Crop Land Use Change Data and Modeling: Case Study of Brazil

Transcription

1 Crop Land Use Change Data and Modeling: Case Study of Brazil Gbadebo ( Debo) Oladosu Keith Kline Virginia Dale January 13, 2009

2 Objectives Examine recent changes in cropland area/yield in Brazil and role of biofuel crops using recent data Examine cropland change model specifications for GTAP simulations 2 Managed by UT-Battelle

3 Biofuel Land Use: Studies of Brazil Birur et al. (2008) GTAP - Global General Equilibrium Simulation of world biofuel production impacts ( ) Similar loss of forest and pasture to sugarcane (7%) Wassenaar et al. (2007) CLUE-s - Statistical simulation model for spatial distribution of national land use change in Central and South America: ( ) Pasture replaces 61% of deforested lands; soybean hotspots in Amazon Region Nassar et al. (2008) Land use for sugarcane expansion in S. Central Brazil ( ) Remote sensing data: Other crops (53%), Pasture (45%) 343 interviews: Corn/soy (23%), Pasture (65%), Other crops(10%) Statistical model: Other crops (11%), Pasture (72%) Morton et al. (2006) Remote Sensing Data: Large (>25ha) deforestation in state of Matto Grosso ( ) Mechanized cropland (mostly soybean) account for 11% and 17% of deforestation 3 Managed by UT-Battelle

4 Current Study: Data Sources Global Agro-Ecological Zones (GAEZ) Report (2002) Used 5-minute grid cell GIS data layers Considered dominant ecosystems for 10 categories (aggregated to 6) e.g., Mosaic = mixture of trees, crops, pasture and grassland Focused on good to high land suitability for major crops under rain-fed and high input agriculture Captures most of land under intermediate inputs Rain-fed considers natural precipitation regimes Agricultural Data (Brazil IBGE) Municipal crop production, land area, yield and production value Data aggregated to 550 micro-regions 4 Managed by UT-Battelle

5 Data: Dominant Ecosystems Woodland: Forest: widespread Concentrated in Amazonia Grassland: Concentrated in middle region Micro-Region Area Share Mosaic: Cropland: Transition areas widespread

6 Data: Good to High Sugarcane Corn Oilseeds Micro-Region Area Share Roots Cereals

7 Data: Temporary Crops Land Use Total Use in 2002 ~56 million ha Net Area Increase : 7.8 million ha Soybean: 5.8 million ha (~74%) Sugarcane/Corn: About 1 million ha each Other Grains: Net Decrease of 0.7 million ha Average yields have gone up for most crops 7 Managed by UT-Battelle

8 Data: Temporary Crops Land Use Change: Cropland Area (Ha) 0 1-1,000 1,001-10,000 10,001-50,000 50, , , , , , , , , , ,001-2,000,000 8 Managed by UT-Battelle Harvested Area Cropland Area (Ha) -242, , , ,000-49, ,000-24, ,000-2, ,000 3,001-10,000 10,001-30,000 30,001-50,000 50, , , ,000

9 Data: Temporary Crops Land Use million ha million ha Harvested Area Corn Other Coarse Grains Other Grains Sugarcane OilSeeds Other Crops Change in Area percent Trend in Total Harvested Area Years Annual Change (%) Rolling 4-Year Annual Average Change (%) Corn Other Coarse Grains Other Grains Sugarcane OilSeeds Other Crops 9 Managed by UT-Battelle

10 Data: Temporary Crops Yield tons/ha Yield tons/ha tons/ha Corn Other Coarse Grains Other Grains OilSeeds Other Crops Sugarcane (2nd Axis) Change in Yield Corn Other Coarse Grains Other Grains Sugarcane OilSeeds Other Crops 10 Managed by UT-Battelle

11 Model: Cropland Allocation Constant Elasticity of Transformation Model in GTAP Compared current structure to alternative structures Total Current GTAP Structure X ln X Crops: i, t t = c i Pt + σ ln Pi, + βi, k 1 = Corn; 2 = Other Coarse Grains; 3 = Other Grains 4 = Sugarcane; 5 = OilSeeds; 6 = Other Crops X t ; X i,t ; P i,t = Micro-region total temporary cropland, cropland under crop i, and gross land returns ((1000R$/ha) for crop i, respectively E k = Other variables (dominant ecosystem, land suitability, etc) Results Elasticity of Transformation (σ) Small, significant, but positive sign (expected sign is negative) Single nesting of crops may hide price relationships in land allocation Model has good fit to data, but structural coefficients incorrect 11 Managed by UT-Battelle t k E k

12 Model: Cropland Allocation Single-Level CET: Estimates Coefficient T-Stat Coefficient T-Stat Equation 1: Corn Equation 4: Sugarcane Elasticity of Transf Elasticity of Transf Base Term Base Term Corn Corn OilSeeds OilSeeds Sugarcane Sugarcane Wheat Wheat Equation 2: Other Coarse Grains Equation 5: OilSeeds Elasticity of Transf Elasticity of Transf Base Term Base Term Corn Corn OilSeeds OilSeeds Sugarcane Sugarcane Wheat Wheat Equation 3: Other Grains Equation 6: Other Crops Elasticity of Transf Elasticity of Transf Base Term Base Term Corn Corn OilSeeds OilSeeds Sugarcane Sugarcane Wheat Wheat Managed by UT-Battelle R-Squared: 30% and 92%

13 Model: Cropland Allocation Multi-Level Nested CET Model: Total 1/4/5/6/3 2 1/4/5/6 3 1/4/5 6 Crops: 1 = Corn 2 = Other Coarse Grains 3 = Other Grains 4 = Sugarcane 5 = OilSeeds 6 = Other Crops Total 1/5/2/4/6 3 1/5/2/4 6 1/5/2 4 1/4 5 1/ Managed by UT-Battelle X ln X = c Pn + σ ln n Pn + βn, k n, l, t, l, t 1 5 n k k n, r, t, r, t (1) (2) 1 4 n = sub-nest 1 5; l = left member of a nest; r = right member of a nest E

14 Model: Cropland Allocation Multi-Level Nested CET Model: Results Structure (1) has better fit to the data Transformation elasticities 4 of 5 have expected signs in both cases (negative) Sub-nest involving Oilseeds has the positive sign under both structures Oilseeds behavior may reflect non-price planting motives Soybean planting as land preparation step when converting pasture to sugarcane (noted by Nassar et al 2008). General rotation of legume crops with other crops for soil improvements 14 Managed by UT-Battelle

15 Model: Cropland Allocation Multi-Level Nested CET Model: Estimates (1) CoefficientT-Stat Coefficient T-Stat Equation 1: Nest 1 Equation 4: Nest 4 Elasticity of Transf Elasticity of Transf Base Term Base Term Corn Corn OilSeeds OilSeeds Sugarcane Sugarcane Wheat Wheat Equation 2: Nest 2 Equation 5: Nest 5 Elasticity of Transf Elasticity of Transf Base Term Base Term Corn Corn OilSeeds OilSeeds Sugarcane Sugarcane Wheat Wheat Coefficient T-Stat Equation 3: Nest 3 Elasticity of Transf Base Term R-Squared: 30% and 91% Corn OilSeeds Sugarcane Wheat Managed by UT-Battelle

16 Model: Cropland Allocation Multi-Level Nested CET Model: Estimates (2) Coefficient T-Stat CoefficientT-Stat Equation 1: Nest 1 Equation 4: Nest 4 Elasticity of Transf Elasticity of Transf Base Term Base Term Corn Corn OilSeeds OilSeeds Sugarcane Sugarcane Wheat Wheat Equation 2: Nest 2 Equation 5: Nest 5 Elasticity of Transf Elasticity of Transf Base Term Base Term Corn Corn OilSeeds OilSeeds Sugarcane Sugarcane Wheat Wheat Coefficient T-Stat Equation 3: Nest 3 Elasticity of Transf Base Term Corn OilSeeds Sugarcane Managed by UT-Battelle Wheat R-Squared: 23% and 82%

17 Conclusion Most cropland expansion occurred in areas where mosaic ecosystems were dominant This reflects continuation of longer-term conversion trends Need for more precise data on composition of mosaic cover Land suitability and ecosystems heterogeneous across Brazil Needs to be reflected in detailed policy analysis Cropland expansion/contraction is widespread Soybean represents 75% (sugarcane 12%) of recent cropland expansion in Brazil Sugarcane expansion is concentrated in South, which has large areas of fallow/pasture land This study supports others showing sugarcane land expansion is more than 90% from pasture and other crop land Cropland distribution A case is made for different specification of cropland allocation structure in GTAP Estimated elasticities would be useful for Brazil 17 Managed by UT-Battelle

18 End Thank You Further details: 18 Managed by UT-Battelle