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CESifo, the International Platform of the Ifo Institute of Economic Research and the Center for Economic Studies of Ludwig-Maximilians University 10 th Venice Summer Institute 6 - 11 July 2009 “THE ECONOMICS AND POLITICS OF CLIMATE CHANGE” to be held on 6 - 7 July 2009 on the island of San Servolo in the Bay of Venice, Italy Climate Change, Land Use Patterns and Deforestation in Brazil José Féres, Eustáquio Reis and Juliana Speranza Climate change, land use patterns and deforestation in Brazil José Féres, Eustáquio Reis and Juliana Speranza Institute for Applied Economics Reasearch (IPEA) Abstract This paper aims at evaluating the impacts of climate change on land use patterns in Brazil. To this purpose, we specify and estimate an econometric land use model to assess how climate variables affect land allocation decisions according to three types of use: cropland, pasture and forestland. Based on the econometric model estimates, we then simulate how farmers will adapt their land use choices to future climate scenarios. Climate projections are based on the PRECIS model, developed by the Hadley Center, which provides data on future temperature and precipitation at a 50 km X 50 km resolution for the Brazilian territory. Simulation results suggest that climate change may induce a significant conversion of forest to pastureland and this conversion pattern may increase the deforestation pressure in the Amazon region. Moreover, the spatial differences in climate change are likely to imply a heterogeneous pattern of land use responses that vary by region. Keywords: climate change, land use models, deforestation, Brazilian agriculture. 1. Introduction A growing number of scientific studies indicate that the rising concentration of greenhouse gases in the atmosphere will lead to higher temperatures. General circulation models suggest that Brazil will warm less rapidly than the global average and that warming will vary by season. Temperature increases will likely be greatest over the Amazon rainforest and least in the southeastern coastal states (Hulme and Sheard 1999, Nobre et al. 2005). The spatial differences in climate change are likely to imply a heterogeneous pattern of human land use responses across Brazilian regions. Understanding such pattern is of fundamental importance in order to assess the future path of global warming. Since deforestation is a main source of carbon dioxide emissions, global warming will depend in part on future land use in the Amazon and the ability of the area’s vegetation to sequester carbon, thus creating a feedback within the climate change mechanism. A decrease (increase) in the rate of forest conversion contributes a dampening (amplifying) effect on CO2 emissions; a decrease (increase) in CO2 emissions moderates (accelerates) climate change. How do deforestation patterns respond to climate change? For the moment, researchers have focused their attention on how human behavior is likely to influence land cover change and how various future land use scenarios will affect regional climate. However, little efforts have been devoted to examine how climate change affects agricultural land use patterns and forest conversion. Addressing this latter issue is essential for understanding this feedback loop between climate change/land use and therein providing input into models that predict the trajectory of climate change. Moreover, understanding farmers´ adaptation to climate change is important to assess the sustainability of the agricultural activities and to evaluate the impact of climate change on farmers´ well-being. This paper aims at evaluating the impacts of climate change on land use patterns in Brazil. To this purpose, we specify and estimate an econometric land use model to assess how climate variables affect land allocation decisions according to three types of use: cropland, pasture and forestland. The model is estimated by a simultaneous equation method, in order to account for the interdependence regarding land allocation decisions. Our dataset is based on the 1995 Brazilian Agricultural Census, produced by the Instituto Brasileiro de Geografia e Estatística (IBGE). With the estimated coefficients in hand, we then simulate how farmers will adapt their land use choices to future climate scenarios. Climate projections are based on the PRECIS model, developed by the Hadley Center, which provides data on future temperature and precipitation at a 50 km X 50 km resolution for the Brazilian territory. Simulation results suggest that climate change may induce a significant conversion of forest to pastureland and this conversion pattern may increase the deforestation pressure in the Amazon region. Depending on the emission scenario and the timeslice, deforestation ratios range between 15% to 20% of the establishment areas. Moreover, the spatial differences in climate change are likely to imply a heterogeneous pattern of land use responses: in regions where the agricultural productivity is expected to increase, farmers may convert pasture to cropland. On the other hand, farmers may convert cropland to pasture in areas where climate change may make farming unprofitable. This paper is organized as follows. The second section reviews the literature on the economic impacts of climate change on the agricultural sector. Section 3 presents the economic model and the econometric specification. Finally, section 4 discusses the main simulation results regarding land allocation patterns. 2. Litterature review There is a vast economic literature on the impacts of climate change on agriculture. The pioneering studies adopted the so-called “production function approach” (Decker et al. (1986), Adams (1989), among others). This approach, also called “agronomic model”, takes an underlying production function and varies the relevant environmental input variables to estimate the impact of these inputs on production. Due to its experimental design, the production function approach provides estimates of the effect of weather on yields of specific crops that are purged of bias due to determinants if agricultural output that are beyond farmers´ control. Its disadvantage is that these estimates do not account for the full range of compensatory responses to changes in weather made by profit-maximizing farmers. For example, in response to changes in climate, farmers may alter their use of fertilizers or change their mix of crops. Since farmers´ adaptations are completely constrained in the production function approach, it is likely to produce estimates of climate change that are biased downward. By using economic county-level data on land values, Mendelsonh et al. (1994) develop a hedonic model that in principle corrects for the bias in the production function approach. Instead of looking at the yields of specific crops, they examine how climate in different places affects the value of farmland. The clear advantage of the hedonic approach is that if land markets are operating properly, prices will reflect the present discounted value of land rents into the infinite future. The hedonic approach accounts both for the direct impacts of climate on yields of different crops as well as the indirect substitution of different activities. Several applications of the hedonic approach to US agriculture have found mixed evidence on the sign and magnitude of the impacts of climate change on agricultural land. (Mendelsonh, Nordhaus and Shaw (1999), Schelenker, Hanemann and Fischer (2005,2006)). The hedonic approach has been recently criticized by Deschênes and Greenstone (2007). The authors observe that the validity of the method rests on the consistent estimation of the effect of climate on land values. However, it has been recognized that unmeasured characteristics are important determinants of output and land values in agricultural settings. Consequently, the hedonic approach may confound climate with other factors, and the sign and magnitude of the resulting omitted variable bias is unknown. Deschênes and Greenstone (2007) propose a fixed-effects model that exploit the presumably year-to-year variation in temperature and precipitation to estimate the impacts of climate change on agricultural profits and yields. More specifically, the authors use a county-level panel data to estimate the effect of weather on US agricultural profits, conditional on county and state by year fixed effects. The weather parameters are identified from the county-specific deviations in weather about the county averages after adjustment for shocks common to all counties in a state. This variation is presumed to be orthogonal to unobserved determinants of agricultural profits, so it offers a possible solution to the omitted variable bias problems that appear to plague the hedonic approach. Using long-run climate change predictions from the Hadley 2 Model, their preferred estimates indicate that climate change will lead to a 4.0 percent increase in annual agricultural sector profits. They also find the hedonic approach to be unreliable because it produces estimates that are extremely sensitive to seemingly minor choices about control variables, sample and weighting. Regarding the Brazilian case, Sanghi et al. (1997) evaluate the effects of climate on Brazilian agricultural profitability using the hedonic method. They estimate the impacts of a 2.5ºC temperature increase and a 7% increase in precipitation, and they find that the net impact of climate change on land value is negative, between -2.16% and -7.40% of mean land values. Sanghi et al. (1997) results are more moderate than the results of the Siqueira et al. (1994) study, even considering that the temperature change under consideration is more conservative. However, their state-level analysis confirms the Siqueira et al. (1994) results that the Center-West states are the most negatively affected. This land is primarily the hot, semi-arid, and most recently developed cerrado. The cooler Southern states benefit mildly from warming. Results from different agricultural census years are not substantially different, however both the sign and the magnitude of the climate variables change considerably across census years. Evenson and Alves (1998) extend the Sanghi et al. (1997) exercise to include the effects of climate change on land use, and the mitigating effects of technology on the relationship between climate change and agricultural productivity. They model land value as well as the profit-maximizing share of farmland in different land uses (perennials, annuals, natural pasture, planted pasture, natural forest, and planted forest) as a function of climate, technology, and control variables. They jointly estimate the six land share functions and the land value function. Results show that a combined increase of 1°C and 3% rainfall will lead to a 1.84% reduction in natural forest and an increase of 2.76% in natural pasture. Their analysis suggests that increased investments in research and development would partially mitigate the loss of natural forest due to climate change. This same mild climate change is predicted to reduce land values by 1.23% in Brazil as a whole. As in Sanghi et al. (1997), the North and Northeast and part of the Center-West face the most severe negative impacts. Many municípios in the Center-East, South, and Coastal regions benefit from climate change. Finnaly, Féres et al. (2007) evaluate the impact of climate change on agricultural profitability in Brazil by applying the fixed-effects method. Simulation results suggest that the overall impact of climate change will be quite modest for the Brazilian agriculture in the medium term, but these impacts are considerably more severe in the long term. Generally speaking, the empirical evidence indicates that the net impact of climate change on Brazilian agriculture is negative, although there are varying regional consequences. The North, Northeast and Center-West regions seem to be more vulnerable to the climate change effects and they may incur in higher agricultural losses. On the other hand, the South region of Brazil may benefit from the higher temperatures predicted by the climate models. It should also be noticed that Evenson and Alves (1998) is the only work that propose a land use model to analyze farmers´ adaptation to climate change in Brazil. Our paper presents three contributions with respect to Evenson and Alves (1998). First, our econometric specification is derived from a structural model, which is consistent with the economic theory underlying farmers´ behavior. Second, we estimate the econometric model by simultaneous equation methods, which may account for the interdependence between land allocation decisions. Third, our simulations are based on climate variables provided by a downscaled general circulation model at a resolution of 50 km X 50 km, which are more precise than the projections used in previous studies. 3. Economic and Econometric Modeling General model We consider that farmers allocate their land for three types of use: crop, pasture and forest. Farmers decide their land allocations so as to maximize profits, subject to the restriction that land allocations cannot exceed total farm area. This decision process may be represented by the following constrained optimization problem: Max ∑ Π ( p ´, r ´, n , X ) : ∑ n 3 3 i n1 , n 2 , n 3 i i =1 i =1 i = N (1) where ni is the land allocated for each land use type i (i=1,2,3), pi´ is a price vector for outputs associated to each use type, r´ is a vector of input prices, n is a vector of land allocations for the three use types, X is a vector containing agro-climate variables and N is the total farm area. A Lagrangean function, denoted L, states the constrained maximization problem as L = ∑ Π i ( pi ´, r´, n, X ) + µ N − ∑ ni . 3 i =1 3 i =1 (2) The necessary conditions for an interior solution of (2) are ∂L ∂Π i = −µ =0 ∂ni ∂ni i = 1, 2, 3 (3) 3 N − ∑ ni = 0. (4) i =1 Equations (3) allocate land among the three use types so as to equate the marginal profit from each use type. The land constraint (4) is binding assuming an interior solution. Solving equations (3) and (4) yields the optimal land allocation choices for the three use types, which are expressed as a function of output and input prices, total farm areas and the agro-climate variables ni* ( pi ´, r´, N , X ) i = 1, 2, 3. (5) * i Moreover, replacing the optimal land allocation expressions n ( pi ´, r´, N , X ) in equation (4) and differentiating, we have 3 ∑ i =1 ∂ni* ( pi , r , N , X ) ≡ 1; ∂N 3 and ∑ i =1 3 ∑ i =1 ∂ni* ( p i , r , N , X ) ≡ 0; ∂p 3 ∑ i =1 ∂ni* ( p i , r , N , X ) ≡0 ∂r ∂ni* ( p i , r , N , X ) ≡ 0. ∂X (6) These four identities impose physical conservation laws on the allocation decisions. The first identity says that land reallocations completely absorb an additional hectare of land, so that they sum to 1. The other three identities says that land reallocations following a change in output prices, input prices or agro-climate variables sum to zero since land availability must remain unchanged. Thus, if producers increase cropland area in response to an increase in crop prices, this increase must be offset with a reduction in the areas allocated to the other two alternative uses (pasture and forest). Econometric specification and estimation In order to derive the econometric specification of our land use model, we assume that the restricted profit function in equation (1) takes a normalized quadratic form. The choice of this functional form may be justified for three reasons. First, the normalized quadratic is a flexible functional form. Second, it is consistent with economic theory, since it allows imposing linear homogeneity on the profit function as well as symmetry restrictions. Finally, closed form expressions for ni* are tractable using the normalized quadratic because its first derivatives are linear. To begin deriving the allocation equations, we observe that the necessary conditions expressed in (3) and (4) form a system of four linear equations. Setting the equations ∂Π i removes µ. The resulting equations form a corresponding to (3) sequentially equal to ∂ni linear system of three equations and three unknowns (n*crops, n*pasture and n*forest). The solutions are the estimable land allocation equations for the three use types: j t s f =1 k =1 l =1 ni* = β 0i + ∑ β1i f p f + ∑ β 2i k rk + β 3i N + ∑ β 4i l X l + η i i = crops, pasture, forest subject to the parameters´ restrictions implied by (6) (7) ∑β i 3 =1; i ∑β i 1f = 0; i ∑β i 2k = 0 and i ∑β i 4l =0 (8) i and the symmetry restrictions β1i f = β1if . (9) In order to estimate the system of three equations given by (7) subject to the restrictions in (8) and (9), we use the Iterated Seemingly Unrelated Regression (ISUR) method. The choice of a simultaneous equation method is justified by two reasons. First, it may account for the correlation between the errors ηi. Such correlation is likely to be present, since land allocation decisions between the three types of use should be interdependent. Second, a simultaneous equation method allows us to impose the cross-equation restrictions expressed in (8) and (9). It should also be noted that the adding-up conditions (8) imply that the equation system in (7) is singular. In order to circumvent this problem, we drop one equation and estimate the other remaining two equations. The coefficients of the omitted equation can be recovered by using the restrictions in (8). Since we iterate our estimation procedure, coefficient estimates are invariant to the choice of which equation is deleted. Such invariance assures the robustness of our coefficient estimates. Simulation In order to assess how farmers will adapt their land allocations in response to climate change, we adopt the following simulation strategy. First, we simulate the land allocated to each of the three use types by considering the temperature and precipitation predicted by the climate model for the baseline period and obtain ^* j ^ ^ t ^ ^ s ^ n i , BASELINE = β 0i + ∑ β1i f p f + ∑ β 2i k rk + β 3i β 3i N + ∑ β 4i l X l , BASELINE f =1 k =1 (10) l =1 ^ where β s are the estimated coefficients of the econometric model. Next, we replace the climate variables for the baseline period by the predicted temperature and precipitation for timeslice T1, keeping all other explaining variables unchanged ^* ^ j ^ t ^ ^ s ^ n i ,T 1 = β 0i + ∑ β 1i f p f + ∑ β 2i k rk + β 3i β 3i N + ∑ β 4i l X l ,T 1 f =1 k =1 l =1 (11) The variation in the area allocated to each type of use i may be computed by the formula ^* ∆ni* = ^* n i ,T 1 − n i , BASELINE ^* X 100 (12) n i , BASELINE * We therefore obtain the estimated variations ∆ncrops , ∆n *pasture and ∆n *forests resulting from climate change. Data Our main data source consists of the 1995 Brazilian Agricultural Census, produced by the Instituto Brasileiro de Geografia e Estatística (IBGE). The Census contains information on land use, output and input prices for all Brazilian municipalities. The variables used in the econometric model are described below 1 • Land allocations: cropland was defined as the sum of temporary and permanent crop areas, while pastureland was computed as the sum of natural and planted pastures. Forest areas correspond to the sum of natural forest, planted forest and productive land that was not used in the four years preceding the agricultural Census. • Output prices: crop prices were computed as a Laspeyres price index based on twelve crops1, which represent the vast majority of the Brazilian agricultural production. The municipal average cattle price was used as a proxy for the price of pasture products. In constructing the price for forest products, we just considered the timber price, since other forest products represent a negligible value when compared to logging activities. We assume that the timber price is a good proxy for the opportunity cost of conserving the forest area. • Input prices: due to data availability restrictions, we consider only two inputs in our econometric specification _ labor and land. Labor price is given by the average rural salary, computed as the sum of the salaries paid to rural workers in a certain municipality divided by the number of rural workers. Land prices were calculated as the value of rented lands in the municipality divided by the total rented area. • Climate variables: the climate variables are the observed average temperatures and precipitations for the period 1960-1996. The reason to include historical averages is that farmers are likely to decide their land use allocations based on long-term climate features. Moreover, in order to take into account seasonality effects, we specify the econometric regression in terms of quarterly averages. This approach is based on the hypothesis that a one-degree increase in the average temperature for the months December/January/February (summer) may have a different impact in These crops are rice, sugarcane, corn, beans, wheat, soybean, banana, cotton, potatoes, pepper, cocoa and coffee. terms of land allocation than a one-degree increase in the average temperature for the months June/July/August (winter). Climate projections for the period 2010-2100 are based on the PRECIS model, developed by the Hadley Center, which provides data on temperature and precipitation at a 50 km X 50 km resolution for the Brazilian territory. • Agronomic variables: our agronomic variables include information on soil type, erosion propensity, declivity, drainage restrictions and other characteristics that may affect farmers´ decisions concerning land allocation. 4. Results The land allocation equations in (7) were estimated by the Iterated Seemingly Unrelated Regression method for 2,846 Brazilian municipalities. In order to impose the homogeneity restrictions, we use the forest price as the numeraire and express the crop and cattle prices in relative terms (denoted by prel_crops and prel_cattle, respectively). The forest allocation is the omitted equation, whose parameters were recovered by using the restrictions expressed in (8). Table 1 reports the estimation results. Table 1: Estimation results – land allocation equations Equation cropland pastureland Independent variables prel_crops prel_cattle prel_land prel_labor tmp30djf tmp30mam tmp30jja tmp30son pre30djf pre30mam pre30jja pre30son Agronomic variables Obs 2846 2846 Parms 46 46 RMSE 92141.36 366093.7 Dependent variable: cropland Coef. Std. Error t-statistic 537.34 3587.65 -16.00 -623.38 34762.81 -39612.18 46660.09 -30350.94 -198.00 11.41 413.76 102.95 288.21 2599.28 62.42 147.87 7619.05 8229.03 6659.29 6782.15 105.45 122.00 123.44 123.87 1.86 1.38 -0.26 -4.22 4.56 -4.81 7.01 -4.48 -1.88 0.09 3.35 0.83 yes "R - sq" 0.9545 0.969 chi2 59739.32 89000.43 Dependent variable: pastureland Coef. Std. Error t-statistic 3587.65 68636.61 76.11 1443.18 -102519.90 42998.77 -115500.30 170312.30 1621.34 -3847.55 3609.76 -1701.83 2599.28 30288.68 248.03 603.07 30279.63 32697.55 26458.92 26954.73 419.10 484.80 490.47 492.19 1.38 2.27 0.31 2.39 -3.39 1.32 -4.37 6.32 3.87 -7.94 7.36 -3.46 yes Breusch-Pagan test of independence: chi2(1) = 1099,62 Generally speaking, the model showed a good fit and the estimated coefficients presented the expected signs. For example, an increase in the relative crop price leads to an increase in cropland, while an increase in the cattle prices is associated to an increase in pastureland. Most of the temperature and precipitation coefficients are statistically significant, which means that climate factors are an important determinant of farmers´ decisions regarding land allocation choices. Moreover, the estimated coefficients suggest that variations in temperature and precipitation in different seasons may have a distinct impact in terms of land allocations. Finally, the Breusch-Pagan test rejects the hypothesis that the residuals are uncorrelated. This means that simultaneous equation methods provides more efficient results than estimating each equation individually. With the estimated coefficients in hand, we can simulate how farmers´ will adapt to climate change. We consider two IPCC emission scenarios: A2 and B2. For each emission scenario, we consider three timeslices: the predicted average temperature and precipitation for the periods 2010-2040, 2040-2070 and 2070-2100. Tables 2 and 3 present simulation results. Table 2: variation in cropland, pastureland and forestland – A2 scenario Region Brazil crop -1,7% (-0,9 x 106 ha) North Northeast Southeast South CenterWest 2010-2040 pasture forest +11,1% -17,1% (19,7 x 106 ha) (-18,9 x 106 ha) crop +3,1% (1,6 x 106 ha) 2040-2070 pasture forest +11,1% -19,36% (19,9 x 106 ha) (-21,4 x 106 ha) crop +11,0% 2070-2100 pasture +6,5% (5,5 x 106 ha) (11,5 x 106 ha) forest -15,4% (-17,0 x 106 ha) - 2,4% +17,7% -14,6% +17,9% +16,7% -15,8% +44,1% +10,4% -13,3% (-0,1 x 106 ha) (4,3 x 106 ha) (-4,2 x 106 ha) (0,5 x 106 ha) (4,1 x 106 ha) (-4,6 x 106 ha) (1,4 x 106 ha) (2,5 x 106 ha) (-3,9 x 106 ha) -27,6% +28,3% -17,9% -18,9% +25,1% -18,7% +31,8% +9,8% -27,2% (-4,0 x 106 ha) (9,1 x 106 ha) (-5,1 x 106 ha) (-2,7 x 106 ha) (8,1 x 106 ha) (-5,3 x 106 ha) (4,6 x 106 ha) (3,1 x 106 ha) (-7,7 x 106 ha) -7,0% +4,9% -23,2% +11,1% +5,9% -30,6% -7,6% +9,6% -23,8% (-0,8 x 106 ha) (1,9 x 106 ha) (-2,7 x 106 ha) (1,3 x 106 ha) (2,2 x 106 ha) (-3,5 x 106 ha) (-0,9 x 106 ha) (3,6 x 106 ha) (-2,7 x 106 ha) +27,9% -6,0% -32,2% +30,4% -4,6% -40,2% +33,4% -16,8% -13,1% (3,8 x 106 ha) (-1,2 x 106 ha) (-2,5 x 106 ha) (4,1 x 106 ha) (-1,0 x 106 ha) (-3,1 x 106 ha) (4,5 x 106 ha) (-3,5 x 106 ha) (-1,0 x 106 ha) -6,4% +8,4% -14,2% -7,1% +10,2% -17,4% -12,0% +9,3% -14,7% (-0,5 x 106 ha) (5,2 x 106 ha) (-4,8 x 106 ha) (-0,5 x 106 ha) (6,4 x 106 ha) (-5,9 x 106 ha) (-0,9 x 106 ha) (5,8 x 106 ha) (-4,9 x 106 ha) Table 3: variation in cropland, pastureland and forestland – B2 scenario Region Brazil North Northeast Southeast South CenterWest 2010-2040 2040-2070 2070-2100 crop +0,5% pasture +9,9% forest -16,2% crop +2,7% pasture +10,6% forest -18,2% crop -3,0% pasture +10,1% forest -15,0% (0,3 x 106 ha) (17,7 x 106 ha) (-18,0 x 106 ha) (1,3 x 106 ha) (18,8 x 106 ha) (-20,2 x 106 ha) (-1,5 x 106 ha) (18,1 x 106 ha) (-16,6 x 106 ha) +4,0% +13,0% -11,3% +10,3% +15,5% -14,0% 24,9% 12,8% -13,3% (0,1 x 106 ha) (3,2 x 106 ha) (-3,3 x 106 ha) (0,3 x 106 ha) (3,8 x 106 ha) (-4,1 x 106 ha) (0,8 x 106 ha) (3,1 x 106 ha) (-3,9 x 106 ha) -26,6% +25,5% -15,3% -23,5% +25,1% -16,4% +12,6% +14,1% -22,3% (-3,8 x 106 ha) (8,2 x 106 ha) (-4,3 x 106 ha) (-3,4 x 106 ha) (8,1 x 106 ha) (-4,7 x 106 ha) (1,8 x 106 ha) (4,5 x 106 ha) (-6,3 x 106 ha) +13,6% +3,5% -25,2% +16,3% +3,7% -28,6% -20,3% +13,6% -24,0% (1,6 x 106 ha) (1,3 x 106 ha) (-2,9 x 106 ha) (1,9 x 106 ha) (1,4 x 106 ha) (-3,3 x 106 ha) (-2,4 x 106 ha) (5,1 x 106 ha) (-2,8 x 106 ha) +22,6% -2,7% -31,8% +27,1% -1,7% -42,1% +15,9% -8,6% -4,7% (3,0 x 106 ha) (-0,6 x 106 ha) (-2,5 x 106 ha) (3,7 x 106 ha) (-0,4 x 106 ha) (-3,3 x 106 ha) (2,1 x 106 ha) (-1,8 x 106 ha) (-0,4 x 106 ha) -5,1% +8,0% -13,8% -9,1% 9,6% -15,9% -15,2% +10,0% -15,3% (-0,4 x 106 ha) (5,0 x 106 ha) (-4,6 x 106 ha) (-0,7 x 106 ha) (6,0 x 106 ha) (-5,3 x 106 ha) (-1,1 x 106 ha) (6,3 x 106 ha) (-5,1 x 106 ha) At the national level, our simulation results suggest that climate change may induce a significant reduction in forestland. Depending on the emission scenario and the timeslice, deforestation ratios range between 15% and 20% of the establishment areas. Most of the forestland would be converted into pasture. In particular, it should be remarked that this conversion pattern is observed in the Northern region, where the Amazon rainforest is located. This means that farmers´ adaptation to climate change may increase the deforestation pressure in the Amazon rainforest and it may contribute to an increase in greenhouse gas emission due to deforestation. Moreover, the spatial differences in climate change are likely to imply a heterogeneous pattern of land use responses that vary by region. In the South region, where the projected climate conditions may increase agricultural productivity, farmers may convert pasture into cropland. On the other hand, in the Center-West region simulations indicate a conversion from crops to pasture. This pattern is in line with previous studies that show that agricultural productivity in Center-West region will be severely affected by climate change. Agricultural productivity analysis In order to check whether the regional land use change patterns are in line with the expected changes in agricultural productivity, we estimated agricultural productivity regressions. We have specified the reduced form equations AVGPROD = f(TEMP, PREC, Z) (13) where AVGPROD refers to average productivity (ton/ha), TEMP is temperature, PREC is precipitation and Z is a vector of agronomic characteristics that may affect agricultural productivity. Seven crops were analyzed: rice, sugarcane, beans, tobacco, corn, soybeans and wheat. The results are in Table 3 In general, the North, Northeast and Center-West regions are the most negatively affected by climate change. In these regions, for the different scenarios and timeslices, our simulations suggest a decrease in average productivity for most crops. This is not surprising, since in these areas temperatures are already near the upper bound of most plants´ tolerance. In particular, the decrease in average productivity for subsistence crops in the Northeast region indicates that climate change may have significant socioeconomic implications. On the other hand, the simulations point to an increase in productivity for the vast majority of crops in the South region. In fact, due to its lower temperatures and more fertile soils, there is some room for adaptation in this region. Table 3 – Average productivity variations for selected crops by Brazilian regions Rice North Northeast Southeast South Center-West Cana North Northeast Southeast South Center-West A2 B2 2010-2040 2040-2070 2070-2100 2010-2040 2040-2070 2070-2100 -26.6% -23.4% -9.9% -30.3% -26.8% -9.9% -28.9% -26.0% -11.0% -27.1% -24.3% -15.4% -1.3% -0.7% 19.5% 9.2% 6.2% 15.0% 46.4% 44.4% 8.2% 48.5% 46.2% 5.8% -13.5% -12.3% -12.1% -14.1% -14.4% -5.9% -36.4% -2.3% 32.8% 39.5% -1.7% -36.7% -4.3% 34.5% 66.5% -1.1% -54.8% -7.1% 45.6% -36.6% -5.8% -33.4% -0.9% 37.4% -14.1% -2.7% -31.7% -3.9% 34.3% -17.7% -3.6% -54.8% -4.6% 47.5% -59.6% -3.0% -25.3% -29.9% 27.3% 37.0% -8.0% -27.1% -30.5% 32.6% 36.8% -7.6% -19.0% -30.3% 30.7% 30.8% -7.9% -29.7% -27.7% 32.8% 36.5% -6.5% -26.5% -31.1% 27.9% 38.5% -7.3% -19.0% -29.2% 27.4% 34.7% -5.6% North Northeast Southeast South Center_west -46.6% -24.9% 29.8% 25.0% -17.9% -43.8% -23.0% 29.1% 22.1% -18.5% -40.9% -28.7% 22.0% 30.9% -20.6% -46.0% -20.3% 31.8% 25.7% -21.5% -47.1% -17.2% 33.4% 23.3% -21.4% -40.9% -31.8% 19.4% 45.8% -27.1% Corn North Northeast Southeast South Center-West 31.8% -26.7% 10.8% -8.5% -11.9% 29.6% -26.7% 18.7% -9.5% -13.5% 31.1% -17.4% 20.9% -12.1% -7.9% 29.7% -21.7% 21.7% -8.2% -12.4% 29.0% -26.3% 16.5% -10.8% -13.6% 28.9% -16.8% 17.5% -14.4% -6.1% 34.7% -10.6% -14.5% 30.7% -5.5% 40.4% -6.4% -15.5% 21.3% -0.7% 43.6% -37.5% -21.9% 38.3% 2.9% 37.6% -7.7% -13.6% 28.8% -1.8% 26.1% -10.8% -11.3% 33.2% -3.5% 45.6% -34.4% -22.0% 42.0% 2.1% -20.9% -17.6% 26.4% 30.3% 8.6% -18.3% 2.3% 37.6% 33.0% 1.6% -32.3% -41.0% 2.0% 19.5% -5.4% -30.1% -17.8% 33.5% 31.3% 9.0% -24.4% 13.3% 22.6% 22.8% -4.6% -40.0% -49.9% 14.4% 18.0% 0.4% Beans North Northeast Southeast South Center_west Tabacco Soya North Northeast Southeast South Center-West Wheat North Northeast Southeast South Center-West Conclusion This paper aims at evaluating the impacts of climate change on land use patterns in Brazil. To this purpose, we specify and estimate an econometric land use model to assess how climate variables affect land allocation decisions according to three types of use: cropland, pasture and forestland. The model is estimated by a simultaneous equation method, in order to account for the interdependence regarding land allocation decisions. At the national level, our simulation results suggest that climate change may induce a significant reduction in forestland. Depending on the emission scenario and the timeslice, deforestation ratios range between 15% and 20% of the establishment areas. Most of the forestland would be converted into pasture. In particular, it should be remarked that this conversion pattern is observed in the Northern region, where the Amazon rainforest is located. This means that farmers´ adaptation to climate change may increase the deforestation pressure in the Amazon rainforest and it may contribute to an increase in greenhouse gas emission due to deforestation. 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Potential effects of global climate change for Brazilian agriculture, and adaptive strategies for wheat, maize, and soybeans. Revista Brasileira de Agrometeorologia, Santa Maria, v.2 pp. 115129. Appendix 1: variations in land areas Cropland Scenario A2 – 2010-2040 Scenario B2 – 2010-2040 Scenario A2 – 2040-2070 Scenario B2 – 2040-2070 Scenario A2 – 2070-21 Scenario B2 – 2070-2100 Pasture Scenario A2 – 2010-2040 Scenario B2 – 2010-2040 Scenario A2 – 2040-2070 Scenario B2 – 2040-2070 Scenario A2 – 2070-2100 Scenario B2 – 2070-2100