or export 

markets. 

Among the APEC economies, only Thailand’s marginal lands achieved an average score of 

Suitable, while the marginal lands of three economies (Brunei; China; and Papua New Guinea) 

were rated as unsuitable for biofuels feedstock production. The marginal lands in the remaining 

economies were in the range of marginally to moderately suitable, and represent 90% of the 

initial land area assessed. Again, it should be noted that this analysis is done for each economy 

on average, ignoring potential hot spots. 

The total annual biomass resource potential on marginal lands in APEC economies appears to 

be around 1.3 Gt, which translates roughly into about 540 hm3

 of ethanol fuel (260 Mt of 

gasoline equivalent). Current gasoline consumption in the APEC region is about 621 Mt and 

crude oil import is approximately 1.3 Gt (IEA 2006). Therefore the ethanol volume from marginal 

lands would displace two-fifths of the region’s gasoline consumption and one-fifth of its crude oil 

import. 

21 
Many economies could replace a substantial volume of their current gasoline consumption and 

crude oil import with ethanol from marginal lands, as illustrated in the Appendix. These include 

Peru; Australia; Chile; Viet Nam; and New Zealand. But there could be technical or economic 

restrictions on the use of these lands, which suggests that only a portion of this potential can be 

realized. This portion would be different in each economy and could be determined by a detailed 

techno-economic feasibility assessment, which is beyond the scope of this study. 

Previous work by the authors (Milbrandt, A., Overend, R. 2008) evaluated the ethanol potential 

from existing lignocellulosic biomass (primarily crop and forest residues) in the APEC region. 

The study estimated similar volume of ethanol potential, 509 hm3

 of ethanol fuel or 245 Mt of 

gasoline equivalent, enough to displace two-fifths of the region’s gasoline consumption and onefifth of its crude oil import. Comparing the results from both studies at an economy level, it 

appears that in some economies (such as China; Indonesia; Japan; Korea; Malaysia; the 

Philippines; Thailand; and Viet Nam) it might be best to utilize the existing lignocellulosic 

biomass rather than to develop their marginal lands. The ethanol potential from these resources 

is much higher than that from marginal lands (Table 7 and Table 8 in the Appendix). On the 

other hand, economies such as Australia; Canada; Chile; Mexico; New Zealand; Russia; and 

the United States should consider developing their marginal lands given their high biomass, and 

ethanol production potential. However, large lignocellulosic biomass resources exist in those 

economies as well. Therefore, a combination of feedstock — lignocellulosic biomass from farm 

and forest residues and purposely grown on marginal lands — would assure a sustainable 

biomass resource supply for biofuels production in these economies. Such production could 

substantially displace current petroleum products’ import and consumption. 

The present study evaluates only the ethanol potential on marginal lands. Biodiesel could 

present very good opportunities for some of these lands in tropical regions, so future work 

should evaluate the biodiesel potential on marginal lands in APEC economies and compare it 

with the results from the previous study. More detailed assessments at a sub-regional or an 

economy level, with particular attention to multi-year series of NPP data and a finer than 100 

km2

 spatial resolution, would also provide a better understanding of the biofuels potential on 

marginal lands in the APEC region. Since interest in biofuels is in large part driven by 

greenhouse gas mitigation policies, future resource assessment work could be linked to climate 

modeling efforts, which are now starting to have the ability to predict regional changes in climate 

that would affect biofuel output. 

22 
 

 

 

 

 

 

 

 

 

 

References 

1. Biomass Research and Development Board, Increasing Feedstock Production for 

Biofuels Economic Drivers, Environmental Implications, and the Role of Research. 

BR&Di, Washington DC.148pp, 2008 

2. Campbell, J. E., D. B. Lobell, et al. (2008). "The Global Potential of Bioenergy on Abandoned 

Agriculture Lands." Environmental Science & Technology 42(15): 5791-5794. 

3. ESRI, Licensed data, http://www.esri.com/data/data-maps/index.html 

4. Eswaran, H., F. Beinroth, and P. Reich. 1999. Global land resources and population 

supporting capacity. Am. J. Alternative Agric. 14:129-136. 

5. FAO, GeoNetwork, Accessed January 2008, 

http://www.fao.org/geonetwork/srv/en/main.home 

6. FAO-IIASA, Global Agro-Ecological Zones, Accessed January 2008, 

http://www.iiasa.ac.at/Research/LUC/GAEZ/index.htm 

7. Goudriaan, J. and H. H. van Laar, Modeling potential crop growth processes, Kluwer 

Academic Publishers, Dordrecht, The Netherlands, 1994. pp. 238. 

8. International Energy Agency (IEA), Country energy statistics, Oil by Country, 2006. 

http://www.iea.org/Textbase/stats/prodresult.asp?PRODUCT=Oil 

9. IIASA, European Biomass Resources for Transportation Fuels, Policy Workshop – 

Brussels, March 14 2008, http://www.iiasa.ac.at/Research/LUC/luc07/Homepage-NewsHighlights/Brussels_Workshop_March08.pdf 

10. Milbrandt, A., Overend, R., Survey of Biomass Resource Assessments and Assessment 

Capabilities in APEC Economies, November 2008, 

http://www.biofuels.apec.org/pdfs/ewg_2008_biomass_resource_assessment.pdf 

11. Penn State University, Digital Chart of the World, Accessed January 2009, 

http://www.maproom.psu.edu/dcw/ 

12. Schlamadinger, B. and G. Marland (1996). "The role of forest and bioenergy strategies in the 

global carbon cycle." Biomass and Bioenergy 10(5-6): 275-300. 

13. Sinclair, T.R., Muchow, R.C., 1999. Radiation use efficiency. Adv. Agron. 65, 215- 65 

14. UNEP-IUCN, World Database on Protected Areas (WDPA) - Released February 2009, 

Accessed February 2009, http://www.wdpa.org/ 

15. USDA, World Soil Resources, Accessed January 2009, 

http://soils.usda.gov/use/worldsoils/mapindex/index.html 

16. Veroustraete F., Sabbe H., European Net Ecosystem Productivity Determined with 

Space-borne Sensors, Manuscript for Topics in Ecology: Lessons from the past towards 

the future. Revised February 2000, 

http://geofront.vgt.vito.be/geosuccess/documents/European%20NEP.pdf 

17. VITO (Flemish Institute for Technological Research), GeoSuccess, Accessed March 

2009, http://geofront.vgt.vito.be/geosuccess/relay.do?dispatch=introduction 

23 
APPENDIX 

24 
Table 6 Ethanol Potential from Marginal Lands, Share of Current Gasoline Consumption and Crude Oil Import 

in APEC Economies 

* Source: IEA 2006; gray areas – not applicable; N/A – information not available 

25 
Table 7 Ethanol Potential from Existing Biomass Resources, Share of Current Gasoline Consumption and 

Crude Oil Import in APEC Economies 

Source: Milbrandt, A., Overend, R. 2008; * IEA, 2005; gray areas – not applicable; N/A – information not available 

26 
Australia 

Total land area – 7,694,273 km2 

Population - 20,434,176 

Marginal lands - 1,036,239 km2 (13.5% of total area) 

Biomass resource potential on marginal lands – 389,564,000 tonnes/year 

Ethanol potential from marginal lands – 153 hm3 (537% of current gasoline consumption) 

Figure 5 Marginal Lands in Australia 

27 
Brunei Darussalam 

Total land area – 6,078 km2 

Population – 386,511 

Marginal lands - 85 km2 (1.4% of total area) 

Biomass resource potential on marginal lands – 10,723 tonnes/year 

Ethanol potential from marginal lands – 0.004 hm3 (1% of current gasoline consumption) 

Figure 6 Marginal Lands in Brunei Darussalam 

28 
Canada 

Total land area – 9,832,884 km2 

Population - 33,390,141 

Marginal lands - 376,092 km2 (3.8% of total area) 

Biomass resource potential on marginal lands – 98,521,000 tonnes/year 

Ethanol potential from marginal lands – 38.7 hm3 (62% of current gasoline consumption) 

Figure 7 Marginal Lands in Canada 

29 
Chile 

Total land area – 722,511 km2 

Population - 16,284,741 

Marginal lands - 95,645 km2 (13% of total area) 

Biomass resource potential on marginal lands – 39,120,000 tonnes/year 

Ethanol potential from marginal lands – 15.4 hm3 (357% of current gasoline consumption) 

Figure 8 Marginal Lands in Chile 

30 
China 

Total land area – 9,402,887 km2 

Population - 1,321,851,888 

Marginal lands - 511,905 km2 (5.4% of total area) 

Biomass resource potential on marginal lands – 122,719,000 tonnes/year 

Ethanol potential from marginal lands – 48 hm3 (47% of current gasoline consumption) 

Figure 9 Marginal Lands in China 

31 
Indonesia 

Total land area – 1,847,033 km2 

Population - 234,693,997 

Marginal lands - 37,123 km2 (2% of total area) 

Biomass resource potential on marginal lands – 15,494,000 tonnes/year 

Ethanol potential from marginal lands - 6 hm3 (23% of current gasoline consumption) 

Figure 10 Marginal Lands in Indonesia 

32 
Japan 

Total land area – 370,727 km2 

Population - 127,467,972 

Marginal lands - 4,878 km2 (1.3% of total area) 

Biomass resource potential on marginal lands – 1,924,000 tonnes/year 

Ethanol potential from marginal lands – 0.8 hm3 (1% of current gasoline consumption) 

Figure 11 Marginal Lands in Japan 

33 
Republic of Korea 

Total land area – 94,773 km2 

Population - 49,044,790 

Marginal lands - 1,651 km2 (1.7% of total area) 

Biomass resource potential on marginal lands – 588,000 tonnes/year 

Ethanol potential from marginal lands – 0.2 hm3 (1.4% of current gasoline consumption) 

Figure 12 Marginal Lands in Republic of Korea 

34 
Malaysia 

Total land area – 328,536 km2 

Population - 24,821,286 

Marginal lands - 3,534 km2 (1% of total area) 

Biomass resource potential on marginal lands – 1,000,000 tonnes/year 

Ethanol potential from marginal lands – 0.4 hm3 (2.4% of current gasoline consumption) 

Figure 13 Marginal Lands in Malaysia 

35 
Mexico 

Total land area – 1,953,851 km2 

Population - 108,700,891 

Marginal lands - 255,862 km2 (13% of total area) 

Biomass resource potential on marginal lands – 69,000,000 tonnes/year 

Ethanol potential from marginal lands – 27 hm3 (44% of current gasoline consumption) 

Figure 14 Marginal Lands in Mexico 

36 
New Zealand 

Total land area – 267,214 km2 

Population - 4,115,771 

Marginal lands - 17,299 km2 (6.5% of total area) 

Biomass resource potential on marginal lands – 9,599,000 tonnes/year 

Ethanol potential from marginal lands – 3.8 hm3 (78% of current gasoline consumption) 

Figure 15 Marginal Lands in New Zealand 

37 
Papua New Guinea 

Total land area – 458,666 km2 

Population - 5,795,887 

Marginal lands – 7,123 km2 (1.6% of total area) 

Biomass resource potential on marginal lands – 1,432,000 tonnes/year 

Ethanol potential from marginal lands – 0.6 hm3 (current gasoline consumption not available) 

Figure 16 Marginal Lands in Papua New Guinea 

38 
Peru 

Total land area – 1,296,605 km2 

Population - 28,674,757 

Marginal lands - 57,029 km2 (4.4% of total area) 

Biomass resource potential on marginal lands – 66,225,000 tonnes/year 

Ethanol potential from marginal lands – 26 hm3 (1,666% of current gasoline consumption) 

Figure 17 Marginal Lands in Peru 

39 
Philippines 

Total land area – 280,958 km2 

Population - 91,077,287 

Marginal lands – 6,357 km2 (2.3% of total area) 

Biomass resource potential on marginal lands - 1,793,000 tonnes/year 

Ethanol potential from marginal lands – 0.7 hm3 (13% of current gasoline consumption) 

Figure 18 Marginal Lands in the Philippines 

40 
Russia 

Total land area – 16,897,294 km2 

Population - 141,377,752 

Marginal lands - 369,176 km2 (2.2% of total area) 

Biomass resource potential on marginal lands – 154,412,000 tonnes/year 

Ethanol potential from marginal lands – 61 hm3 (105% of current gasoline consumption) 

Figure 19 Marginal Lands in Russia 

41 
Chinese Taipei 

Total land area – 35,980 km2 

Population - 22,858,872 

Marginal lands - 693 km2 (2% of total area) 

Biomass resource potential on marginal lands – 558,000 tonnes/year 

Ethanol potential from marginal lands – 0.2 hm3 (1.3% of current gasoline consumption) 

Figure 20 Marginal Lands in Chinese Taipei 

42 
Thailand 

Total land area – 515,357 km2 

Population - 65,068,149 

Marginal lands - 17,253 km2 (3.3% of total area) 

Biomass resource potential on marginal lands – 10,394,000 tonnes/year 

Ethanol potential from marginal lands – 4 hm3 (38% of current gasoline consumption) 

Figure 21 Marginal Lands in Thailand 

43 
United States 

Total land area – 9,426,295 km2 

Population - 301,139,947 

Marginal lands - 1,214,007 km2 (13% of total area) 

Biomass resource potential on marginal lands – 377,413,000 tonnes/year 

Ethanol potential from marginal lands – 148 hm3 (19% of current gasoline consumption) 

Figure 22 Marginal Lands in the United States 

44 
Viet Nam 

Total land area – 322,743 km2 

Population - 85,262,356 

Marginal lands - 21,090 km2 (6.5% of total area) 

Biomass resource potential on marginal lands – 11,281,000 tonnes/year 

Ethanol potential from marginal lands – 4.4 hm3 (79% of current gasoline consumption) 

Figure 23 Marginal Lands in Viet Nam 

45 
 

Prepared By: 

Anelia Milbrandt 

National Renewable Energy Laboratory (NREL) 

1617 Cole Blvd 

Golden, Colorado, USA 80401 

Phone: (303) 275-3000 

Website: www.nrel.gov 

Dr. Ralph P. Overend 

NREL Research Fellow (Retired) 

Ottawa, Ontario, Canada 

Produced for: 

APEC Secretariat 

35 Heng Mui Keng Terrace Singapore 119616 

Tel: (65) 68919-600 Fax: (65) 68919-690 

Email: info@apec.org Website: www.apec.org 

© 2008 APEC Secretariat 

APEC#209-RE-01.4 

46