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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.
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.
References
Adams, R. (1989). Global climate change and agriculture: an economic perspective.
American Journal of Agricultural Economics, December, 71(5), pp. 1272-79.
Anderson, K and E. Reis. (2007). The Effects of Climate Change on Brazilian Agricultural
Profitability and Land Use: Cross-Sectional Model with Census Data. Final report
to WHRC/IPAM for LBA project Global Warming, Land Use, and Land Cover
Changes in Brazil.
Decker, W.L., V. Jones, and R. Achtuni. (1986). The Impact of Climate Change from
Increased Atmospheric Carbon Dioxide on American Agriculture. DOE/NBB-0077.
Washington, DC: U.S. Department of Energy.
Dêschenes, Olivier and Michael Greenstone (2007). “The Economic Impacts of Climate
Change: Evidence from Agricultural Output and Random Fluctuations in Weather”.
American Economic Review, 97(1): 354-85.
Evenson, R.E. & D.C.O. Alves (1998). Technology, climate change, productivity and land
use in Brazilian agriculture. Planejamento e Políticas Públicas, 18.
IPCC (2001). Climate change 2001: Synthesis Report. Summary for Policymakers.
Approved in detail at IPCC Plenary XVIII (Wembley, United Kingdom, 24-29
September 2001). World Meteorological Organization and United Nations
Environmental Programme.
Mendelsohn, R., W. Nordhaus, e D. Shaw (1994). The Impact of Global Warming on
Agriculture: A Ricardian Analysis. American Economic Review. 84(4): 753-71
Mendelsohn, Robert, William D. Nordhaus and Daigee Shaw (1999). “The Impact of
Climate Variation on US Agriculture”. In The Impact of Climate Change on the
United States Economy, ed. Robert Mendelsohn and James E. Neumann, 55-74.
Cambridge: Cambridge University Press.
Sanghi, A., D. Alves, R. Evenson, and R. Mendelsohn (1997). Global warming impacts on
Brazilian agriculture: estimates of the Ricardian model. Economia Aplicada,
v.1,n.1,1997.
Schlenker, W., W. M. Hanemann and A. C. Fisher (2005). Will U.S. Agriculture Really
Benefit from Global Warming? Accounting for Irrigation in the Hedonic
Approach," American Economic Review (March) 395-406.
Schlenker, W., W. M. Hanemann, and A. C. Fisher (2006). The Impact of Global Warming
on US Agriculture: An Econometric Analysis of Optimal Growing Conditions.
Review of Economics and Statistics 88(1): 113-125.
Siqueira, O.J.F. de, J.R.B. de Farias, and L.M.A. Sans (1994). 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