GISELA GIS-based Evaluation of Land-use and Agriculture Market Analysis and the Effects of Income Distribution

Transcription

1 GISELA GIS-based Evaluation of Land-use and Agriculture Market Analysis and te Effects of Income Distribution Sunsuke Mori Professor, Department of Industrial Administration, Faculty of Science and Tecnology, Tokyo University of Science Yamasaki 2641, Noda,Ciba , Japan Tel +81 (4) Fax +81 (4) Masairo Kato Accenture Co., Akasaka Minato-ku,Tokyo , Japan Tel: +81 (3) Fax: +81 (3) Takaumi Ido NEC Soft Ltd.., Sinkiba , Koto-ku, Tokyo , Japan Tel +81 (3) Masasi Okura Researc Associate, Department of Industrial Administration, Faculty of Science and Tecnology, Tokyo University of Science Yamasaki 2641, Noda,Ciba , Japan Tel +81 (4) Fax +81 (4) Daici Nalata Graduate student, Graduate Scool of Science and Tecnology, Tokyo University of Science Yamasaki 2641, Noda,Ciba , Japan Tel +81 (4) Fax +81 (4) Abstract One of te important future issues is ow agriculture production can meet te future demand increase due to te population and te income growt. Global warming would give bot positive and negative impacts on tem. Agriculture is often expected to supply biofuels to meet te growing transportation energy demand and te warming control policy. GISELA GIS based Evaluation for Land use and Agriculture production model - is developed to evaluate te current and te potential cropland for rice, weat, maize and soy-beans production under climate canges. We also assess te food and te feed demand based on te istorical regional statistics for world into 18 regions. Finally, we assess te future food market integrating te above supply and demand conditions developing a dynamic optimization model, GISELA. Current GISELA findings are as follows: (1) potential cropland in sout America will be extensively cultivated, (2) market price of weat and soy will gradually go up wile tat of maize is almost stable in medium yield case, and (3)in te low-yield case, all crop prices ike rapidly in te mid of tis century. We ten approac te unger issue focusing on te income distribution. Applying Gamma distribution to te income quintile statistics of World Bank, we estimate ow te income distribution pattern sifts along wit te economic growt. As a preliminarily results, altoug te population in absolute poverty would decrease, tere still remain people wo would not access te food market in some regions especially in case of low-yield scenario. Keywords: Global Warming, GIS, Land use, Food market model Presented at te IEW 29, 17-19, Venice, Italy

2 Introduction Recently, biofuel is expected to play a major role in bot energy security and global warming mitigation issues. In 27, United States promoted maize-based bio etanol production to mitigate te ig oil price as well as to support te agriculture industry. Te international crop prices ten went up rapidly. Te existing studies on te food supply security issue ave mainly focused on weter te future potential food production can meet te increasing demand under te population and te economic growt in developing regions. Dietary pattern canges according to te income growt sould ten be taken into account for assessing te demand-supply conditions in te international crop market. Altoug te food supply per capita as been improved in average wic could support all population in te world, owever, tere exist eigt undred million people suffering from unger. It is pointed out tat te global warming would cause water resource sortage and extreme climate events wic affect te future food production. Te above points suggest tat te food supply and demand issues sould be discussed compreensively from multiple points of views. In tis paper, we talk about two topics: firstly, we describe te overview of an optimization model to assess te long term food production potentials based on detailed production conditions using Geograpical Information Systems (GIS). Te impacts of climate canges are also taken into account. Second, in order to assess te effects of crop market on poor class, we focus on te canges of income distributions applying te Gamma distribution function to te Income Quintile Statistics of World Bank. 1. Background of tis Study Te possibility of food production wic meets te food demand under te population and te economic growt as been a central policy issue. We can list up a number of debates between te pessimistic and te optimistic views since te istorical Maltus argument. Wile Lester Brown [Brown(1995,25)] proposed a typical pessimistic view, IFPRI empasizes a possibility to overcome te unger in 22 VISION [IFPRI, 28] stressing an importance of world corporation and action. Mitcel [Mitcel, 1997] points out te production growt of te former central economy countries and concludes tat te world will feed te doubling population in te next two decades. Kawasima [Kawasima, 28] also concludes tat te food supply sortage is unlikely to occur because of te food demand saturation, te assessments of potential cropland including fallow land. Te assessment of te impacts of global warming is still controversial due to te uncertainties in bot te spatial distribution of climate cange and te possibility of adaptability. Te Fourt Assessment Report of Intergovernmental Panel of Climate Cange [IPCC, 27] points out te decrease of yields in te 1-2 Celsius degree atmosperic temperature rise in te low latitude area wile 1-3 Celsius degree atmosperic temperature rise in te middle latitude area may bot increase and decrease tem. Higer temperature rise would mostly decrease te yields. Besides te above middle-to-long term food potential production issues, recent market price fluctuation is also worried about. Figure 1 and Figure 2 exibit te international prices of major crops and te trends in te world crop supply and demand [MAFF, 28], respectively. Te world crop production as mostly followed te world demand wile te market prices ave eavily fluctuated. Needless to say, suc vulnerability of te world crop market affects te daily life of poor people seriously. Te above suggests tat te projection on te distribution of income and te assessment of future market possibilities are essential as well as te crop supply and demand possibility wen we talk about te poverty and te unger issue.

3 2,5. 2,. 1,5. 1,. 5. Production Consumption Inventory Figure 1 World crop production, demand and inventory in million ton [MAFF, 28] Weat Maize Soy Rice Figure 2 Trends in world crop prices in US dollars per ton [MAFF,28] Te second and te tird issues are related wit eac oter. Te investment for te expansion of cropland under te market uncertainty would decrease wile te marginal productivity of additional cropland would be decreasing. Suc lowered investment will make te agriculture system more vulnerable by te insufficient irrigation, fertilizer, storage system, etc. On te contrary, te augmented management and production capability given by te tecnological progress will strengten te market robustness wic provides stable agriculture policy. Biofuel will widely be utilized under te stable crop market. Wen we discuss te food demand-supply issue, it is needed to assess not only te potential supply capability but also te market conditions, especially te market pressure to raise te price. In tis study, we develop a model to assess te crop production and demand potentials under te market beavior model. Tis model provides a basis to assess te food distribution and biofuel potentials. 2. Outline of tis study Many of existing studies on potential cropland ave focused on te certain regions based on detailed data. Te long term assessment on te potential global food production under climate canges are provided by IIASA[IIASA,23], RITE[Akimoto, 26] were maximum potential cropland is focused on. IMAGE-2[Strengers, 21] provides te dynamic canges of crop land under climate canges were te market beavior is not formulated. Global food market as been analyzed by equilibrium models. IFPSIM developed by Oga [Oga,1998] is widely used to assess te food market, wile te assessments of potential cropland and land use cange are not

4 explicitly formulated. We develop a long term food supply and demand model considering te long term climate cange. In tis paper, te sort term price fluctuations affected by financial market and oter speculative dealings are not described. Te market vulnerability will be suggested by te equilibrium price canges and te required development of potential cropland. In tis paper, we consider four major crops, i.e., maize, rice, soy and weat. We apply Geograpical Information System (GIS) to analyze te relationsip between climate canges and te potential production by crop. Potential cropland by crop is estimated based on te detailed land use data given by USGS[USGS,26], soil map given by FAO[FAO,24], growing conditions on temperature and water availability. Reflecting te uncertainties of climate models, we compare te six Atmospere-Ocean General Circulation Model (AOGCM) studies and estimate te pessimistic yield case and optimistic yield case besides te base case. Te potential cropland area generated by te above gives te upper limit of te cultivation. We ten develop a dynamic optimization model to see te market beavior. Under te given world population and economic growt scenario, te model generates equilibrium market prices and demand by crop by maximizing te summation of discounted consumer s surplus. Feed demand is given exogenously. Te outline of tis study is sown in Figure 3. Te boxes wit bold line and italic caracters are te exogenous data and te boxes connected wit te market equilibrium model by bot-side arrow represent endogenous variables. Oter boxes are te estimated parameters. Land use data and climate cange simulations provide potential cropland by crop and region. Te demand functions of crops by region are given based on te Business-as-Usual (BAU) GDP and population scenario and statistically estimated parameters. We employed te IPCC-SRES-B2 scenario [IPCC,2] for te reference GDP and population scenario. Te model is named GISELA GIS-based Evaluation of Land use and Agriculture market. Land use data Soil data Climate grid data Crop growing conditions Cropland Potential cropland Net primary production (NPP) Scenario on GDP-PPP and population Utilized cropland by crop type Price and income elasticity Projected yields by region and crop Initial demand and supply Meat demand Market Equilibrium Model Feed demand by crop Cultivation of potential cropland World market price Crop production and demand Figure 3 Outline of GISELA model We ten focus on te modeling of te income distribution to see te food and poverty issue more quantitatively. Gamma distribution is employed to represent te income distribution based on te income quintile data. 3. Structure of GISELA model 3.1 Definition of region group

5 Te market equilibrium model block of GISELA aggregates te world into eigteen regions sown in Table 1 wile it deals wit USGS 1km grid data to assess te potential cropland. We assume te reference population and GDP scenario according to te IPCC-SRES-B2 scenario. Table 1 Eigteen regions of GISELA market model Code Region Code Region USA USA CAF Central Africa CAN Canada SAF Soutern Africa MCM Central America JPN Japan BRA Brazil CHN Cina, Hong-Kong, Taipei SAM Sout America SEA Sout-East Asia WEP Western Europa IND India EEP Eastern Europa TME Middle-East FSU Former USSR OCE Australia, NewZealand NAF Nortern Africa XAP Rest of te world 3.2 Estimation procedure of potential cropland Potential crop production is a product of te potential cropland and te yield. Te estimation procedure of te former is as follows: te current land use data is provided by USGS GLCC-Version2. We extracted te number of grids classified as maize, rice, soy and weat production area. We calculate te ratio of number of grids to te FAO cropland statistics in 2 as conversion coefficient. Next, we extract te pasture/grassland area, te tropical forest area and oter forest area. According to Tivy [Tivy, 199] and FAO Soil Map [FAO, 24], we select te possible soil grid for cropland conversion. We ten extract te potential grid based on te climate conditions. We assume te cumulative temperature and te cumulative precipitation sown in Table 2 following to Seino [Seino, 26] as growing condition. Table 2 Climate conditions for crop growing Cumulative temperature in crop Celsius degree Maize Rice Soy Weat Cumulative precipitation in mm We employ te long term climate scenario according to te six AOGCM simulation results sown in Table 3 provided by IPCC data distribution center [IPCC, 28]. Since te simulation results for SRES-B2 are not provided, we employ te case of SRES-A1B wic represents similar GHG emission in 21. Te atmosperic temperature rise in from te average value between of SRES-B2 is Celsius degree wile tat of SRES-A1B is Celsius degree [IPCC, 27b]. Terefore we assume sligtly iger climate canges.

6 Table 3 Six AOGCM simulations [IPCC, 28] Name Organization Spatial resolution (degree) CNRM-CM3 Centre National de Receres Meteorologiques 2.8 MIROC3.2(HI) National Institute for Environmental Studies 1.1 MIROC3.3(ME) National Institute for Environmental Studies 2.8 MPI-ECHAM5 Max-Plank-Institut for Meteorology 1.9 MRI-CGCM2.3.2 Metorological Researc Institute 2.8 NCAR-CCSM3. National Centre for Atmosperic Researc 1.4 We employ te MIROC3.2(HI) simulation results as te reference case because of te igest spatial resolution of tis model. Based on te above land use, soil and te climate data, we can estimate te potential cropland by crop and region. Table 4 sows te potential cropland in 25 according to te MIROC3.2(HI) results were te cropland in 2 is also sown. It sould be noted tat te summation of potential cropland by crop is not te potential cropland since tere are some grids wic are suitable for cultivation for plural crops. IIASA [IIASA,23] estimates te potential cropland to be 2,718 million a and Crosson et.al. [Crosson, 1992] report 1,392 million a as potential cropland in developing region wile Table 4 indicates tat te total potential cropland is 1,591 million a. Terefore our estimation is compatible wit existing studies. Altoug te potential cropland seems abundant, te conversion to cropland will be constrained since most of tem are in forest area and we do not consider te condition of slope ground. 3.3 Estimation of yields In tis study, we define te yields as te product of poto-syntesis efficiency and te progress of agricultural tecnology since oter factors suc as te CO2 fertilization effect [Allen,L.H. et.al. 26] and te ig temperature damage [Abrol,P. et.al., 1996] are controversial. Te former reports 1% increase of yields in doubled CO2 concentration wile te latter indicates 1% decrease of yields by 1. Celsius degree temperature rise. However, it is also pointed out tat tese numbers depend on te crop type, agriculture metod, etc. [IPCC, 27c] We employ te Cikugo model [Seino et.al., 1992] to assess te Net Primary Productivity (NPP) as te index of te potosyntesis efficiency based on te climate data sown in Table 2. We calculate te NPP values for te six AOGCM results in 25 and 299 sown in Table 3 and ten extract te lowest and te igest NPP values wic represent te range of uncertainties of yield by crop. Tese two cases, i.e., low-yield case and ig-yield case, and te mid-yield case by MIROC3.2(HI) as reference results are compared. We estimate te NPP canges interpolating te 25 and 299 values. It is often pointed out te tecnological progress in agriculture is a function of per capita GDP [Kawasima, 28]. We estimate te relationsip between istorical per capita GDP and yields aggregating te regions into some groups. Te estimated parameters are sown in Table 5. Table 4 Estimation of potential cropland and current cropland in 2 in million a Maize Forest Pasture Tropical forest total 2 Africa Oceania Nort America Sout America Eurasia World

7 Rice Forest Pasture Tropical forest total 2 Africa Oceania Nort America Sout America Eurasia World Soy Forest Pasture Tropical forest total 2 Africa Oceania Nort America Sout America Eurasia World Weat Forest Pasture Tropical forest total 2 Africa Oceania Nort America Sout America Eurasia World Total potential cropland Forest Pasture Tropical forest total Africa Oceania Nort America Sout America Eurasia World ,59.8

8 Table 5 Region grouping and te estimated parameters of yields Yield=A ln(gdp-ppp/cap)+b Crop Region group A B R^2 USA, CAN, SAM, WEP, FSU,NAF, OCE 2. (24.8) (17.8).76 Maize BRA, CAF, SAF, JPN, IND, SEA.37 (18.4) -1.2 (7.3).62 EEP, CHN.35 (3.4) 1.31 (1.7).19 SAM, OCE 2.76 (17.6) (13.).81 Rice USA, EEP, WEP, CAF, JPN, IND, SEA 1.25 (43.8) (27.2).88 NAF, CHN 1.46 (11.4) (4.9).71 SAF USA, CAN, BRA, SAM, CHN, OCE.296 (14.9) -.62 (3.6).52 Soy WEP EEP, FSU, CAF, SAF, JPN, IND, SEA 1.43 (16.8).27 (15.4) -1.9 (14.9) -1.4 (7.49) NAF.86 (3.12) (1.9).6 SAM, WEP 2.17 (33.5) -1.6 (27.7).97 CAF, IND.74 (16.6) (1.9).78 Weat EEP, CHN.64 (11.6) (3.54).64 USA, CAN, BRA, FSU, SAM, SEA, OCE.55 (15.4) (9.31).61 NAF, JPN (*) Values in te parentesis represent t-statistics..94 (27.2) -5.9 (19.8).91 Based on te growt rates on NPP and yields, we develop te scenario on yields. In Figure 4, we compare our results in 23 wit te production estimates given by FAO[FAO, 23] in 23 were our estimates are mostly compatible wit tose of FAO.

9 million ton 1 8 Maize Rice Wea 6 4 Soy 2 Hig-yield Mid-yield Low-yield FAO Hig-yield Mid-yield Low-yield FAO Hig-yield Mid-yield Low-yield FAO Hig-yield Mid-yield Low-yield FAO Figure 4 Comparison of potential crop production Oter adaptation options will be applicable under te climate cange. However, due to te lack of information, we ave not included suc possibilities at te moment. 3.4 Crop demand functions In our model, demand for crop consists of food demand and te feed demand wic is given exogenously extrapolating te istorical trends. We assume te food demand functions as follows: ereafter, suffix t, and i represent period, region and crop type, respectively. Te demand for crop i, D (t,i) is represented by D,i, i Y (t) / L (t) MDP (t,i ) (t,i) A L (t) (1),i were MDP, Y and L denote domestic market price of crop i, GDP and population respectively. Similar to te case of yield in te previous section, we aggregate te regions into some groups and estimate te parameters using regression analysis. Te results are summarized in Table 6 were one can see tat te statistically significant parameters are limited. However, since we employ te consumer s surplus as objective function and zero price elasticity seems to be unrealistic, we impose te lower bound of price elasticity to be.15 uniformly.

10 Table 6 Region grouping and te estimated parameters of crop demand,i ), D (t,i) A L (t) Y (t) / L (t) MDP (t,i,i i Crop Region group θ β R^2 Maize (all regions) Rice Soy Weat USA, CAN, WEP, EEP, FSU, OCE MCM, SAM, SAF, TME BRA, NAF, CAF -.55 (4.16).68 (2.92).5 (12.).34 (2.22) JPN CHN, IND, XAP SEA USA,MCM, XAP CAN, WEP, JPN, CHN, IND, SEA EEP, FSU, NAF,CAF, SAF, TME, OCE.26 (2.89).83 (6.15).62 (13.4).63 (8.21) (3.3).81 (6.51) SAM BRA CAN WEP OCE SAM USA, BRA, MCM, SAF, CAF, SEA EEP, FSU, NAF, TME CHN, IND, JPN, XAP (*) Values in te parentesis represent t-statistics. 4. Formulation of market equilibrium model.56 (6.92).5 (1.3).76 (3.94) -.11 (2.23).59 (2.93).11 (.68) GISELA market equilibrium model maximizes te discount sum of te consumer s and te producer s surplus under te supply-demand balance and market price conditions as well as te constraints on potential cropland developments. GISELA market model distinguises te market price as five categories by crop and region according to FAO statistics [FAO, 26], i.e., A:domestic production price(dp),b:export price (P_ex),C:import price (P_im), D:domestic market price (MDP) and E:international market price (DIP). DIP is te weigted mean value of export price of eac region and MDP is te weigted mean value of te supply of domestic products and import products. Tax and subsidy are imposed between DP and P_ex and between DIP and P_im. Te sare of eac region in te international market is formulated as a decreasing function of te ratio of P_ex to DIP. Te relationsips among te above five prices are sown in Figure 5.

11 Domestic production price (DP) Domestic market price (MDP) Weigted mean Tax and subsidy Export price (P_ex) Import price (P_im) Weigted mean Tax and subsidy Figure 5 Relationsips among price categories International market price (DIP) Market equilibrium Te cropland for crop i production at t in region, C (t,i), is represented by te newly cultivated potential cropland of category k (k=forest, tropical forest and pasture), N (t,k,i), and rotated cropland fromcrop j to crop i, R (t,j,i), * C (t,i) C (t 1,i) N (t,k,i) R(t, j,i) R(t,i, j) N (t, k, i) N (t, k) (2) i k j were N * (t,k) represents te total potential cropland by region and period as is sown in te fift block of Table 4. C (t,i) and yield Yld (t,i), described in section 3.3, give te supply balance equation among domestic production, trade Trd (t,i) and domestic supply S (t,i) distributed to te food and te feed demand, i.e. D (t,i) and NF (t,i), respectively. S (t,i) C (t,i) Yld (t,i) Trd (t,i) (3) S t,i D t,i NF t,i (t,i) (4) Trd (5) Te cost of te development of new cropland is one of te most uncertain factor in te land use investigations. In tis study, we assume a simple supply function. Let C (t,i) and t,c (t,i),i denote te cropland area and te marginal production price of crop i in region at period t, respectively. We assume tat te t,c (t,i),i increases as te new cropland is developed. Tat is, t,c (t,i),i) (,C (,i),i) f C (t,i) C (,i) (6) (,i were f(x) represent te additional marginal cost according to te conversion of land use. We simply assume tat f(x) increases towards 15% of te initial cost as C (t,i) approaces te upper limit. Tis assumption is similar to te assumption wit supply price elasticity model employed in IFPSIM. Te formulation and estimation problem in te land use cange are te remaining issue. Based on te demand and te supply functions one can calculate te equilibrium by maximizing te discount t, D t, i i denote te inverse function of equation (1). sum of te producer and te consumer surplus. Let MDP t, i t, D t, i i (7),, Ten te equilibrium is obtained by max. t (1 r) t i D ( t, i) C ( t, i) ( t,, i) d ( t,, i) d (8) were exogenous feed demand is excluded. Wen te supply fails to meet te demand, a price premium P * (t,i) is imposed on te international market price to

12 give te market equilibrium as * * DIP (t,i) DIP(t,i) P (t,i) (9) 5. Simulation results 5.1 Crop production and price canges In tis section, we sow some simulation results on GISELA model. Figure 6 and Figure 7 sow te world production profile of maize and rice in mid-yield case. Production increases accordingly to te demand cange. International market prices in te mid-yield case and te low-yield case are exibited in Figure 8 and Figure 9. In mid-yield case, te prices rise gradually. On te oter and, te market prices rise rapidly in te mid of tis century. Te premium price in Equation (9) is introduced in tis case. Te world food market will be vulnerable and affect te poor people. Te distribution issue will appear seriously. 12 Maize Production USA MCM CAN BRA 1 SAM WEP 8 EEP FSU million ton 6 4 NAF SAF CAF JPN 2 CHN IND SEA OCE TME XAP Figure 6 Maize production profile in mid-yield case 16 Rice Production USA CAN 14 MCM BRA 12 SAM WEP million ton EEP NAF SAF CHN FSU CAF JPN IND SEA TME OCE XAP Figure 7 Rice production profile in mid-yield case

13 International Market Price dollars per kg maize rice soy weat Figure 8 International market prices in mid-yield case in US dollars per kg International Market Price dollars per kg maize rice soy weat Figure 9 International market prices in low-yield case in US dollars per kg 5.2 Conversion of potential cropland Table 7 compares te ratio of converted area to te potential cropland by yield case. In te low-yield case, potential cropland is developed extensively as crop prices rise. It sould be noted tat potential cropland is converted rapidly in Sout America. In Middle Africa and oter region (XAP), te demand for te cropland conversion canges among cases due to te uncertainties in climate cange. It is suggested tat te food supply policy sould be set up carefully especially in tese developing regions. Cina sows ig development rates in all cases. It is because tat te potential cropland in Cina is not so large.

14 Table 7 Simulation results on te ratio of converted area to te potential cropland USA CAN MCM BRA SAM WEP 25.%.%.% 3.7% 33.7%.% 29.%.% 3.5% 55.3% 56.3%.% Mid-yield case Low-yield case Hig-yield case EEP FSU NAF CAF SAF JPN 25.%.%.%.% 2.7%.% %.%.% 26.1% 13.5%.% CHN IND SEA TME OCE XAP %.%.4%.%.%.% % 67.8% 53.1%.% 2.3% 17.1% USA CAN MCM BRA SAM WEP 25.%.%.% 18.5%.%.% 29.%.% 14.% 55.3% 56.3%.% EEP FSU NAF CAF SAF JPN 25.%.%.% 2.% 2.7%.% %.%.% 68.3% 62.3%.% CHN IND SEA TME OCE XAP %.% 1.9%.%.%.% % 67.8% 74.1%.% 3.7% 75.% USA CAN MCM BRA SAM WEP 25.%.%.% 38.9% 56.3%.% 29.%.%.% 55.3% 56.3%.% EEP FSU NAF CAF SAF JPN 25.%.%.%.%.4%.% 29.%.%.% 2.5% 1.9%.% CHN IND SEA TME OCE XAP %.%.%.%.%.% %.% 8.4%.% 2.1% 8.1% 6. Modeling of te income distribution 6.1 Te modeling of income distribution As is well known, te global warming would cause serious damages especially on te poor people wo would ave less adaptive options tan middle to ig income ones. Te ig crop price will also affect tem even if te total crop supply could meet te total food demand. Te assessments of income distribution are terefore needed to see ow te future global warming influence te society troug te food market canges. Gini coefficient is widely used to represent te level of economic equity. However, since it fails to deal wit te income level explicitly, it is not suitable to assess te number of people wo cannot access te food market. On te oter and, World Bank[World Bank, 25] provides te income distribution in absolute value as Income Quintile for around 2 countries. We apply te Gamma distribution to tis quintile data and ten find te relationsip between te average income and te parameter of Gamma distribution. Extrapolating te parameters based on te SRES scenario, we can estimate te future income distribution by region. Comparing te possible future crop prices given by GISELA wit te income distribution and population projection, we can assess te fraction of people wo are excluded from te food market. 6.2 Gamma distribution To begin wit, we ave to coose te distribution type. Since te data number is limited, distribution function wit one or two parameters is needed. Second, income distribution may not be symmetric. We employ te Gamma distribution wic meets tese two requirements as follows:

15 f ( x, x 1 α 1 β, ) x e (1) α ( ) 2 were te products of two parameters,, represents te mean value and does te variance. As is observed in te Figure 1, tis function wit different parameter draws completely different sape wit same mean value α=2;β=1.2 α=1.2;β=1 α=3;β= Figure 1 Example of Gamma distribution function sapes wit same mean values Based on te income quintile statistics by World Bank, we can obtain te parameters by country. Tables 8 represents te estimated parameters as well as te mean income, R2 and Gini coefficients for selected countries. Table 8 Estimated parameters of Gamma distribution, mean income, R2 and Gini coefficients Country α β Ave.Income R2 Gini coeff. Year Country α β Ave.Income R2 Gini coeff. Year Albania India Denmark Spain Japan Tajikistan Belgium Canada Sweden France Czec Republic Mongolia Slovak Republic Pakistan Norway Belgium Bosnia and Herzegovina Canada Uzbekistan Switzerland Hungary Sri Lanka Finland Banglades Belarus Burundi Ukraine Yemen, Rep Germany Latvia Slovenia Tajikistan Rwanda Switzerland Croatia Armenia Ukraine Poland Croatia Indonesia Austria Greece Bulgaria Ireland Austria Egypt, Arab Rep Etiopia Egypt, Arab Rep Hungary Lao PDR Mongolia Spain Romania Kyrgyz Republic Kyrgyz Republic Poland Belarus Australia Neterlands Algeria Slovenia Greece Russian Federation Israel Albania Bosnia and Herzegovina Pakistan Latvia Romania Lituania Banglades Ireland Bulgaria Estonia Lituania United Kingdom Kazakstan Italy

16 Country α β Ave.Income R2 Gini coeff. Year Country α β Ave.Income R2 Gini coeff. Year New Zealand Tailand Jordan Kenya Azerbaijan Singapore Nepal Iran, Islamic Rep Uzbekistan Uganda India Nicaragua Georgia Tailand Lao PDR Hong Kong, Cina Vietnam Turkey Vietnam Ecuador Estonia Nigeria Yemen, Rep Pilippines Armenia Uruguay e Jamaica Cameroon Iran, Islamic Rep Côte d'ivoire Portugal Bolivia Guinea Cina Jordan Uruguay e Mauritania Jamaica Malawi Uganda Israel Pilippines Indonesia Mexico Burkina Faso Costa Rica Morocco Rwanda Mozambique Cina Tunisia Guinea-Bissau Turkey Nepal Sierra Leone Mozambique Mali Dominican Republic Sri Lanka Gambia, Te Guinea Gambia, Te Trinidad and Tobago Madagascar Cambodia Burkina Faso Georgia Costa Rica Turkmenistan Venezuela, RB Gana Venezuela, RB United States Malaysia Senegal Guatemala Senegal Peru Country α β Ave.Income R2 Gini coeff. Year Dominican Republic Malawi Swaziland Mali Niger Nigeria Zambia Papua New Guinea Argentina Peru Argentina El Salvador Zambia El Salvador Honduras Mexico Cile Honduras Panama Colombia Panama Brazil Zimbabwe Cile Colombia Paraguay Sout Africa Paraguay Brazil Guatemala Bolivia Swaziland Central African Republic Sierra Leone Botswana Lesoto Namibia Figure 11-Figure 14 exibit te applied Gamma distribution in te cases of Lituania, Peru, Tailand and Zambia. Tese figures sow te variety of income distribution and te effectiveness of Gamma distribution.

17 Lituania $1/capita Figure 11 Example of te Gamma distribution (Lituania,2) :2.8 :11.6 R2:.97 Gini coeff Peru $1/Capita Figure 12 Example of te Gamma distribution (Peru 2) :.98 :21. R2:.95 Gini coeff Tailand $1/Capita Figure 13 Example of te Gamma distribution (Tailand 2) :1.28 :15.75 R2:.94 Gini Coeff. 34.7

18 .25.2 Zambia $1/Capita Figure 14 Example of te Gamma distribution (Zambia 1998) :.81 :4.1 R2:.93 Gini Coeff Te countries are aggregated into 18 regions like te GISELA aggregations in te previous capter. Te Gamma parameters are calculated based on te aggregated mean and variance. 6.3 Projection of Gamma parameters We are interested in ow te income distribution pattern canges wit te economic growt. Figure 15 exibit te relationsip between per capita income in PPP and te Gamma parameter. Aggregating 18 regions into two groups, we obtain te following regression results in Table 9. 1 β 1 1 Relationsip between β and ln(income) ln( Income) in PPP $1 BRA CAF CAN CHN EEP FSU IND JAN MCM NAF OCE SAF SAM SEA TME USA WEP XAP Figure 15 Relationsip between and per capita income in PPP Table 9 Estimated parameters of ln B A ln(y) Group-1 (in blue circle) Group-2 (in orange circle) B -.49 (.153 ) (1.856 ) A.982 ( 12.1 ).865 ( 27.5 ) R regions BRA,CHN,MCM,NAF,SA CAF,CAN,EEP,FSU,IND,J F,SAM,SEA,XAP PN,OCE,TME,USA,WEP Values in te parentesis represent t-statistics.

19 Te A values in Table 9 suggest tat in Group-1 does not cange as te per capita income increases wile tat in Group-2 does. Tis point represents te caracteristics of te istorical economic growt. On te contrary, no significant relationsip were found on. However, given te mean per capita income Y, te definition Y / can be applied. 6.4 Projection on income distribution Based on te future scenario of GDP and population, we can give te income distribution parameters. Figure 16 sows te canges in absolute poor population wose annual income is less tan $365. Reflecting te global economic growt, number of absolute poor people decreases. However, te fraction of tose people sown in Figure 17 suggests tat te decreasing patterns are different among regions. Population in tousand Population wit Annual Income less tan $365 (min) SAM NAF SAF CHN MCM BRA SEA XAP OCE TME IND JPN CAF FSU WEP EEP CAN USA Figure 16 Profile on te absolute poor population (annual income less tan $365) 3% 25% 2% 15% 1% 5% % Fraction of People wit Annual Income less tan $365 (min) USA CAN EEP WEP FSU CAF JPN IND TME OCE XAP SEA BRA MCM CHN SAF NAF SAM World Figure 17 Fraction of te absolute poor population by region (annual income less tan $365) 6.5 Assessment of food accessibility a preliminarily study GISELA generates domestic market prices of crops. Assuming te minimum food requirement of crop to be 28kcal/day, we can calculate te minimum expenditure for food. Needless to say, since tis value does not cover te need for protein and oter nutrition, it is no more tan a preliminarily index. However, it will still suggest ow

20 te population wic is ard to access te food market canges according to te economic growt. Wile tis expenditure is less tan 5% of te average income even in te lowest income region and in te minimum yields case, tere are still poor people wose income is less tan te above minimum expenditure. Figure 18 and Figure 19 sow te profiles of population of tose wo cannot afford te minimum crop requirement and te fraction of tese people by region in te minimum yield case of weat, respectively. Figure 2 and Figure 21 are tose in average yield case. Population in tousand Population of tose wo cannot afford te minimum Weat (min) SAM NAF SAF CHN MCM BRA SEA XAP OCE TME IND JPN CAF FSU WEP EEP CAN USA Figure 18 Population of tose wo cannot afford te minimum weat requirement in minimum yield case 9.% 8.% 7.% 6.% 5.% 4.% 3.% 2.% 1.%.% Fraction of tose wo cannot afford te minimum Weat (min) USA CAN EEP WEP FSU CAF JPN IND TME OCE XAP SEA BRA MCM CHN SAF NAF SAM World Figure 19 Fraction of tose wo cannot afford te minimum weat requirement in minimum yield case

21 Population in tousand Population of tose wo cannot afford te minimum Weat (ave.) SAM NAF SAF CHN MCM BRA SEA XAP OCE TME IND JPN CAF FSU WEP EEP CAN USA Figure 2 Population of tose wo cannot afford te minimum weat requirement in average yield case 8.% 7.% 6.% 5.% 4.% 3.% 2.% 1.%.% Fraction of tose wo cannot afford te minimum Weat. (ave) USA CAN EEP WEP FSU CAF JPN IND TME OCE XAP SEA BRA MCM CHN SAF NAF SAM World Figure 21 Fraction of tose wo cannot afford te minimum weat requirement in average yield case 5 Population of tose wo cannot afford te minimum Weat (World) 45 Population in tousand Max yield Ave. yield Min. Yield Figure 22 Comparison of population of tose wo cannot afford te minimum weat requirement

22 Tese figures suggest tat altoug te number of poor people wo are ard to afford te minimum requirement food is decreasing, its declining ratio may not be so large and remaining population is far from negligible small even in te middle of tis century. 7. Conclusion We describe te development of GISELA model wic integrates te potential supply and te market beavior and its simulation results. In all cases, te potential cropland is converted in Sout America region. In mid-yield case, crop prices ike gradually and tus te food market will relatively stable. On te contrary, owever, in te low-yield case, potential cropland as to be rapidly converted in Sout-east Asia, Africa and oters. International market crop prices rise rapidly in te mid of tis century. Price premium is introduced in te second alf of tis century. Tis suggests tat te food market becomes more vulnerable and poor people would suffer from te ig price. Income distribution is modeled based on te quintile statistics provided by World Bank applying Gamma distribution. Te estimation results represent te variety of distribution patterns and enable us to assess te future distribution. We ten estimate te number of absolute poor people and assess te food market accessibility. Altoug tis procedure is no more tan a preliminarily study, it still suggests tat te poverty and unger problem may still remain in te middle of tis century even if total crop production would meet te world food demand. Poverty and unger are te central issues in te societal sustainability. It is concluded tat tis study provides an evaluation system to discuss tem quantitatively. Te assessment of te impact of bio fuel production and te adaptation possibility is te future subject of tis study. REFERENCES Akimoto,K, Sano,F., Mori,S. and Tomoda, T.(26), Evaluation of Global lmpact on Agriculture under Several GHG Emission Scenarios (in Japanese), Proc. of te 22 nd Conference on Energy, Economy and Environment, Tokyo, Japan, Jan., 26 Allen,L.H, Barker, J.T and Boot,K.J.(1996), Te CO2 Fertilization Effect, Global Climate Cange and Agricultural Production) Bazzaz,F. and Sombroek,W. eds.), FAO, Jon Wiley & Sonsa, NY, USA, 1996 Abrol,P. and Ingram,K.T.(1996), Effects of Higer Day and Nigt Temperatures on Growt and Yields of Some Crop Plants, Global Climate Cange and Agricultural Production) Bazzaz,F. and Sombroek,W. eds.), FAO, Jon Wiley & Sonsa, NY, USA,1996 Crosson,P and Anderson,J.R (1992), "Resources and Global Food Prospects, Supply and Demand for Cereals to 23", World Bank Tecnical Paper 184, 1992 FAO(24), "Te Digital Soil Map of te World (CD-ROM), FAO, 24 FAO(23), World agriculture: towards 215/23, FAO, UN, 23 FAO(26), FAOSTAT, ttp://faostat.fao.org (26/1/16) IFPRI(28), "REACHING SUSTAINABLE FOOD SECURITY FOR ALL BY 22", ttp:// actionlong.pdf (28/8/12) IIASA(23), "Global Agro-ecological Assessment for Agriculture in te 21st Century", IIASA, Laxenburg, Austria, 23 IPCC(27a),IPCC Fourt Assessment Report, Working Group II Report "Impacts, Adaptation and Vulnerability", Capter 5, Cambridge University Press, UK, 27

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