Environ. Sci. Technol. 2011, 45, 334–339
Land Availability for Biofuel Production X I M I N G C A I , * ,† X I A O Z H A N G , † A N D DINGBAO WANG‡ Ven Te Chow Hydrosystems Laboratory, Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States, and Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, Florida 32816-2450, United States
Received March 1, 2010. Revised manuscript received November 9, 2010. Accepted November 19, 2010.
Marginal agricultural land is estimated for biofuel production in Africa, China, Europe, India, South America, and the continental United States, which have major agricultural production capacities. These countries/regions can have 320-702 million hectares of land available if only abandoned and degraded cropland and mixed crop and vegetation land, which are usually of low quality, are accounted. If grassland, savanna, and shrubland with marginal productivity are considered for planting low-input high-diversity (LIHD) mixtures of native perennials as energy crops, the total land availability can increase from 1107-1411 million hectares, depending on if the pasture land is discounted. Planting the second generation of biofuel feedstocks on abandoned and degraded cropland and LIHD perennials on grassland with marginal productivity may fulfill 26-55% of the current world liquid fuel consumption, without affecting the use of land with regular productivity for conventional crops and without affecting the current pasture land. Under the various land use scenarios, Africa may have more than one-third, and Africa and Brazil, together, may have more than half of the total land available for biofuel production. These estimations are based on physical conditions such as soil productivity, land slope, and climate.
1. Introduction A key constraint on our ability to expand biofuel production to reduce our dependence on fossil fuels is likely the limited amount of land available for producing energy crops and for expanding refinery and transportation infrastructure. Given that many countries and regions in the world already experience pressure on land needed for critical socioeconomic activities, converting existing cropland to or developing new land for biofuel production raises immediate concerns. These concerns include the food versus fuel debate (1); the effects on the livelihoods of small-scale farmers, pastoralists, and indigenous people; the threat to nature conservation (2); the possible increase of carbon emissions (3); and invasive species in agroecosystems (4). Moreover, land use change usually causes changes in water use, and consequently, biofuel production may aggravate water stress, which is already a growing worldwide issue (5). A fundamental un* Corresponding author phone: (217) 333 4935; e-mail: xmcai@ illinois.edu. † University of Illinois at Urbana-Champaign. ‡ University of Central Florida. 334
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certainty underlying the current understanding of these potential impacts is the land use change that will occur as biofuel production increases. The pressing question in this debate is whether or not the world has sufficient land available for biofuel crop cultivation that could be utilized without compromising the concerns described above. More specifically, what types of land can be used for sustainable biofuel production, how much is available, where is the land, and what is the land currently used for? The answers to these questions will provide the basis for justifying the potential of biofuel and evaluating the associated environmental and socioeconomic impacts.
2. Background Scientific and stakeholder communities have been discussing and debating whether restricting the development of biofuel crops to “marginal agricultural land” (MAL) can ameliorate the conflicts among food production, biofuel production, and the environment (2, 3, 6-8). MAL has low inherent productivity for agriculture, is susceptible to degradation, and is high-risk for agricultural production (9). In addition, MAL is recognized as an economic term, in which the marginality of the land is related to soil productivity, cultivation techniques, and agriculture policies, as well as macroeconomic and legal conditions (10). Several other more specific terms have been used to describe MAL, including abandoned farmland, degraded land, wasteland, and idle land (10). Abandoned land consists of land that was previously used for agriculture or pasture but has been abandoned without being converted to forest or urban areas (11). Degraded land is subject to long-term loss of ecosystem function and services, which cannot be recovered without aid (12). Wasteland, similar to degraded land, describes land with physical and biological characteristics that render it unfavorable for agriculture use (13). Finally, idle land comprises abandoned, degraded, and waste lands, as well as land not otherwise developed and designated conservation areas (14, 15). The land available for biofuel production based on the criteria listed above has been estimated at national and global levels. For instance, a study from China identified 35-75 million hectares (mha) of “marginal land” that might be suitable for biofuel feedstock crops, including saline land, steep hillsides, and idle land (16), while another research report from China estimated 116 mha of marginal land (mainly in the southwestern part of the country), but only roughly 20% (23 mha) was biophysically suitable for feedstock production (17). Two studies from India provided different estimates too, one announced there was a possibility to convert up to 14 mha of the so-called “wastelands” to jatropha for biodiesel (2), and the other (18) claimed that India had 60 mha of wasteland, half of which might be suitable for jatropha. It was estimated that Africa has at least 500 mha of marginal, unused, and underused land (19). Previous work (20, 21) suggested that about 500 mha of degraded farm lands in tropical regions were available for biofuel production based on a qualitative photosynthesis assessment considering confounding factors (e.g., climate) but neglecting soil degradation, and 385-472 mha of abandoned agricultural land in the world was estimated using historical agriculture databases (11, 22), based on coarse global estimates with limited information at regional scales. However, the estimates described above used inconsistent definitions of land availability for biofuel production and are subject to uncertainty and incompleteness due to the data sources. Given the data’s limitations and the complexity caused by the spatial het10.1021/es103338e
2011 American Chemical Society
Published on Web 12/09/2010
FIGURE 1. A framework for assessing MAL by learning-based fuzzy logic modeling. erogeneity of socioeconomic conditions and agricultural technologies, it is very difficult, if not infeasible, to produce a precise estimate in a short period of time. In this paper, we present a meaningful global estimate based on physical characteristics of land, such as soil productivity, land slope, and climate, using existing global databases, including the remotely sensed land use estimates (23). The results are presented for regions and countries with major agricultural production capacity or potential, including Africa, China, Europe, India, South America, and the continental United States. On the basis of the land availability for biofuel production, we also estimate the net energy gain (NEG) using average NEG per hectare obtained from the literature.
3. Methodology Land available for bioenergy production is assessed through two steps. The first is to identify land with marginal agricultural productivity and the current cover of that land. The land available for bioenergy feedstock production is determined under several scenarios that consider cropland, grassland, or both. A fuzzy logic modeling (FLM) technique is employed to assess the land productivity (Figure 1). The outputs from the FLM are improved by using a learning approach that extracts the criteria determining land categories from the existing land use data, i.e., remotely sensed world cropland maps (24). Finally the NEG is estimated. 3.1. Land Suitability Indices and Data Sources. This study follows the index of soil rating for plant growth (SRPG) developed by USDA-NRCS (25) and tested with the Northern Plains region of the United States (26). SRPG takes an approach based on a multiple-criteria evaluation. We extend SPRG to the global scale using global data sets with the best available spatial resolution, considering the following four sets of indices: soil productivity properties, land slope, soil temperature regimes, and humidity index (see Table S1 of the Supporting Information for spatially referenced data sets, including global land use and land cover data, most with a 30 arc-second resolution grid for the global coverage). An overall soil productivity rating is computed for each land pixel according to the five categories and 16 soil properties in the Harmonized World Soil Database (Table S3, Supporting Information). Each of the soil property ratings is assigned with a value between zero and one; the rating of each of the categories takes the average over the ratings of all the properties belonging to the category; the overall soil productivity rating is the product of the ratings of all the categories.
The topography data obtained from Global Terrain Slope (GTS) (27) is compiled using elevation data from the Shuttle Radar Topography Mission (SRTM) with 30 arc-second resolution. GTS includes eight slope classes: 0-0.5%, 0.5-2%, 2-5%, 5-10%, 10-15%, 15-30%, 30-45%, and >45%, and thus, the slope files contain eight maps, in which the value of each pixel represents the area percentage belonging to this particular slope class. For the land pixel at a particular location, the sum of the values from all eight files equals 100. The soil temperature regime is adopted to assess the temperature effect on land suitability. USDA-NRCS (28) developed the global soil temperature regime, which includes 14 soil temperature classes. Given the strong correspondence between air temperature and soil temperature, we use the former as a substitute of the latter. Three air temperature ranges are classified to reflect the 14 soil temperature classes (Classes 3 and 4 with air temperatures below 265 K, classes 5-8 between 265 and 280 K, and classes 9-16 above 280 K). See Figure S1 (Supporting Information) for the correspondence relationship. A humidity index (HI) is an indicator of the degree of humidity of the climate at a given location. There are different definitions of HI; this study uses the ratio of average annual precipitation (P) over potential evapotranspiration (PET) (29). The computed HI for the period of 1961-1990 is shown in Figure S2 (Supporting Information), which agrees with a previous study (30) that used different data sets. 3.2. Fuzzy Logic Modeling for Land Productivity Assessment. FLM is used to treat the uncertainty of the global data sets and the fuzzy nature inherent in land classification according to multiple criteria. FLM has been proved to be a powerful tool to address data variability, imprecision, and uncertainty (31) and to treat the ambiguity and uncertainty involved in generating realistic continuous classifications (32), especially in land productivity assessments (33, 31). FLM is composed of three major steps, i.e., fuzzification, fuzzy rule inference, and defuzzification. Fuzzification is the process of converting quantitative values of a factor into qualitative categories in terms of land productivityslow (L), marginal (M), and regular (R)sby using a membership function of the factor (See Figures S5-S9 in the Supporting Information), which is based on empirical knowledge or expert opinion. The output of the fuzzification process is the probabilities that describe to what degree the land belongs to L, M, or R in terms of an individual factor. To determine the probabilities that characterize L, M, or R over all the factors, the process VOL. 45, NO. 1, 2011 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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of fuzzy rule inference is conducted, which is based on a set of empirical rules. An inference rule consists of a condition part (IF) and a conclusion part (THEN) (31). For each category (L, M, or R), one or several rules are combined to give one value, i.e., the probability of the land belonging to L, M, or R. Finally, defuzzification translates the probabilities resulting from the fuzzy rule inference process into a a single value ranging from 0 (lowest productivity) to 1 (highest productivity) (35). For this purpose, a defuzzification membership function (see Figure S9, Supporting Information) is used to specify the membership value of L, M, or R corresponding to a specific value of the land suitability score. On this membership curve, a range of the land suitability indicator (a subrange of 0-1) is given for L, M, and R, respectively, which varies spatially. The medium of the range is used for a representative land suitability value following the center-of-maximum (CoM) method (36, 31), one of the most frequently referenced methods. Using the representative values of L, M, and R and the probabilities resulting from the rule inference process, a weight-averaged membership function value of the land suitability is calculated. This is used as the overall land suitability indicator for each land pixel. Some empirically determined thresholds of L, M, and R are then applied to the aggregated land suitability score to determine if the land belongs to L, M, or R. In this study, the lower bounds of 0.70 for R and 0.55 for M are determined by the learning process described below. More detailed FLM procedures, including a demonstration example, are provided in the Supporting Information. The FLM includes several items which are based on empirical knowledge and expert opinion, such as the membership function for individual factors, the rules for aggregation, and the thresholds to classify the final land category (L, M, or R). These items can be adjusted or calibrated through a learning process by comparing the regular productivity of land and cropland from remote sensing data (Figure 1). The premise for the learning process is that the cropland is usually classified as regular productivity land. Two objectives are followed to adjust the membership functions and rule subsets: (1) maximizing the total number of land pixels which are both actual cropland and modeled to the R category, while minimizing the total number of pixels which are cropland but not modeled to the R category or are noncropland but modeled to the R category, and (2) adjusting and verifying the modeled marginal land with the land cover statistical data, such as the pasture land data from FAO (34). The FLM of land productivity is applied to the six countries/ regions selected for land productivity assessment: The membership functions and inference rules vary with these spatial regions. The current land use (cover) of regular and marginal productivity land is identified by overlaying the land productivity map with a land cover map. The following typical land covers are recognized in the land cover map: crop, mixed crop and natural vegetation (including small crop fields), grassland, savanna, shrubland, forest, wetland, and urban land. For the selected six countries or regions, we obtain the areas that fall into each of these land cover categories. 3.3. Scenario Design for Land Availability Projection for Biofuel Production. On the basis of the FLM outcomes, we estimate the land available for biofuel production under several scenarios for the selected countries and regions. Scenario 1 considers only mixed crop and natural vegetation land with marginal productivity, which includes at least part of the abandoned, wasted, or idle agricultural land and some small crop fields. Scenario 2 adds cropland with marginal productivity, which represents degraded or low-quality cropland, to the abandoned land and wasteland already considered in scenario 1 (refer to Table 1 for the current land 336
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TABLE 1. Modeled Land Area with Marginal Productivity on Different Land Covers (in mha)
cropland crop/veg mixing forest shrubland savanna grassland wetland urban
Africa
China
Europe
India
South America
United States
65.34
81.87
69.03
91.19
48.25
24.68
66.43 195.58 22.72 300.65 25.79 3.41 0.24
52.33 51.49 22.40 19.22 37.23 0.23 0.20
33.36 29.62 4.83 1.00 0.32 0.23 0.97
18.46 11.33 17.93 9.73 0.83 0.13 0.29
108.11 343.74 35.71 123.98 27.02 1.62 0.80
43.40 101.89 15.03 9.37 34.39 0.24 3.04
covers on marginal lands). Scenario 3 is bold and goes beyond cropland to consider the potential of using low-input highdiversity (LIHD) grassland to produce biofuel (6, 37). It is assumed that some potential lies in the marginal productivity portion of grassland, savanna, and shrubland, while the regular productivity regions of these environments are maintained as pasture or reserved for future cropland development. However, scenario 3 is probably not realistic, since even marginal land is used for pasture. This leads to scenario 4, which discounts the pasture land that is possibly accounted by the estimate of scenario 3. After scenario 3, scenario 4 adds (i) the regular land used for mixed crop and vegetation because that land is probably pasture currently and (ii) grassland, savanna, and shrubland with either regular or marginal productivity, but discounts the total current pasture land (38). 3.4. Net Energy Gain Estimation. Given the estimates of land available for biofuel production, we estimate the net energy gain (NEG), which is defined as the difference between the amount of energy gained from the biomass harvest and the energy consumed for feedstock production and ethanol refinement. Two types of biofuel crops are considered, one is LIHD mixtures of native grassland perennials (16 perennial herbaceous grassland species according to the experiments reported in ref 6); the other is the second-generation biofuel crops (cellulosic plants such as miscanthus and swithgrass). There is a wide range of NEG for both LIHD prairies and the second-generation cellulosic biofuel feedstocks, depending on natural soil, climatic heterogeneity, and agricultural inputs (39). We adopt an average estimate from the various sources, particularly the most recently published studies. The amount of NEG for LIHD biomass is estimated as 17.8 GJ ha-1, when converting LIHD biomass into cellulosic ethanol and electricity (6, 40), of which ethanol may consist of about 90% (40), i.e., 16 GJ ha-1. The range of NEG for the secondgeneration biofuel feedstocks varies even more, with a range of 60-160 GJ ha-1 for switchgrass (41, 42) and 115-590 GJ ha-1 for miscanthus in Europe (43-45). On the basis of these estimates, we use a moderate range of 60-140 GJ ha-1 for mixed second-generation biofuel crops. It is assumed that the second-generation biofuel crops are planted on the marginal croplands as specified under scenarios 1 and 2, and LIHD prairies are planted on the grassland as specified under scenarios 3 and 4 only. Using the energy generation ranges specified above and an estimated global liquid fuel consumption of 1.9 × 1011 GJ in 2006 (46), total net energy gain and the percentages relative to the world’s total liquid fuel consumption under scenarios 1-4 for the six countries and regions are computed.
4. Results Table 1 presents the modeled marginal land area (M) on different land covers, in million hectares (mha). This answers the question of how much MAL is available in the six regions and countries and what is the current land cover (or use) of
TABLE 2. Potential Area Available for Biofuel Crop (Cellulosic Crops and LIHD) and the Corresponding Net Energy Gain for Six Countries or Regions under Four Scenarios land by regions (mha) scenarioa
Africa
China
Europe
India
South America
US
total
net energy gain (GJ × 109)b
percentage to global liquid fuel consumption
S1 S2 S3 S4
66 132 481 314
52 134 213 152
33 102 109 111
18 110 138 151
108 156 343 256
43 68 127 123
320 702 1411 1107
19-45 42-98 53-110 49-105
10-24 22-52 28-58 26-55
a Notation: S1, marginal mixed crop and vegetation land (part of abandoned land); S2, S1 and marginal cropland (abandoned and degraded crop land); S3, S2 and marginal grassland, savanna, and shrubland (land with LIHD); S4, S3 discounted by the land possibly used for pasturing at present. b The NEG of LIHD prairies is 16 GJ ha-1 (5) and 60-140 GJ/ ha for mixed second-generation biofuel feedstocks such as switchgrass and miscanthus (32-36). The NEG is calculated assuming that second-generation of biofuels crops are planted on the marginal croplands and LIHD prairies on the marginal grassland. The world liquid fuels consumption in 2006 is 1.9 × 1011 GJ (37).
FIGURE 2. Maps of land available for bioenergy production under scenario 4 in U.S., Europe, China, India, South America, and Africa. the MAL. The numbers in Table 1 are used to calculate land available for bioenergy crops under the four scenarios defined above; Table 2 shows the potential land area for each of the scenarios, the corresponding NEG, and the percentage of the global liquid fuel consumption. According to scenario 1, the total available land for biofuel production in all the regions sampled in this study is 320 mha, which is mixed crop and natural vegetation land with marginal productivity (i.e., abandoned land and waste cropland, Table 1). The area from scenario 2, including degraded or low quality cropland and the abandoned land and wasteland, increases to 702 mha. This number is bit higher than a previous estimate of 500 mha (6, 20, 21). Note that this and the previous estimate are based on different approaches and different global regions (six selected countries and regions for this estimate versus mainly tropical areas in refs 6, 20, and 21). With the broadened definition of marginal land in scenario 3, which includes grassland for LIHD, the total land availability increases to 1411 mha. If considering the current pasture land under scenario 4, i.e., adding all the land to scenario 3 that is possibly used for pasturing but discounting the total pasture land, the total land availability for biomass production is 1107 mha. Figure 2 shows the distribution of the land available for bioenergy under scenario 4 in each of the regions and countries. Under these scenarios, Africa alone possesses more than one-third of this land, while Africa and Brazil together own more than half of all the land estimated in this study. It should be noted that we do not consider the potential of converting current forest land into bioenergy cropland. However, there has been a dynamic exchange between forest and agricultural land in many regions. Even though some forest land has been cleared for crops, other cropland abandoned before has been reverted to forest, similar to what
occurred in the eastern U.S. during the 20th century (47). Moreover, land use and land cover in some regions may undergo cyclic changes indicative of local crop rotation practices. Such land use shifts may provide an opportunity to increase the land use value and sustain land productivity by switching energy crops with other crops. Although it is hard to justify the potential of these land use shifts at the global level, the benefit at the local level could be significant if biofuel crops are proven to have potential in the land use rotation cycle. Moreover, under all the scenarios, it is assumed that the bioenergy crops will be rainfed and do not need irrigation. In arid or semiarid areas, if irrigation is allowed, the land availability will be significantly increased. On the basis of the land estimates under the four scenarios and the range of the NEG per unit of area for LIHD prairies and second-generation biofuel crops, total NEG is estimated as shown in Table 2, including the NEG in billion GJ and the percentage of current global liquid fuel consumption under the four scenarios.
5. Conclusions Following the debate on marginal land use for biofuel production, we provide an estimate of land availability under each of the four scenarios previously specified. Our results show that the countries/regions with major agricultural production capacity in the world may have 320-702 mha of land available for that purpose if only abandoned or degraded cropland is used for biofuel production. Energy production on this land could amount to 10-52% of current world liquid fuel consumption by planting second-generation biofuel feedstocks. Furthermore, if developing new land by converting marginal grassland to energy cropland, the land availVOL. 45, NO. 1, 2011 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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ability will be up to 1411mha. Discounting the pasture land that might be included in the estimate, the land available for bioenergy is estimated as 1107 mha. Planting secondgeneration biofuel feedstocks on marginal croplands and LIHD prairie on marginal grassland may fulfill 26-55% of the current world liquid fuel consumption, without compromising the use of land with regular productivity for conventional crops and without affecting the current pasture land. The range of the possible NEG depends on the yield of the energy crops, which is related to climate, soil quality, water, and other agricultural inputs varying over the world. Under any of the projections, Africa has more than onethird, and Africa and South America have more than half of the total land available for biofuel production. Thus, the locations of biofuel production potential and demand are not consistent, given that larger fuel demands exist in the US, Europe, China, and India. The transportation of this fuel between continents will cause additional energy consumption. It should be noted that not all the estimated land, particularly abandoned or degraded cropland, can be used for bioenergy production. Furthermore, trade-offs may exist between the present environmental and ecological value of MAL and the potential value for biofuel production. Note that LIHD biofuels can be carbon negative, because net ecosystem carbon dioxide sequestration (4.4 Mg ha-1 yr-1 of carbon dioxide in soil and roots) exceeds the amount of fossil carbon dioxide released during biofuel production (0.32 Mg ha-1 yr-1) (6). However, the use of grassland for feedstock harvest may require significant infrastructural investment. The final land availability will be affected by not only the physical feasibility factors described above but also global energy and food markets, technological innovations, social and institutional adaptations and accommodations, engineering infrastructural support, and resources availability (e.g., water for refinery industry). Nevertheless, the MAL land maps generated in this study can be used as a base to consider these conditions for further exploration of land available for biofuel production; the estimates can also be integrated with research efforts in biology and agronomy to help identify productive plant species, genetics, and create sustainable crop techniques (48). The challenges for the world are to identify the most highly productive plant species that can be grown on the various types of marginal or abandoned lands (48) and to develop innovative land use systems specifically designed for energy crops that have high energy productivity and meanwhile support species diversity and community development.
Acknowledgments This study was partially funded by the Energy Biosciences Institute(EBI),cosponsoredbyUniversityofCaliforniasBerkeley, University of Illinois at Urbana-Champaign, and the Lawrence Berkeley National Laboratory. This study was also partially supported by U.S. NSF grant EFRI-083598. The authors are grateful to three anonymous reviewers for their helpful suggestions.
Supporting Information Available Data sources for land assessment and detailed description of the fuzzy logic model. This information is available free of charge via the Internet at http://pubs.acs.org/.
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