Brazilian Land Use Model and Application for Ethanol Impacts


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Presentation of Laura Barcellos Antoniazzi for the “Workshop on the Impact of New Technologies on the Sustainability of the Sugarcane/Bioethanol Production Cycle”

Apresentação de Marcos Laura Barcellos Antoniazzi realizada no “Workshop on the Impact of New Technologies on the Sustainability of the Sugarcane/Bioethanol Production Cycle”

Date / Data : May 14 - 15th 2009/
14 e 15 de maio de 2009
Place / Local: ABTLuS, Campinas, Brazil
Event Website / Website do evento:

Published in: Technology, Business
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Brazilian Land Use Model and Application for Ethanol Impacts

  1. 1. ICONE Brazilian Land Use Modeland its application for ethanol impacts Laura Barcellos Antoniazzi Researcher, ICONE ( Sao Paulo, May 15th 2009
  2. 2. Outline1. Introduction.2. General structure of the land use model  Diagrams explaining the information flows, connections among variables and effect-cause relations.  Assumptions.3. Summarized results.  Historic data.  Supply and demand projections for Brazil.  Area allocation at regional level.  Improvements under development 3. Applications 2
  3. 3. Introduction There are many concerns worldwide about the social and environmental impacts of biofuels productions and expansion. Land Use Change (LUC), as well as Indirect Land Use Change (ILUC), are now being taking into account in many policies aiming to promote biofuels. ICONE started to work on an Agriculture Projection Model in the beginning of 2008 in a joint effort with the Food and Agricultural Policy Research Institute (FAPRI), from Iowa State University, which is part of the Center for Agriculture and Rural Development (CARD). Although the Brazilian model follows the same structure of the FAPRI Models, it was adapted to the specific conditions and situations of the Brazilian agricultural sector. The Model aims to capture the Brazilian land use dynamics observed in the past and to forecast new dynamics 3
  4. 4. Agricultural Land use in Brazil (Agricultural census)P = PreliminarySource: IBGE
  5. 5. General structure of the land use model The model comprises two general sections: supply and demand and land allocation. Supply and demand, for a given year, is calculated at a national level. Supply includes production and initial stocks (in the case of crops), and demand includes domestic consumption, net trade and final stocks. Supply (regional) = Demands (national) Land allocation calculations are integrated to the supply side. Area is calculated at a regional level, as a function of the expected market profitability of the product and of the competing products. The amount of land allocated for a given crop, in a given region, depends on the response to expected market profitability, which means that regions with higher market returns will have higher planted area. Allocation of land across regions Brazilian production national supply The calculation of the expected planted area is also calibrated with the lagged area (area of the previous year) in order to avoid strong oscillations in the planted area. Confidential. Do not quote or cite unless authorized. 5
  6. 6. General structure of the land use model Planted forests are also included in the area allocation section of the model. For the version we are using now, projections of land allocation for planted forest are exogenous. The model comprises 6 macro-regions in Brazil:  Region 1: South (States of Rio Grande do Sul, Santa Catarina e Parana);  Region 2: Southeast (States of Sao Paulo, Minas Gerais, Espirito Santo e Rio de Janeiro);  Region 3: Center-West Cerrado (States of Goias and Mato Grosso do Sul and the Cerrado area in Mato Grosso);  Region 4: Amazon North (States of Amazonas, Para, Amapa, Acre, Rondonia, Roraima and the Amazon area in Mato Grosso);  Region 5: Coastal Northeast (States of Ceara, Alagoas, Sergipe, Pernambuco, Rio Grande do Norte and Paraiba);  Region 6: Mapito e Bahia (States of Maranhao, Piaui, Tocantins e Bahia). The regions are independent in the model in the sense that land allocation equations for each crop are different among regions. However, given that the total production must be equal to the demand, if a given crop looses area in a certain region, for a given regional yield, other region will offset it with an increase in area. Confidential. Do not quote or cite unless authorized. 6
  7. 7. Fig. 1 Brazilian Biomes and States RR AP AP AM MA CE PA RN PB PE AC PI AL RO MT TO SE BA GO MG MS ES Amazon Forest Atlantic Forest SP RJ Savanna PR Steppe Pantanal wetland SC South Grassland RS
  8. 8. Fig. 2 Macro-regions used in the Land Use ModelNorth AmazoniaCenter West CerradoMAPITO and BahiaNortheast coastSoutheastSouth Source: UFMG, ICONE. Confidential. Do not quote or cite unless authorized. 8
  9. 9. Figure 3. Land Use Model: Interactions Among Sectors Rice Corn Ethanol Sugarcane Sugar Industry and Cotton biodiesel Soybean oil Drybean Pork Soybean Soybean Poultry (eggs meal and chicken) Pasture BeefSource: ICONE Confidential. Do not quote or cite unless authorized. 9
  10. 10. Figure 4. General Structure of the Land Use Model Exogenous macroeconomic data - Population; - World and national GDP; - World oil price and domestic gasoline price; Domestic - Exchange rate; consumption - Inflation rate; - Fertilizer price index; - Vehicle fleet. Costs Net exports Demand Final stocks (t-1) Expected Price Area return Production Supply Yields Initial stocks Exogenous Endogenous variable variableSource: ICONE. Confidential. Do not quote or cite unless authorized. 10
  11. 11. General structure of the land use model One key output of the model is the total land allocated for agriculture and pastures. If this total, plus the exogenous planted forest area, is increasing over time, more natural land is brought for production purposes. This excess allocation of land can be explained by the combination of two factors:  Increase of cattle herd in regions with agricultural frontiers (regions 4 and 6 of the model), with a simultaneous reduction or stabilization of cattle herd in the traditional areas. This can be interpreted as an indirect effect due to crops expansion;  Expansion of crops in the frontier, which is a direct effect. In order to measure the indirect effect it is necessary to isolate these two causes of crops and pasture expansion in the frontier regions. 11
  12. 12. Data Gathering and Preparation In order to run the regressions and to calculate the parameters (elasticities and coefficients) used for the projections, a 13 year database was organized, from 1996 to 2008. Due to the lack of information with respect to prices and costs of production, data from 1997 and 2007 were used for the estimations. With respect to the variables that are solved endogenously in the model, the following variables are included in the database:  Supply and demand balance sheets for Brazil;  Regional information on production, planted area, yields, prices received by farmers, costs of production, cattle herd structure, animal production (beef, chicken, eggs, pork and milk). The model requires a set of exogenous macroeconomic variables (presented in the figure 2). We are using exogenous macroeconomic variables supplied by FAPRI. The model is prepared to project ethanol exports and domestic demand endogenously, but can also work with exogenous scenarios, such as different countries´ mandates. ICONE is ready also to create exogenous scenarios of ethanol world trade, calculating supply and demand for the main producers and consumers (Unites States, European union, Japan, etc.). 12
  13. 13. Assumptions Equilibrium price is obtained when supply is equal to demand, in a given year, for a given activity (crops or animal products). Area allocated to a given crop, in a given year, is a result of the market equilibrium. Producers respond, in terms of planted area, according to the expected market return (costs of production of the current year and prices received in the previous year). Prices received by farmers and prices paid by consumers follow the same trend over time. The model assumes full availability of capital for investments and credit for working capital, which means that it does not capture negative effects of low availability of credit in the market. Crops yields and total recoverable sugar factors, although regional, are projected as a time series trends. Prices are solved at a national level and are transmitted to the regions using transmissions coefficients estimated by regressions. Improvements in infrastructure, which would may lead to increase prices and to reduce costs of production can be simulated as scenarios. Confidential. Do not quote or cite unless authorized. 13
  14. 14. The two model´s versions First Version Results are ready and have already been used for some projects. Results validation among analyzed sectors will be carry out soon. The first version doesnt considered land availability, so that total agricultural land use can expand indefinitely. Equations for Allocation Area are independent in each Region. Historical planted areas are considered as an explanatory variable. Improved Version Area allocated for each crop and pasture will be defined by two equations1. Expansion Equation: it will define total agricultural area and it is subject to land availability.2. Shares Equation: for each crop and pasture, its share on the total agricultural area is the dependent variable. Returns of all crops are used as explanatory variables. Equations for Allocation Area are simultaneously in each Region.
  15. 15. Net Growth of Agricultural Land Uses Area and Cattle Herd, 2002-06 (1,000 ha and heads) Sugarcane Other crops Total used CattleState Pasture (ha) (ha) (ha) area (ha) Herd (hd)São Paulo 622 -224 -882 -484 -909Minas Gerais 153 389 -625 -82 1,644Paraná 74 850 -1 287 -284Mato Grosso do Sul 41 1 -985 -210 558Goiás 34 576 -2,041 -1,431 545Bahia 26 492 143 661 912Mato Grosso 25 1,634 -1,437 0 3,881Maranhão 16 298 -463 -148 1.835Pará 3 115 2,502 2,620 5,311South-Centre 949 3,226 -5,971 -1,920 5,435Total 1,000 5,446 -5,385 1,061 18,383 Source: PAM/IBGE, Agricultural Census/IBGE and PPM/IBGE.
  16. 16. Brazil: Crops Area (excluding 2nd crop) and Production and Meat Production Performance Crops Area and Production (million tons and million ha) Meat (million tons)900 160 12 18800 140 16 10700 120 14600 8 12 100500 10 80 6400 8 60300 4 6200 40 4100 20 2 2 0 0 0 0 5 9 1 3 7 7 90 92 94 96 98 00 02 04 06 08(e) /0 /0 /9 /0 /0 /9 00 04 06 98 02 96 Production Planted Area Chicken Beef PorkSource: CONAB; USDA; ABIEC; ABIPECS; ABEF. Elaboration: ICONE
  17. 17. Brazil: Livestock dynamics Catle Herd in the Model´s Regions Pasture Area in the Model´s Regions 60000 70000 South Southeast Center West Cerrado 60000 North Amazonia 50000 MAPITO and Bahia Northeast coast( 1 ,0 0 0 h d s ) 50000 (1,000 ha) 40000 40000 30000 30000 20000 20000 South Southeast 10000 Center West Cerrado 10000 North Amazonia MAPITO and Bahia Northeast coast 0 0 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
  18. 18. Brazil: Sugarcane, Sugar and Ethanol Production Sugarcane Production and Area Sugar and Ethanol Production Million ton Million ha Million ton Billion ltr 600 8 35 60 7 30 500 50 6 25 400 40 5 20 300 4 30 15 3 20 200 10 2 5 10 100 1 0 0 0 0 90/91 92/93 94/95 96/97 98/99 00/01 02/03 04/05 06/07 90/91 92/93 94/95 96/97 98/99 00/01 02/03 04/05 06/07 Production Planted Area Sugar EthanolSource: CONAB; IBGE; UNICA. Elaboration: ICONE
  19. 19. Brazil: Ethanol Supply & Demand (billion liters) 60 60 Production Exports Domestic Consumption 50 50 40 40 30 30 20 20 10 10 0 0 96/97 97/98 98/99 99/00 00/01 01/02 02/03 03/04 04/05 05/06 06/07 07/08(e)Source: CONAB; IBGE; UNICA. Elaboration: ICONE
  20. 20. Overview of Sugarcane in Brazil
  21. 21. Applications The model is very flexible and can be used for different types of simulations and scenarios. The model is also prepared to simulate impacts of variables that are not explicit in the model, such as improvements in transportation infrastructure (construction of an ethanol pipeline, for example), costs reduction due to he adoption of new technologies (second generation ethanol and GMOs adoption), among others applications. Useful for specific sector analysis Regional land use results can be disaggregate for smaller scales (World Bank´s Brazil Low Carbon Study) Land use changes can be converted into carbon emissions. It is possible to connect the Brazilian Model with world models. It is used to compare land use results from other models (GTAP- CARB)