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Environ. Sci. Technol. 2010, 44, 2204–2209

System Approach for Evaluating the Potential Yield and Plantation of Jatropha curcas L. on a Global Scale Z H E N G G U O L I , †,‡ B I N - L E L I N , * ,† XIAOFENG ZHAO,§ MASAYUKI SAGISAKA,† AND RYOSUKE SHIBAZAKI| Research Institute of Science for Safety and Sustainability (RISS), National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba 305-8569, Japan, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Key Laboratory of Resources Remote Sensing & Digital Agriculture, Ministry of Agriculture, Beijing 100081, China, Institute of Urban Environment, Chinese of Academy of Sciences (CAS), Xiamen 361021, China, and Center for Spatial Information Science, University of Tokyo, Tokyo 153-8505, Japan

Received October 29, 2009. Revised manuscript received January 4, 2010. Accepted January 25, 2010.

Many Jatropha curcas Linnaeus (JCL) plantations have been established in tropical and subtropical regions worldwide. To assess the potential of JCL for biofuel production, the potential areas for JCL plantations, and the yields of JCL must be estimated as accurately as possible. Here, we present a system approach to estimate JCL yields, classify yield levels, and estimate productivity of future JCL plantations. We used a process-based net primary productivity (NPP) model to estimate potential JCL yields. The model estimated that the potential yield of JCL dry seed will vary from 0 to 7.62 ton ha-1 y-1, in contrast to estimates of 1.50-7.80 ton ha-1 y-1 from previous assessments. We formulated a zoning scheme that takes into account land cover status and potential yield levels. This scheme was used to evaluate the potential area and production of future plantations at the global, regional, and national levels. The estimated potential area of JCL plantations is 59-1486 million hectares worldwide, and the potential production is 56-3613 million ton dry seed y-1. This study provides scientific information on global patterns of potential plantation areas and yields, which can be used to support bioenergy policy makers to plan commercial-scale JCL plantations.

1. Introduction As an alternative energy source, biomass is expected to ease fuel shortages and reduce global warming potential (1). However, biofuel production from conventional crops is becoming more controversial, with growing awareness of possible negative impacts on climate change, loss of biodiversity, and food security (2-4). As a renewable energy crop, Jatropha curcas Linnaeus (JCL) has several advantages; it * Corresponding author phone: +81-29-861-8844; fax: +81-29861-8904; e-mail: [email protected]. † National Institute of Advanced Industrial Science and Technology. ‡ Chinese Academy of Agricultural Sciences. § Chinese of Academy of Sciences. | University of Tokyo. 2204

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has a high oil content (5-7), is drought tolerant (8), requires only low levels of nutrients (9), is highly adaptable to marginal lands (9-11), and emits very low levels of greenhouse gases (GHG) (12, 13). For these reasons, this plant species is a potential alternative to conventional crops (7). In recent years, large-scale investments in JCL plantations have been made throughout tropical areas, particularly in the countries of Sub-Saharan Africa, Latin America, and South and SouthEastern Asia (14, 15). However, there are still potential risks from unsustainable plantations in these developing countries, such as competing for water and land with food crops or high carbon stocks (16, 17) and unexpected pest and disease infestations resulting from cultivation of monocultures (18). Biofuel production requires a sustainable supply of biomass, which should have minimal impacts on food security and biodiversity (19, 20). For policy makers to plan JCL plantations on a commercial scale, scientific data about suitable locations is required. From an economic perspective, marginal lands with low yield are not the first choice for JCL investors (18). From a food security and biodiversity perspective, JCL plantations should not displace food crops and forests (19, 20). Therefore, both land cover status and potential yield are two critical factors that should be considered when planning future plantations. The objective of this study was to provide scientific information on the potential area and yield of JCL plantations that can be used by policy makers and planners. We developed a system approach to estimate JCL yields, classify yield levels, and evaluate the potential of future plantations.

2. Methods 2.1. Overview of System Approach. The approach is composed of three modules (Figure 1). On the basis of the BiomeBGC model, a modified net primary productivity (NPP) calculation is used to estimate the biological productivity and potential dry seed yield of JCL. The estimated JCL yields are further classified into five levels according to the average yields under different levels of water and nutrient supply. To identify the spatial location of future plantations, yield levels are overlaid with global land cover types (Figure S1, Supporting Information) to formulate a zoning scheme. Finally, the potential area and production in each zone are summarized at the global, regional, and national levels. The database required for each of the above analyses was established using the Geographic Information System software ArcGIS 9.2 produced by the Environmental Systems Research Institute (ESRI). A detailed description of data preparation, growth requirements of JCL, the Biome-BGC model, and NPP simulation is given in the Supporting Information. 2.2. JCL Yield Estimation. The yield of JCL is a direct result of cultivation practices. This has substantial implications for future plantations (16). As an undomesticated plant, the JCL yield varies widely (0.4-12 ton ha-1). Francis et al. (9) showed that JCL grows well in a wide range of conditions. There is little information on the genetics, input responsiveness, and agronomy of JCL (7). Therefore, most estimations of JCL yield are site specific and not accurate for large-scale applications. To estimate the potential yield of JCL at a global level, Jongschaap et al. (21) proposed a concept based on the physiology of the plant. This concept was used to modify a process-based LPJmL model taking into account the biophysiological conditions of JCL (22). The model simulated regional JCL yield and was shown to be reliable. Hence, we adopted this model to estimate global-scale JCL yield in this study. 10.1021/es903004f

 2010 American Chemical Society

Published on Web 02/08/2010

FIGURE 1. General structure of the system approach. 2.2.1. NPP Simulation for JCL. Of the many NPP models, the Biome-BGC model was selected for NPP simulation of JCL. Because the Biome-BGC model only accounts for natural vegetation types or biome classes (23), JCL was approximated in the model using a modified framework from the closest vegetation type, commonly cited as perennial deciduous shrub (7, 24). Several recent studies have described growth conditions for JCL (10, 11, 21, 25), and these were reviewed and integrated into the model to estimate yield (Table S1, Supporting Information). In addition, the previous biophysiological parameters in the Biome-BGC model were further adjusted based on the specific reference parameters for deciduous drought-resistant shrubs (22) (Table S2, Supporting Information). 2.2.2. Determination of Dry Seed Yield. The dry seed yield is a primary consideration for energy crop potential (7, 21). Since simulated NPP values only provide information on the ability of JCL to fix carbon, the simulated NPP must be converted to dry seed yield. First, the annual net accumulation of JCL dry matter is calculated from NPP, assuming an average carbon content of 47.5% in the plant material (26); second, the harvested parts (fruits) of JCL are roughly calculated as 20% of dry matter (22); third, the proportion of dry seed in the harvested fruits is estimated to be 72% (21, 24). 2.3. Classification for Yield Level. The spatiotemporal heterogeneity of JCL growth must be taken into account to accurately predict the yields of future plantations. Yield levels were estimated taking water supply and soil fertility into account (10) (Table S3, Supporting Information). The range of JCL dry seed yields was then divided into five levels: Level 1, 0.25-0.75 ton ha-1; Level 2, 0.75-1.5 ton ha-1; Level 3, 1.5-2.5 ton ha-1; Level 4, 2.5-3.5 ton ha-1; and Level 5, >3.5 ton ha-1. Dry seed yields lower than 0.25 ton ha-1 were not considered in this study. 2.4. Zoning for Future Plantations. The spatial distribution of JCL indicates the most suitable areas for a JCL plantation based on climatic conditions. Collectively, these areas represent the so-called “JCL belt”, which ranges from latitudes 35° S to 30° N (10). However, this belt is only a rough approximation of areas that have potential for a JCL plantation. Further research and analyses of factors that could limit growth (27) and land cover status (28) are required to determine whether each area is suitable for a JCL plantation. To identify suitable plantation areas in this belt and predict yields more accurately, we developed a zoning scheme based

on the current land cover status and potential yield levels (Table S4, Supporting Information). 2.4.1. Unsuitable Areas for JCL Cultivation. Areas that are unsuitable for JCL plantations should be identified at an early stage to ensure sustainable JCL-based biofuel development. Three types of areas are unsuitable: (1) barren areas, in which the low yields make production of JCL uneconomic (9, 24); (2) all natural forests in the JCL belt (deciduous and evergreen broadleaf forest) to preserve biodiversity and sequester carbon (2, 4); and (3) high-yielding agricultural lands (cropland and grassland) to ensure security of food supply (19, 29). 2.4.2. Identifying Potential Areas for Future JCL Plantations. Four zones were defined as potential areas for JCL plantation. First, lands with the lowest yield range (0.25-0.75 ton dry seed ha-1) were identified as low-yield plantation zones (LYPZ). These marginal lands represent areas in which a JCL plantation would have a minimal impact on the environment. Second, lands with a potential yield range of 0.75-2.5 ton dry seed ha-1 were defined as medium-yield plantation zones (MYPZ). These areas represent a trade off between mitigation of environmental impacts and increased JCL production. Third, areas with high potential yields (>2.5 ton dry seed ha-1), such as shrublands and savannah areas, were defined as high-yield plantation zones (HYPZ), as the yields of JCL are expected to be high in such areas. Finally, low-yielding agricultural lands (0.25-1.5 ton dry seed ha-1) were defined as tentative plantation zones (TPZ) for JCL.

3. Results and Discussion 3.1. Predicted Yield of JCL. 3.1.1. NPP Simulation of JCL. The simulation predicted that the global mean NPP value of JCL in the JCL belt was 0.61 kg C m-2 y-1, consistent with a previous simulated value of 0.58 kg C m-2 y-1 (22). As shown in Figure S2 (Supporting Information), simulated NPP values in JCL belt range from 0 to 1.31 kg C m-2 y-1. Tropical evergreen broadleaf forest showed the highest simulated NPP value (>0.80 kg C m-2 y-1), followed by mixed forest (0.60-0.80 kg C m-2 y-1), shrubland (0.20-0.40 kg C m-2 y-1), and wooded savannah and savannah areas (3.5 ton ha-1. (Lands with potential dry seed yield lower than 0.25 ton ha-1 are not considered in this study).

FIGURE 2. Estimated JCL dry seed yield depending on the latitude (A), annual mean air temperature (B), and annual mean precipitation (C) in the JCL belt. pattern (Figure S3, Supporting Information). The dry seed yields range from 0 (desert) to 7.62 (tropical rainforest) ton ha-1. These values are consistent with estimates from a previous model (1.50-7.80 ton dry seed ha-1) (21). Furthermore, the mean values of simulated rain-fed yields in Brazil (3.56 ton dry seed ha-1) and India (2.11 ton dry seed ha-1) are consistent with those from a previous simulation (3.77 and 2.20 ton ha-1 in Brazil and India, respectively) by Lapola et al. (22). To validate estimated JCL seed yields, we compared these values with reported data from selected countries or regions (7). Although the estimated values differ from reported measured values, the differences were not significant. 3.1.3. Analysis of Factors Influencing JCL Yield. The growth and yield of JCL can be affected by many factors, such as climatic conditions, water availability, soil conditions, plant age, and pests and diseases (30-32). The JCL yield is generally correlated with latitude at regional or global levels (Figure 2A). The highest yields of JCL could be expected in tropical latitudes. However, JCL yields in the northern JCL belt would be significantly lower than those from similar latitudes in the southern JCL belt. In addition, variations in JCL yield may reflect the different climatic conditions in the Southern Hemisphere (SH) compared to the Northern Hemisphere (NH). For example, the warm climate of areas that are surrounded by oceans could result in high potential JCL yields. Figure 2B shows dry seed yield plotted against annual 2206

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mean temperature, and Figure 2C shows dry seed yield plotted against annual mean precipitation. These figures show that higher dry seed yields are associated with tropical and temperate climates (annual mean temperature of 18-25 °C) without dry seasons and with hot summers. This is consistent with the results of previous studies, which reported that yields of 5 ton dry seed ha-1 could be obtained from areas with annual mean precipitation of 900-1200 mm (27, 33). However, some tropical and subtropical semi-arid regions (e.g., subtropical deserts of Africa and semiarid and arid regions of Asia and Australia) are expected to give lower dry seed yields (2-3 ton ha-1), which are consistent with field observations (9, 32). Since the potential productivity of rain-fed plantations in arid or semi-arid regions is low, additional irrigation is necessary to increase JCL yield. There are still some limitations in validating the model and its estimated results. For example, there is little information on appropriate growth conditions of JCL, such as optimal climate/soil conditions, and such variables can affect the accuracy of the results. Consequently, the JCL yield estimation is based on approximate criteria as a general guide rather than on specific parameters for JCL. Moreover, the parameters for open shrublands (perennial deciduous shrub) were used for the NPP simulation; however, these parameters cannot completely represent the demands and limitations in real growth situations and, therefore, can introduce bias into the results. In addition, these criteria may not cover all the unknown factors that influence JCL yield. Some additional environmental factors may be added to the model to enable more accurate global-scale yield estimation. For example, the model could include climate change factors such as precipitation oscillation and redistribution and local factors such as field management, technological development, and genetic variability. 3.2. Yield Levels of JCL Dry Seed. Areas in which the potential yields of JCL are low (levels 1-2, 0.25-1.5 ton dry seed ha-1) include North Africa, the Middle East, and North Oceania. These areas account for more than 30% of the total land area in the JCL belt as shown in Figure S4, Supporting Information. Areas in which the potential yields of JCL are moderate (level 3, 1.5-2.5 ton dry seed ha-1) include South America, South and East Africa, East and Southwest Oceania, and South Asia. These areas account for approximately 20% of the belt land area. JCL crops with high potential yield levels (levels 4 and 5, 0.25-1.5 ton dry seed ha-1) could be grown in various parts of Central/South America, West Africa, and Southeastern Asia, which account for over 40% of the

116.2 45.9 38.4 34.9 27.4 20.5 19.3 18.4 16.9 15.9 374.8 145.1 127.3 112.3 87.0 65.4 63.0 60.4 54.4 53.0 Brazil Angola Indonesia Tanzania Zaire Ethiopia Madagascar Colombia Thailand Venezuela 214.5 147.7 116.9 93.9 66.4 74.9 67.6 65.4 58.9 61.2 520.2 326.0 254.4 218.5 174.2 171.9 168.7 167.5 154.3 139.5 national level (top 10 countries) 74.5 Brazil 38.6 Australia 40.8 India 26.9 Argentina 18.2 Zaire 17.0 Mexico 17.4 China 06.9 Zambia 8.5 Angola 7.4 South Africa 74.9 43.7 42.1 27.1 21.7 18.0 10.8 7.8 7.4 5.3 Australia Brazil Mexico India Namibia Argentina China Bolivia Peru Chile 13.6 7.9 6.3 5.1 4.7 2.4 2.3 1.9 1.9 1.7 India Peru Mexico Australia China Namibia Nepal Brazil Bolivia United States

12.4 8.4 6.1 6.4 7.7 1.9 1.8 1.5 1.6 1.5

279.9 177.9 113.5 25.3 7.5 904.8 575.5 368.6 83.3 23.5 Africa South America Asia North America Oceania 559.1 411.3 268.3 149.2 98.2 83.5 75.1 52.6 47.5 38.5 South America Oceania Asia North America Africa 21.9 15.3 8.1 5.9 5.1 Asia South America North America Africa Oceania

23.6 15.3 7.6 5.5 6.5

604.2 1955.7 in total 1486.1 3613.1

1409.3 99.8.2 645.5 329.6 230.5 level Africa South America Asia Oceania North America regional 79.6 74.8 64.3 45.9 34.0

global level 298.5 in total 297.3 in total 58.6 56.3

order order area (104 km2) order

in total

area (104 km2) production (106 ton y-1) order

HYPZ

area (104 km2) production (106 ton y-1)

MYPZ

production (106 ton y-1) production (106 ton y-1)

area (104 km2) LYPZ TPZ

TABLE 1. Potential Area and Dry Seed Production of Each Zone at the Global, Regional, and National Levels

JCL belt land area. Areas with low potential yields (200 million ton dry seed y-1). Plantations in HYPZ are expected to have the highest JCL yields (∼3.2 ton dry seed ha-1). This equates to a potential production of 1960 million ton dry seed per year (Figure 4D), which is lower than that from the MYPZ because of the smaller potential plantation area (∼600 million hectares). Sustainable JCL-based biofuel programs should be developed for countries in the JCL belt. Such programs should take into account the potential production and future energy demands in each region. China, Brazil, and India have rapidly growing economies and are facing similar energy shortages. These countries have each launched their own national biodiesel programs. For example, the Indian government aims to increase biodiesel production to 500% of current levels by 2015 (36), Brazil is aiming at 310%, and China is aiming at 300% (22, 37). For oil production calculations, JCL seed oil content is assumed to be 34% and extraction efficiency 75%, and seeds are estimated to be 94% dry matter (21). As a result, additional JCL production is forecasted to be approximately 10, 11, and 47 million tons of dry seed in China, Brazil, and India, respectively. Since production will vary depending on the location, these production targets cannot be achieved solely from plantations in the TPZ. Especially in India, these goals cannot be reached without allocating the entire LPYZ to JCL plantations and without irrigation. In Brazil, sugar cane and soybean plantations dominate the TPZ (22). In these countries, when the potential production of JCL is compared with future biodiesel demand, it becomes clear that these JCL-based biodiesel targets will be difficult to achieve if plantations are restricted to lowproductivity areas. In conclusion, we used a system approach to identify potential areas for JCL plantation and to estimate productivity of JCL crops. The approach takes into account land cover status and potential yield. JCL plantations in the TPZ or LYPZ may positively affect the local environment as it could utilize marginal agricultural land or benefit the ecological restoration 2208

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of degraded soil. However, plantations in such areas will have low productivity. In contrast, plantations in the MYPZ and HYPZ can be expected to have higher yields, but there will be a substantial ecological impact if large-scale natural shrublands or savannah areas are displaced by JCL plantations. In each zone, the trade offs between economic production and ecological conservation should be taken into account in developing programs for future JCL-based biofuel production.

Acknowledgments The research was conducted under the Program of Comprehensive Promotion and Evaluation Study, Development of Preparatory Basic Bioenergy Technologies, with the funding of the New Energy and Industrial Technology Development Organization (NEDO) of Japan. Special thanks to Tritib Suramaythangkoor (National Institute of Advanced Industrial Science and Technology, Japan), Wenbing Wu, and Yang Peng (Chinese Academy of Agricultural Sciences, China) for their comments and kind suggestions. We also thank three anonymous reviewers who provided helpful comments on this manuscript.

Supporting Information Available Detailed descriptions on data sources and preparation, growth requirements of JCL, Biome-BGC model, NPP simulation, and relevant supplementary figures and tables. This material is available free of charge via the Internet at http:// pubs.acs.org.

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