Simulating the impact of biofuel development on country-wide land-use change in India

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Simulating the impact of biofuel development on country-wide land-use change in India Ru¨diger Schaldach a,*, Jo¨rg A. Priess b, Joseph Alcamo a a b

Center for Environmental Systems Research, University of Kassel, Kurt-Wolters-Straße 3, D-34109 Kassel, Germany Helmholtz Centre for Environmental Research (UFZ), Permoserstraße 15, D-04318 Leipzig, Germany

article info

abstract

Article history:

India’s growing population and economy generate an increasing demand for energy. Facing

Received 26 March 2008

the decline of global fossil fuel resources, the Indian government and energy industry are

Received in revised form

considering the long-term expansion of biofuel production in order to increase energy

26 March 2010

security. This development leads to a strong competition of energy crops versus food crops

Accepted 18 August 2010

for land and may result in an increasing pressure on natural resources. In a pilot scenario

Available online 25 September 2010

study, the LandSHIFT model is applied to assess the impact of biofuel production on landuse change in India up to the year 2030. The model aims at the spatially explicit simulation

Keywords:

of land-use change and its relation to other global change processes on the national up to

Bioethanol production

the global scale. It explicitly addresses competition between land-use activities such as

India

human settlement, biofuel production and food production as well as the resulting effects

Land-use change

on the spatial extent of natural land. Baseline of the study is a simulation with drivers from

LandSHIFT model

the “Order from Strength” scenario of the Millennium Ecosystem Assessment. To illustrate

Scenario analysis

the consequences of expanded biofuel production for the extent of natural land, we calculate three scenarios of bioethanol production to substitute 5%, 10% and 20% of the expected petrol demand in 2030. In the simulations shown, a comprehensive linkage is made between driving forces (such as population change) and policies (such as biofuel usage) that will affect land-use change over the coming decades. ª 2010 Elsevier Ltd. All rights reserved.

1.

Introduction

India’s emerging economy has a growing demand for energy. In 2040, India is expected to account for 15% of the world’s oil demand [1]. Facing the decline of global fossil fuel resources and the risk of climate change, the Indian government and energy industry are considering the long-term expansion of biofuel production in order to increase energy security [2]. Biodiesel and bioethanol will play a prominent role to meet the growing fuel demands of the transport sector [3]. While biodiesel has a wide range of applications for trucks, busses, agricultural machinery or for water pumps [4,5], ethanol is

mainly used to substitute petrol for individual transport, which is projected to have enormous growth rates over the coming 30 years [3]. For biodiesel production, India’s national planning promotes the cultivation of jatropha, which is believed capable of achieving relatively high yields on low productivity lands. Nevertheless, there are still uncertainties about the utility of jatropha due to the lack of in-depth research and missing experience with large-scale experiments [6,7]. On the other hand, feedstocks used for the production of bioethanol such as maize and sugarcane stand in direct competition for land with food crops and natural vegetation. This aspect of competition for land is rarely

* Corresponding author. Tel.: þ49 561 804 3175; fax: þ49 561 804 3176. E-mail address: [email protected] (R. Schaldach). 0961-9534/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.biombioe.2010.08.048

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addressed in recent model-based assessments in the biofuel sector. Most studies focus on the supply side, accounting for the regional or global potentials to produce biomass for bioenergy under specific conditions of agricultural management [8e10]. In contrast, the study presented in this paper focuses on the demand side. A spatially explicit simulation model is applied to illustrate the consequences of additional production of biofuels on country-wide land-use changes. An overview of land-use modeling techniques is given by Verburg [11]. For this study, we use the LandSHIFT model [12], which explicitly simulates the competition for land resources between landuse activities: human settlement, biofuel production and food production. The baseline chosen is a simulation with drivers from the “Order from Strength” scenario of the Millennium Ecosystem Assessment, which describes a regionalized world with an emphasis on security and economic growth. Future biofuel demands are derived from Indian policy goals on bioethanol production for mandatory blending of petrol. Within this frame, we calculate three scenarios of ethanol production from sugarcane to substitute 5%, 10% and 20% of the expected petrol demand in 2030 and analyse the resulting effects on the spatial extent of natural land. The following section briefly describes the structure and functioning of the model. Subsequently, we discuss the test and validation of selected model components. Finally, simulation results from the scenario analysis are presented.

2.

Methods

2.1.

Overview

The land-use model LandSHIFT [12] has been adapted for the Indian case study. LandSHIFT is an integrated model focusing on national up to global scale land-use change dynamics. Fig. 1 depicts the conceptual model design. Model input is a set of country-level driving variables and parameters, including time series on socio-economic development (e.g. population growth) and on the production of agricultural commodities. Model output is a time series of grid maps of the changing landuse pattern in 5-year time intervals, which can be processed by Geographic Information Systems (GIS), or can serve as input to models for environmental impact assessment. LandSHIFT integrates functional model components to represent both human and environmental aspects of the land-use system. The current model version includes a component to simulate land-use change (land-use-change module) and a component to calculate crop yields (crop productivity module). The information on crop yields serves as input to the land-use decisions calculated by the land-use-change module.

2.2.

Spatial resolution and input data

Landshift operates on multiple geographic scale levels. The macro-level is defined by countries and is used to specify the exogenous model variables and parameters. The geographic extent of each country is defined by a grid (micro-level) with the spatial resolution of 5 0 (approximately 9 km  9 km at the Equator).

Here, landscape characteristics such as terrain slope as well as information on population density and nature conservation areas are defined as properties of each grid cell (Table 1). Moreover, each cell has one dominant land-use type in order to facilitate a direct access to land-use information, which is a major advantage for further processing of the model output, for example with Geographic Information Systems (GIS). The simulated land-use types include urban area, cropland, pasture, unmanaged natural land and forest. Cropland is further sub-divided into 10 crop types while the category “natural land” includes all non-forest types of vegetation such as shrubland, barren land with sparse vegetation cover and open woodlands. The model is initialized with data for the period 1990e1993 describing the “early 1990s” as starting conditions for the simulation runs. This allows the verification of the model for a historical period. The initial land-use/landcover map is based on the global IGBP land-cover dataset, derived from AVHRR source satellite imagery data [13]. The content of this map was expanded by additional information on the spatial distribution of crop types generated with a procedure that merges land-cover data with sub-national census data [14]. The resulting crop map has been tested against comparable products such as the maps produced by SAGE [15]. Information on the initial population density was derived from the global gridded datasets provided by CIESIN [16].

2.3.

Land-use change module

Central component of LandSHIFT is the land-use change module (LUC-module). The task of this module is to regionalize country-level demands for area intensive commodities (e.g. crops) and services (e.g. settlement area) to the micro-level. The demands are derived from the exogenous model variables. For example, the demand for new settlement area is computed from population growth and assumptions of per capita demand for housing area. In contrast, crop demands are defined as the production of each crop within the country. Each commodity and service is linked to a specific land-use type, i.e. it can be produced or provided on grid cells with that land-use type. The LUC-module allocates these demands to the most preferable cells by changing the land-use type of as many cells as are needed to meet the country demand. For the present study, the LUC-module is divided into two sub-modules for the land-use activities settlement (METRO) and crop cultivation (AGRO). While METRO manipulates the population density and settlement fraction of each cell, AGRO is responsible for spatially distributing the crop demands. The activities compete with one another for land resources. Moreover, requirements for food and bioenergy lead to competition between different crop types, i.e. within the domain of the crop cultivation activity. In order to resolve these different levels of competition, we formulate a multi-objective allocation problem and solve it in two steps. First, a hierarchy of land-use activities is specified reflecting their relative economic importance. This results in the sequential execution of the submodules, starting with METRO, followed by AGRO. The second step is to allocate land within each land-use activity. Each sub-module implements two functions that are executed in subsequent order in every time step. First, a preference ranking is carried out on the micro-level in order to

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Fig. 1 e Box diagram of the LandSHIFT model. The model consists of two functional components: the land-use-change module (LUC-module) and the crop productivity module.

identify the most preferable cells for the land-use types that the sub-module is responsible for. Then, based on this ranking the land allocation routine assigns the demand for the commodities or services to the micro-level grid cells with the highest preference values. For the preference ranking, each sub-module conducts a multi-criteria-analysis (MCA) in order to determine the preference value of each micro-level grid cell, based on a set of local cell properties and neighborhood relations [17]. Then,

the cells are ranked according to their values. The preference value Jk of grid cell k is expressed by Equation(1): Jk ¼

n X i¼1

  wi fi pi;k

suitability



m Y   gj cj;k j¼1

constraints

;

with

    ¼ 1; and fi pi;k ; gj cj;k ˛½0; 1

X

wi

i

(1)

wi weight of suitability factor pi fi() value function applied on factor pi

Table 1 e Data sources used for the scenario analysis. Variable

Spatial level

Crop production

Country

Change in crop production

Purpose

Comment

Source

Baseline definition

Production of major crop types per country, from FAOSTAT; see [11] for categories Change in food crop production relative to baseline based on IMPACT model [21] analogue for crop production, also based on IMPACT change in human population count per country relative to baseline Additional sugarcane production for bioethanol

[23]

Scenario driver

Change in crop yields Population growth Change in sugarcane production Land-use type

Grid, 5 0

Initial condition

Population density Terrain slope

Landscape variable

Conservation areas

Zoning regulation

Map of major crop types plus grazing land plus a selection of natural land-cover types Population density on grid level Median slope within a 5 0 grid cell derived from GTOPO30; includes seven slope classes (see reference) Areas designated as national or international conservation areas

[28]

Prepared for this study [14] [16] [44]

[45]

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pi,k suitability factor i on cell k gj() value function applied on constraint cj cj,k constraint j on cell k The first term of Equation (1) is the sum of weighted factors pi that contribute to the cells’ suitability for a particular landuse type. These factors include n landscape properties that reflect preferable local conditions for settlements or agriculture. The factor weights wi determine the importance of a single factor pi in the analysis. The second term is appended by multiplication and represents m land-use constraints cj, which reflect important aspects of human decision making. For example, land-use changes in nature conservation areas can be prohibited by setting the corresponding constraint to zero. Both pi and cj are standardized by value functions fi and gj, which have a co-domain from 0 to 1 [18]. This allows considering the degree of implementation of a constraint, e.g. the degree of protection of a national park from settlement activities. All the components of the MCA analysis (factors, weights, constraints) are defined on country level and are implemented as time-dependent variables to represent changing environmental and political boundary conditions during the simulation period. Their values can be determined either empirically (e.g. by geo-statistical analysis) or by expert knowledge. Here, tools like the Analytical Hierarchy Process [19] can be applied to formalize the process of knowledge acquisition. The simulation experiment for India starts with relatively simple assumptions. Relevant suitability factors pi for METRO are terrain slope and neighbourhood to existing settlement area, while AGRO considers the four factors local crop yields (computed by the crop productivity module), terrain slope, neighbourhood to settlement area and neighbourhood to cropland. Moreover, the value functions fi and gj are strictly linear and the factor weights wi are assumed as equal. Constraints for both land-use activities include the exclusion of forests and nature conservation areas from landuse change (see scenario description). The METRO sub-module implements a rule-based algorithm [12] for the allocation of new settlement area whereas AGRO formulates a “compromise solution”-problem to determine a near optimum crop allocation. The optimization problem is solved by the MOLA (Multi Objective Land Allocation) heuristic [17,20]. The original algorithm has been modified in two ways. First, instead of an amount of area, it allocates an amount of crop production. Second, conflicts are resolved not only by preferring the land-use type with the preference value closest to the ideal point [20] but also by seeking stability of the existing crop pattern. The amount of crop yield that can be produced on a cell is determined by its local production function P. The production P of a grid cell k for crop type c at time step t is defined according to Equation(2): Pk;c ðtÞ ¼ basec  ðYk;c ðtÞ  Areak ðtÞÞ

(2)

Pk,c(t) production on cell k of crop type c in time step t [Mg] Yk,c(t) yield on cell k of crop type c in time step t [Mg km2] basec management parameter for crop type c Area k(t) area available on cell k for crop production [km2] Y is the local yield of crop type c in time step t as computed by the crop productivity module. “Area” is the geographic cell size corrected by the fraction that is used for settlement. The

parameter “base” serves as a proxy for agricultural management factors (e.g. irrigation and multi cropping), which are currently not considered by the model. The parameter is calibrated by fitting the country-level crop production provided by FAO census data for the base year to the simulated production on the cells of the initial land-use map.

2.4.

Crop productivity module

The crop productivity module uses the process-oriented agro-ecosystem model DayCent [21] to calculate global yield maps on a 30’ grid for major crop types under the local climate, soil and management conditions [22]. For sugarcane, we have developed a new DayCent crop parameter set. The national production of sugarcane is validated against FAO census data from 1992 to 1998 [23]. Assuming static crop management for this period (application of 100 kg Nitrogen fertilizer per ha and year), the simulated national production represented 85%e102% of the FAOSTAT estimates, depending on the five-year mean that was used as reference period. We accepted minor deviations of regional yield levels [24], because no corresponding information on fertilizer application was available. Calculations for this study are conducted for climate normal conditions (1961e1990). The model outcome is geographically mapped to the 5 0 cells within the boundary of each 30 0 cell. Changes in crop yields in cell k for crop c during the course of the simulation are induced by the exogenous driving variable “technological change” (techc) as shown by Equation(3): Yk;c ðtÞ ¼ Yk;c 0  techc ðtÞ

(3) 2

Yk,c(t) yield in cell k of crop type c in time step t [Mg km ] Yk,c0 yield in cell k of crop type c in time step 0 [Mg km2] techc impact of technological change on yield of crop c [%] Cell level crop yields for the simulation base year (Yc0) are the values from the global yield maps (see above). The variable techc(t) describes the increase (or decrease) of crop yield in each simulation time step, attributed to advanced farming practices. In our study, this information is provided by the agricultural trade model IMPACT [25].

3. Partial validation of the crop cultivation sub-module A comprehensive validation of the applied land-use model was beyond the scope of this paper. Also, due to the lack of consistent land-cover maps for at least two different points in time a verification of the simulated land-use/land-cover pattern was not possible. Therefore we decided to implement two indirect methods for validating our model. Since the crop cultivation activity of LandSHIFT plays the most prominent part for our analysis, we focus on the validation of selected parts of this submodule. The partial validation considers location of land use and quantity of land-use change [26]. Validation of location is done by testing the plausibility of the model assumptions about the suitability for cropland by analysing the statistical distribution of cell suitability values in the initial land-use map with a relative operating characteristic (ROC) [26,27]. Validation of

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quantity of land-use change is based on the idea that LandSHIFT translates country-level demands for agricultural commodities to the crop area needed for their production. Therefore, plausibility of the simulated quantity of land-use change is tested by comparing the calculated extent of cropland area for the year 2000 with country census data.

3.1.

Validation of location

For each cell of the initial land-use map the medium suitability for the 10 modelled crop types is calculated. Fig. 2 shows the frequency distribution for all non-cropland (n ¼ 18,582) and cropland cells (n ¼ 22,963). The histogram (Fig. 2A) shows that non-cropland cells tend to have lower suitability values (medium value ¼ 0.44) than cropland cells (medium value ¼ 0.67). For further validation we calculate the relative operating characteristics (ROC), which relates the proportions of classified predictions classified as correct and incorrect over the range of the defined suitability classes [27] (Fig. 2B). The performance measure is the area under the curve (AUC) calculated by trapezoidal approximation. If the suitability values were located randomly across the map, the expected value of the ROC would be 0.5 as indicated by the 1:1 line [26] meaning that the proportion of cropland cells in the different suitability classes would be more or less the same. The calculated AUC of 0.71 is significantly better than this random value, indicating that the cropland cells of the initial map can be found predominantly at locations with “high” suitability values.

3.2.

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Validation of quantity

The simulated cropland area for the year 2000 is 1.643  106 km2 compared to 1.697  106 km2 recorded in the FAO census data [23]. This means that the model underestimates the crop area by 3.1%, which can be judged as negligible in the context of this study. Moreover, LandSHIFT computes a sugarcane area of 45,661 km2, which is equivalent to an 8.8% overestimate (41,960 km2 [23]). This can be explained by the high spatial and temporal variability of crop yield levels and land-use intensities, which could not be simulated in detail.

4.

Scenario analysis

4.1.

Study setup

India has an area of 3.29  106 km2 and had a population of approximately 1.032  109 persons in the year 2000. In the same year about half of the area was used as cropland [23]. Closed forests accounted for 11% and other types of unmanaged lands (natural and barren lands) covered 24%. Sugarcane covered less than 3% of the total cropland (41,960 km2). The objectives of the model experiments that have been conducted for this study are twofold: First they illustrate the competition between the land-use activities settlement, food production and bioenergy production in India and second they assess the impact of an increasing demand for biofuels on the

Fig. 2 e Results of the partial model validation. (A) Histogram of frequency distribution and (B) the relative operating characteristic (ROC) curve.

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spatial land-use pattern. The socio-economic driving variables derived from the “Order from Strength” scenario of the Millennium Ecosystem Assessment (MEA) [20] are used as a reference case (baseline scenario) for the land-use simulations. The time horizon of our study is the year 2030 while the base year is 2000, which also marks the first time step of the simulation. To illustrate the consequences of expanded biofuel production on natural land, we calculate three scenarios with an additional demand for sugarcane production to substitute 5%, 10% and 20% of the projected petrol demand in 2030 by bioethanol (mandatory blending). All other scenario assumptions were kept constant. The production of food crops and yield increases due to technological change were calculated by the global agricultural trade model IMPACT [25] as part of the MEA. Fig. 3 shows the configuration of models and scenario drivers for our study. Table 1 gives an overview of the sources of input data for the LandSHIFT model.

4.2.

Scenario description

4.2.1.

Baseline scenario

The “Order from Strength” scenario describes a regionalized world with an emphasis on security and economic growth. Societies react to ecosystem problems only as they arise [28]. Indian population increases from 1.032  109 in 2000 to 1.54  109 in 2030. Average annual income (GDP/capita) increases from 463$ in the year 2000 to about 1200$ in the year 2030, based on the measure of purchasing power parity. At the same time, the production of important food crops increases significantly (e.g. 46% for maize and 40% for rice). This development is accompanied by increasing crop yields. For sugarcane, we assume a production increase from 296 Tg in 2000 up to 444 Tg in 2030, which is in proportion to the expected population growth, as current sugarcane production is used almost entirely for food products (sugar and sweeteners). The same assumption holds for ethanol produced from molasses, which currently is predominantly used for industrial purposes [1] and therefore is not directly available for biofuel production. In the scenario description only little societal concern for environmental problems is presumed. Consequently in our simulations only forest, as it is under strict conservation by the national forest policy [29], and land within the boundaries of existing conservation areas are protected. All other types of natural lands can potentially be converted to cropland or urban land.

4.2.2.

Biofuel scenarios

In the biofuel scenarios it is assumed that part of the petrol demand of the transport sector is substituted by domestically produced bioethanol. Currently, a mandatory blending of 5%

Table 2 e Energy demand for passenger transport in India 2001 and 2030 [3]. 2001 [PJ]

2030 [PJ]

266 193 59 443 954

2477 1782 546 1066 5897

Car Two-wheeler Auto-rickshah Bus Total

is established in 9 Indian states [1]. The substitution of diesel by biodiesel, which will also play an important role for India [4], is not considered by the scenarios. The projected Indian fuel demand in 2030 for road-based passenger mobility is derived from a study published by Singh [3]. Increasing wealth and personal mobility lead to an increase of fuel demand in this sector by 518% as shown in Table 2. Excluding the fuel demand of busses (which are driven by diesel engines) we come up with a total petrol demand of 4831 PJ in the year 2030. The energy scenarios represent three mandatory bioethanol blending levels of 5%, 10% and 20% for the whole country in the year 2030. We assumed an ethanol yield of 86 dm3 per Mg of sugarcane harvested [30]. From these data and with the energy content of ethanol being 21.3 MJ/dm3 [1], we calculate that the amount of bioethanol that can be produced from 1 Mg of sugarcane has a total energy content of 1.83 GJ. For scenario 1 (5% blending) this implies an additional production of sugarcane of 132 Tg, for scenario 2 (10% blending) 264 Tg and for scenario 3 (20% blending) 528 Tg. Table 3 outlines the scenario characteristics. In the simulations we assume a linear increase of sugarcane production for ethanol from 2000 up to the 2030 values.

4.3.

Simulation results

Results from the land-use-change simulations are time series of land-use maps at 5 0 resolution for each scenario. Fig. 4 shows the land-use pattern in the base year and in the year 2030 for the baseline scenario and the 20% blending scenario as examples for the most diverging end points. In order to illustrate the impact of the additional sugarcane production on the land-use pattern and the quantity of land-use change, the crop type information calculated by LandSHIFT has been aggregated into the two classes cropland (without sugarcane) and sugarcane. The land-use type pasture is kept constant during the simulation. Table 4 summarizes the area trends in 10-year time steps. Under the baseline scenario, we find high pressure on the available land resources. Due to the strong population growth

Fig. 3 e Configuration of scenarios and models used in the study.

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Table 3 e Energy demands and resulting additional sugarcane production for the different biofuel scenarios.

Scenario 1 Scenario 2 Scenario 3

Blending level [%]

Energy demand [PJ]

Sugarcane production [Tg]

5 10 20

241.6 483.2 966

132 264 528

the extent of settlement area increases by 62% resulting in the expansion of the Indian Mega-Cities, most noticeable in the northern parts of the country, and in a further increase of population density in rural areas. This expansion is to a great

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extent at the expense of the existing cropland area. Despite substantial yield increases, the growing food demand leads to an overall expansion of cropland (þ19.6%) as well as of land that is used for the cultivation of sugarcane (þ13.7%). One result of this development is a decline of natural land by almost 42%. Forests and nature conservation areas are not affected by these encroachment processes as the scenario assumes a strict conservation policy that prohibits their conversion to both cropland and settlement. The results of the model validation (Section 3) indicate that the converted natural land on average is characterized by environmental conditions that are less suitable or marginal for agriculture due to topographic, climate and soil constraints.

Fig. 4 e Results of the land-use change simulations. (A) Land-use pattern for the main part of India in the year 2000 and in the year 2030 (B) under the base scenario and (C) under the biofuel scenario with 20% mandatory blending of petrol with bioethanol produced from sugarcane.

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Table 4 e Summary of simulation results in 10-year time-steps. Sugarcane and cropland used for food production are listed separately. 2000 [km2]

2010 [km2]

2020 [km2]

2030 [km2]

MEA base scenario Settlement Cropland Sugarcane Natural land

186,226 1,597,420 45,660 803,909

225,658 1,672,800 47,329 749,196

256,857 1,821,190 50,104 573,136

302,113 1,910,510 51,916 458,229

62.0 19.6 13.7 42.9

5% ethanol blending Cropland Sugarcane Natural land

1,597,420 45,660 803,909

1,673,810 53,869 741,717

1,803,210 59,896 581,189

1,913,100 66,564 441,746

19.8 45.8 45.0

10% ethanol blending Cropland Sugarcane Natural land

1,597,420 45,660 803,909

1,674,480 60,013 735,059

1,802,080 69,657 572,690

1,913,700 81,660 427,724

19.8 78.8 46.8

20% ethanol blending Cropland Sugarcane Natural land

1,597,420 45,660 803,909

1,673,370 73,017 723,295

1,818,880 90,151 535,929

1,919,260 111,332 393,558

20.1 143.8 51.0

Affected regions include the border to the Great Indian Desert in Rajasthan, mountainous regions in the northern parts of India as well as hill ranges in the Western and Eastern Ghats. The biofuel scenarios successively introduce an increasing production of sugarcane to meet the respective petrol blending targets. Since the settlement area has the highest allocation priority, calculated changes are the same as in the baseline scenario. Forest and conservation areas also remain stable. In contrast, the area for sugarcane production increases by 46% (5% blending scenario), 79% (10% blending) and 144% (20% blending), respectively. Expansion again is at the expense of the extent of natural land, which decreases by 45%, 47% and 51% (Table 4). In contrast, only minor sideeffects on the extent of other food crops can be observed. Here, the area increase varies between þ19.6% (baseline) and 20.1% (20% blending). The overall spatial pattern of cropland expansion is very similar to the baseline scenario.

5.

Discussion

This study successfully demonstrates the application of a spatially explicit land-use model to assess the consequences of biofuel production on land-use change in India. The computed land-use patterns are the result of competition between the modelled activities and their spatial and temporal interactions. The scenario-based approach allows evaluating and comparing the effects of biofuel policies on country-wide land-use change. Based on the calculated grid maps it is now possible to identify regions within India which are likely to face major changes. This makes the presented methodology a useful tool to support policy and planning processes. Since LandSHIFT uses a global database, the concept of this study can be adapted to other world regions and other biofuel pathways. Nevertheless, the current spatial resolution of the applied model is relatively coarse. Therefore applications should focus on larger countries or world regions where they can be complemented by more detailed local studies. For India,

2000e2030 [%]

high resolution remote sensing data which is suitable to derive land-cover information is available from national sources (Indian Remote Sensing satellite program). An example of the use of this data for an analysis at the watershed-level is given by Semwal et al. [31]. Extensions of our work could include the role of 2nd generation biofuels [6,32] (e.g. the conversion of woody biomass to liquid fuels by Fischer-Tropsch synthesis). A more comprehensive case study for India should also investigate the potential of biodiesel production by jatropha, which along with the use of plant residues plays a prominent role for rural development programs [5,33]. Prerequisite for using the LandSHIFT model for scenario calculations is its capability to capture the land-use dynamics of the human-environment system and to compute reliable results. Questions of model validity have been addressed by evaluating the performance of the DayCent model [22] and the crop cultivation land-use activity, separately. As these two model components are linked to each other, the tests can be regarded as a first evaluation of the whole model system. A more comprehensive model evaluation should incorporate a detailed comparison of the output of the LUC-module against sub-national census data or against independent land-cover maps, where these are available. Also a systematic analysis of model sensitivity and uncertainty was beyond the scope of this paper. The analysis of the simulation results indicates that under the given scenarios two closely linked processes of land-use change are responsible for the decrease of natural land. First, there is an overall expansion of cropland area due to the increasing demand for crop production, which can only partly be compensated by the assumed yield increases. Under the biofuel scenarios, this trend is intensified by the additional sugarcane production. A second important process is the expansion of settlement area resulting in the displacement of existing cropland. These losses are also compensated by the conversion of natural land to cropland. Since natural land is on average characterized by less favourable conditions for agriculture, the expansion of cropland into these areas is associated

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with additional efforts and related costs for land preparation as well as for establishing transport and water infrastructure. These findings underline the need to analyse the dynamics of land-use change from an integrated perspective. Furthermore, the simulation results can provide valuable information for the evaluation of the sustainability of biofuel systems [34,35]. Important aspects include the consequences of changing land-use on biodiversity [36] and on greenhouse gas emissions related to land conversion and agricultural management, which can counteract potential benefits of substituting fossil fuels. For example, the conversion of unmanaged land to cropland can cause the release of carbon dioxide from soil organic matter and from burning of aboveground biomass [37] while the application of fertilizer as part of the agricultural management is a source of nitrous oxide emissions to the atmosphere [30]. It should be kept in mind that the calculated scenarios prohibit conversion of forest and therefore minimize the carbon debt of new sugarcane plantations. A systematic analysis of these effects could be part of more detailed studies. The primary aim of the applied land-use model is the computation of location and extent of land-use change based on local cell properties and neighbourhood relations. According to Heistermann [38], LandSHIFT can be classified as a geographic model. Economic variables are considered only indirectly by using the output from an economic trade model as driving variables. A major simplification of our study is that our model does not account for interdependencies between food and bioenergy markets. Here, we have developed a very rough solution by superimposing blending targets on the baseline scenario. As a result, possible effects of the additional biofuel demands on domestic food production and international trade are not considered. First efforts to simulate these market dependencies with economic models are described in [39,40]. In this context, more elaborated studies will also require the development of more consistent scenarios. Since scenarios are commonly constructed around internally consistent storylines, the introduction of additional policies such as the promotion of biofuel production has to fit into the underlying rationale of the scenario [41]. Moreover, the implementation of a more sophisticated combination of economic and geographic modeling concepts will help to improve the consistency of our study. On the one hand this can be achieved by feeding back geographically detailed information on yield changes and land availability to economic models that commonly operate on higher spatial aggregation level such as regions or countries [42]. This approach, for example, would allow informing the economic model about constraints and difficulties of land conversion. Based on this information, then the economic model can calculate the resulting effects on food and bioenergy markets. On the other hand, the land evaluation and allocation algorithms of LandSHIFT can be improved by integrating economic knowledge to simulate farmer’s decision making on crop and location choices more accurately.

6.

Conclusion

The study illustrates the utility of using a spatially explicit simulation model to investigate large-scale, long-term land-use

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change processes. In the simulations shown, a comprehensive linkage is made between driving variables (such as population change) and policies (such as biofuel usage) that could affect land-use change in India over the coming decades. Moreover, the simulations illuminate the consequences of an increasing demand for agricultural land (both for food and biofuel production) on the further depletion of natural resources in India. It becomes clear that the consideration of competition between land-use activities in the modeling exercise is vital to assess the environmental impact of biofuel production. Further research will address the implementation of a more detailed model of the Indian land-use system. This includes a further integration of economic and geographic modeling concepts as well as the refinement of the suitability assessment and allocation routines within LandSHIFT and the consideration of biodiesel production as an additional bioenergy pathway. Furthermore, methods for a more detailed assessment of environmental impacts at landscape level (i.e. biodiversity) will be integrated into the model framework.

Acknowledgements The study has been elaborated from our contributions to the Joint Indo-German Research Workshop “Will Competition for Land and Water Hinder Energy Development in India? e Recommendations for Research” held at the National Institute of Advanced Studies (NIAS) in Bangalore, India, in March 2006 [43]. The authors like to thank all the colleagues at NIAS and in particular Dilip Ahuja for their support and hospitality.

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