Download Climate change, land use patterns and deforestation in Brazil

Climate change, land use patterns and deforestation in Brazil1
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 a regional climate model which provides data on future temperature and
precipitation at a 50 km horizontal 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
This work was supported by the UK Department for International Development and the Núcleo de Estudos
em Modelos Espaciais Sistêmicos (NEMESIS - PRONEX/FAPERJ program). Thais Barcellos and Yanna
Braga provided efficient research assistance.
1. Introduction
A substantial body of scientific evidence indicates that rising concentration of greenhouse
gases in the atmosphere will lead to higher global mean temperatures and changing
precipitation patterns. At a regional level, 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 land use
responses across Brazilian regions.
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. 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.
However, little efforts have been devoted to examine how climate change affects
agricultural land use patterns and forest conversion. Addressing this issue is essential for
assessing the sustainability of the agricultural activities and to evaluate the impact of
climate change on farmers´ well-being. In addition to that, understanding farmers´
adaptation to climate change may shed some light on the feedback loop between climate
change/land use and therein providing input into models that predict the trajectory of
climate change.
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 regional climate model HadRM3P, developed by the Hadley
Center, which provides data on future temperature and precipitation at a 50 km horizontal
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 time horizon, deforestation ratios range between 15 percent to 20 percent of
total forest areas. Moreover, the spatial differences in climate change are likely to imply a
heterogeneous pattern of land use responses: in regions where agricultural activities are less
vulnerable to climate change, 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 this approach does not explore how
farmers would react to climate change. For example, in response to changes in climate,
profit-maximizing 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 overestimate the impact of climate change on the agricultural sector.
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), Schenkler,
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.
Notwithstanding the relatively large number of impact assessments on the agricultural
sector, there is a paucity of economic studies which focus on farmers´ adaptation strategies
to climate change. By explicitly modeling adaptation, Mendelsohn and Seo (2007) explore
whether farmers who face different climates tend to choose between three different types of
farming: crops only, livestock only and a combination of crops-livestock2. The results show
that choice of farm type and irrigation are very sensitive to climate. Farmers tend to choose
mixed crop-livestock farms and livestock-only farms in warmer locations, while crop-only
farms are more likely to be found in cooler locations. Farmers facing higher precipitation
are more likely to choose livestock only farms. Finally, farmers will tend to irrigate in
locations that are both cool and dry.
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 percent and -7.40
percent 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 percent
rainfall will lead to a 1.84 percent reduction in natural forest and an increase of 2.76 percent
in natural pasture. Their analysis suggests that increased investments in research and
2
Kurukulasuriya and Mendelsohn (2007) apply a similar methodology to analyze farmers´ adaptation
regarding crop choices in Africa.
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 percent 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.
Finally, 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 projections generated
by a regional circulation model, which allows constructing high-resolution climate change
scenarios. Therefore, our climate projections are more detailed than those 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
n1 , n 2 , n 3
3
 3

 ∑ Π i ( p i ´, r ´, n , X ) : ∑ n i = N 
 i =1
i =1

(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
3
3


L = ∑ Π i ( p i ´, r´, n, X ) + µ  N − ∑ ni  .
i =1
i =1


(2)
The necessary conditions for an interior solution of (2) are
∂L ∂Π i
=
−µ =0
∂ni
∂ni
(3)
i = 1, 2, 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)
Moreover, replacing the optimal land allocation expressions ni* ( 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* ( pi , r , N , X )
≡ 0;
∂p
∂ni* ( pi , r , N , X )
≡ 0.
∂X
3
∑
i =1
∂ni* ( pi , r , N , X )
≡0
∂r
(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
corresponding to (3) sequentially equal to
removes µ. The resulting equations form a
∂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 + ∑ β 1i f p f + ∑ β 2i k rk + β 3i N + ∑ β 4i l X l + η i i = crops, pasture, forest
(7)
subject to the parameters´ restrictions implied by (6)
∑β
i
3
=1;
i
∑β
i
i
1f
=0;
∑β
i
2k
i
= 0 and
∑β
i
4l
=0
(8)
i
and the symmetry restrictions
β 1i f = β 1fi .
(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 T0 and obtain
^*
j
^
^
^
t
^
^
s
n i ,T0 = β 0i + ∑ β 1i f p f + ∑ β 2i k rk + β 3i β 3i N + ∑ β 4i l X l ,T0
f =1
k =1
(10)
l =1
^*
^
where β s are the estimated coefficients of the econometric model and n i ,T0 is the estimated
land allocated to use i given the baseline climate scenario T0.
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 ,T1
f =1
k =1
(11)
l =1
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 ,T0
^*
X 100
(12)
n i ,T0
*
We therefore obtain the estimated variations ∆ncrops
, ∆n *pasture and ∆n *forests resulting from
climate change.
Some aspects regarding the limitations of the simulation exercises are worth mentioning.
First, the analysis does not take into account technological progress such as the
development of crop varieties with higher heat tolerance. In this sense, the results should be
interpreted as the potential farmers´ adaptation to climate change given the current
technological condition. Second, simulations do not consider the effect of carbon
fertilization. Increases in atmospheric CO2 concentration can have a positive impact on
crop yields by stimulating plant photosynthesis and reducing the water loss via plant
respiration. Higher agricultural productivity due to carbon fertilization and technical change
may offset the potential negative impacts of climate change on agricultural activities, and
therefore influence land conversion patterns. Such effects are not captured by our
simulations. In addition to that, the simulations assume that input and output relative prices
would not change with climate. However, changing relative prices may influence land
conversion patterns. For example, if farmers convert croplands to pasture when facing
warmer temperatures, crop prices could increase. The resulting higher agricultural
profitability could partly offset this crop-to-pasture conversion process. Since our
simulations do not capture such offsetting effects, results should be interpreted as upperbound estimates for converted areas3.
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
3
•
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 crops4, 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 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
wage, computed as the sum of the salaries paid to rural workers in a certain
municipality divided by the number of rural workers5. 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
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 regional climate model HadRM3P, developed by the Hadley
As observed by Mendelsohn and Seo (2007), the constant price assumption may be justified by the fact that
commodity prices are determined in world markets and regional changes are not a good predictor of global
changes.
4
These crops are rice, sugarcane, corn, beans, wheat, soybean, banana, cotton, potatoes, pepper, cocoa and
coffee.
5
In computing total rural wages, values were imputed for family labor.
Center, which provides data on temperature and precipitation at a 50 km horizontal
resolution for the Brazilian territory6.
•
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 allocation7.
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 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
Note: tmp30xxx and pre30xxx refer to the 1960-1996 temperature and precipitation averages, respectively.
The xxx notation correpond to the initials of the months of interest (i. e., djf = december/january/february).
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
6
A regional climate model is a downscaling tool that adds fine scale (high resolution) information to the
large-scale projections of a global general circulation model. The regional climate model HadRM3P is a
component of the PRECIS modeling system. The modeling experiment was conducted by the Instituto
Nacional de Pesquisas Espaciais (INPE).
7
Agronomic variables were constructed by Anderson and Reis (2007).
in cropland, while an increase in the cattle price is associated to an increase in total pasture
area. 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. This finding also highlights the importance of using climate
data sufficiently disaggregated with regard to time, since annual data could not be able to
capture such seasonal patterns. Finally, the Breusch-Pagan test rejects the hypothesis that
the residuals are uncorrelated. This means that land allocation decisions are interdependent
and therefore simultaneous equation methods provide 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 (high emissions) and B2 (low
emissions)8. For each emission scenario, we consider three timeslices: the predicted
average temperature and precipitation for the periods 2010-2040, 2040-2070 and 20702100. Using long-term climate averages has both pro and cons. The advantage consists in
reducing the uncertainty of climate model projections. On the other hand, working with
long-term averages prevents the assessment of the impacts attributed to extreme events.
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
8
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)
Because climate projections depend heavily upon future human activity, climate models are run against
different emission scenarios. The IPCC emission scenarios are based on different assumptions regarding
future greenhouse gas pollution, land use and other driving forces. The A2 scenario considers a very
heterogeneous world with continuously increasing global population and regionally oriented economic
growth, which is slower and less fragment than other scenarios. The B2 scenario considers a world in which
the emphasis is on local solutions to economic, social and environmental sustainability, with continuously
increasing population (lower than A2). The A2 scenario may be associated to a high emissions scenario, while
the B2 corresponds to a low emissions one. See Nakicenovic et at. (2000).
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, simulation results suggest that climate change may induce a
significant reduction in forestland. Depending on the emission scenario and time horizon,
deforestation ratios range between 15 percent and 20 percent of total forest area of rural
establishments. 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 deforestation pressure in the Amazon rainforest and therefore contribute to an
increase in greenhouse gas emissions.
Moreover, the spatial differences in climate change are likely to imply a heterogeneous
pattern of land use responses that vary by region. Simulation results suggest a conversion
from pasture to crops in the South region, which is characterized by fertile soils and cooler
temperatures. Such agroclimatic conditions make agricultural activities less vulnerable to
climate change. Actually, agronomic studies point out that rising temperatures in the South
may ease the introduction of tropical crops in the region9. Our predicted land use patterns
are in line with the lower vulnerability of the agricultural activities in the region.
On the other hand, our findings indicate a conversion from crops to pasture in the CenterWest region. This pattern is in line with previous studies that show that agricultural
productivity in Center-West region will be severely affected by climate change (Evenson
and Alves 1998; Féres et al. 2007; EMBRAPA 2008). Higher temperatures would reduce
the profitability of most crops, and farmers would convert cropland to degraded pasture
areas.
Finally, it should be remarked that our findings indicate that deforestation pressure would
not be restricted to the Amazon forest. Forest area reductions would be observed in all
Brazilian regions.
9
EMBRAPA (2008) indicates that climate change may have negative effects on Southeast coffee growing
areas, the most important Brazilian production region. Coffee production would migrate to the South region.
Agricultural productivity analysis
In order to check whether regional land use change patterns are in line with expected
changes in agricultural productivity, we estimated reduced form equations with the
following general specification
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. Similar to the land use model, we use quarterly climate data to capture
seasonality effects. The specified regressions included both linear and quadratic terms for
temperature and precipitation, in order to account for possible nonlinearities between
climate and productivity. Seven crops were analyzed: rice, sugarcane, beans, tobacco, corn,
soybeans and wheat. The results are showed in Table 4
In general, the North, Northeast and Center-West are the most negatively affected regions.
For the different scenarios and time horizons, our simulations suggest that climate change
may induce a decrease in average productivity for most crops in these regions. This is not
surprising, since temperatures in these areas are already near the upper bound of most
plants´ tolerance. Of particular concern is the estimated decrease in the average
productivity of subsistence crops (rice, beans, corn) in the Northeast region. The Northeast
rural area is predominantly occupied by small, low-income farmers that have little capacity
to adapt to climate change. Lower agricultural productivity due to climate change may have
significant socioeconomic impacts in the region.
On the other hand, simulations indicate a productivity increase for the vast majority of
crops in the South region. These findings corroborate previous agronomic estimates, which
suggest that prevailing agroclimatic conditions make agricultural activities less vulnerable
to climate change in the South region (EMBRAPA, 2008). Moreover, the productivity
gains provide some support to the conversion patterns predicted by our land use model.
Table 4: Average productivity variations for selected crops by Brazilian regions
A2 scenario
Rice
North
Northeast
Southeast
South
Center-West
B2 scenario
2010-2040
2040-2070
2070-2100
2010-2040
2040-2070
2070-2100
-26.6%
-28.9%
-1.3%
46.4%
-13.5%
-23.4%
-26.0%
-0.7%
44.4%
-12.3%
-9.9%
-11.0%
19.5%
8.2%
-12.1%
-30.3%
-27.1%
9.2%
48.5%
-14.1%
-26.8%
-24.3%
6.2%
46.2%
-14.4%
-9.9%
-15.4%
15.0%
5.8%
-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%
-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%
31.8%
-25.7%
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%
46.6%
-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%
Sugarcane
North
Northeast
Southeast
South
Center-West
Beans
North
Northeast
Southeast
South
Center-West
Tobacco
North
Northeast
Southeast
South
Center-West
Corn
North
Northeast
Southeast
South
Center-West
Soybean
North
Northeast
Southeast
South
Center-West
Wheat
North
Northeast
Southeast
South
Center-West
Conclusion
This paper aimed at evaluating the impacts of climate change on land use patterns in Brazil.
To this purpose, we specified and estimated 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 was estimated by a simultaneous equation method, in
order to account for the interdependence regarding land allocation decisions.
At the national level, simulation results suggest that climate change may induce a
significant reduction in forestland. Depending on the emission scenario and time horizon,
deforestation ratios range between 15% and 20% of total forest 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 therefore contribute to an increase in
greenhouse gas emission.
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, whose agroclimatic
features make agricultural activities less vulnerable to climate change, farmers may convert
pasture into cropland. On the other hand, simulations indicate a conversion from crops to
pasture in the Center-West region. This pattern is in line with previous studies that show
that agricultural productivity in Center-West region will be severely affected by climate
change.
It should be remarked that our findings indicate that deforestation pressure would not be
restricted to the Amazon forest. Forest area reductions would be observed in all Brazilian
regions. These findings suggest that policymakers should reinforce monitoring and control
activities regarding rural land use regulations. In particular, the enforcement of legal forest
reserve requirements and economic-ecologic zoning restrictions seem of fundamental
importance to offset deforestation pressures.
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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