Climate risks to Brazilian coffee production

Abstract

Brazil is the world’s leading coffee exporter, contributing billions of dollars to the global food economy. Yet, a majority of Brazilian coffee farms are operated by ‘smallholders’, producers with relatively small properties and primarily reliant on family labor. While previous work indicates that climate change will decrease the area suitable for coffee production in Brazil, no study has assessed the impacts of climate change on coffee yields or the relative exposure and vulnerability of coffee producing regions to changes in climate hazards (climate-associated losses in yield). To address these knowledge gaps, we assess the sensitivity of coffee yields to temperature and precipitation variation from 1974 to 2017 to map coffee climate hazards. Next, we identify which coffee producing regions in Brazil have the highest exposure to climate hazards due to high dependence of coffee production as a proportion of agricultural area. Finally, we generate a Vulnerability Index to identify which regions are theoretically least able to adapt to climate hazards. Our study finds that since 1974, temperatures in Brazilian coffee growing municipalities have been increasing by ~0.25 ◦C per decade and annual precipitation has been decreasing during the blooming and ripening periods. This historical climate change has already resulted in reductions in coffee yield by more than 20% in the Southeast of Brazil. Minas Gerais, the largest coffee producing state in Brazil, has among the highest climate hazard and overall climate risk, exacerbated by ongoing coffee expansion. Additionally, many municipalities with the lowest adaptive capacity, including the country’s mountainous regions, also have high climate exposure and hazards. Negative climate hazard and exposure impacts for coffee producing regions could be potentially offset by targeting climate adaptation support to these high-risk regions, including research, extension, and credit subsidies for improved coffee varieties, irrigation, and agroforestry and diversifying agricultural production.

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Environ. Res. Lett. 15 (2020) 104015 https://doi.org/10.1088/1748-9326/aba471
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LETTER
Climate risks to Brazilian coffee production
Ilyun Koh1, Rachael Garrett1,2, Anthony Janetos1,3and Nathaniel D Mueller4,5
1Department of Earth and Environment, Boston University, Boston 02215, United States of America
2Environmental Policy Lab, Department of Humanities, Social and Political Science and Department of Environmental Systems Science,
ETH Zurich, Sonneggstrasse 33, 8092 Zurich, Switzerland
3Pardee Center for the Study of the Longer Range Future, Boston University, Boston 02215, United States of America
4Department of Ecosystem Science and Sustainability, Colorado State University, Fort Collins 80523, United States of America
5Department of Soil and Crop Sciences, Colorado State University, Fort Collins 80526, United States of America
E-mail: rgarrett@ethz.ch
Keywords: climate change, agriculture, smallholder, vulnerability, Cerrado, Latin America, coffee
Supplementary material for this article is available online
Abstract
Brazil is the world’s leading coffee exporter, contributing billions of dollars to the global food
economy. Yet, a majority of Brazilian coffee farms are operated by ‘smallholders’, producers with
relatively small properties and primarily reliant on family labor. While previous work indicates that
climate change will decrease the area suitable for coffee production in Brazil, no study has assessed
the impacts of climate change on coffee yields or the relative exposure and vulnerability of coffee
producing regions to changes in climate hazards (climate-associated losses in yield). To address
these knowledge gaps, we assess the sensitivity of coffee yields to temperature and precipitation
variation from 1974 to 2017 to map coffee climate hazards. Next, we identify which coffee
producing regions in Brazil have the highest exposure to climate hazards due to high dependence
of coffee production as a proportion of agricultural area. Finally, we generate a Vulnerability Index
to identify which regions are theoretically least able to adapt to climate hazards. Our study finds
that since 1974, temperatures in Brazilian coffee growing municipalities have been increasing by
~0.25 ◦C per decade and annual precipitation has been decreasing during the blooming and
ripening periods. This historical climate change has already resulted in reductions in coffee yield by
more than 20% in the Southeast of Brazil. Minas Gerais, the largest coffee producing state in Brazil,
has among the highest climate hazard and overall climate risk, exacerbated by ongoing coffee
expansion. Additionally, many municipalities with the lowest adaptive capacity, including the
country’s mountainous regions, also have high climate exposure and hazards. Negative climate
hazard and exposure impacts for coffee producing regions could be potentially offset by targeting
climate adaptation support to these high-risk regions, including research, extension, and credit
subsidies for improved coffee varieties, irrigation, and agroforestry and diversifying agricultural
production.
1. Introduction
Coffee is highly valuable agricultural export for the
global South and accounted for US$846 billion of
global agricultural export value in 2017 (FAO 2018).
Although it is produced in a small area globally (11
million hectares) (FAO 2013), coffee production con-
tributes substantially to foreign exchange in produ-
cing countries (Akiyama 2001). In the last two dec-
ades, Arabica coffee farmers in Latin America have
endured numerous threats to their livelihoods due
to plummeting coffee prices, a shift in the power
of the supply chain structure away from produ-
cers toward consumer-facing companies (i.e. roast-
ers and retailers), and changing rural demographics
(i.e. out-migration and an aging population (Bacon
2005)). Coffee farmers in many regions now face
the risk of declining coffee yields and quality due
to global changes in temperature and precipitation
(Gay et al 2006, Schroth et al 2009, Zullo et al 2011).
Given the already low profit margins (and low farm-
gate prices) associated with coffee production (Bacon
© 2020 The Author(s). Published by IOP Publishing Ltd

Environ. Res. Lett. 15 (2020) 104015 I Koh et al
2005), declines in coffee yields could make existing
coffee producing regions both economically and bio-
physically unsuitable for production in the future
(Zullo et al 2006, de Sousa et al 2019).
The most suitable growing regions for Arabica
coffee are where the annual temperature average is
between 18 ◦C and 22 ◦C (Camargo 1985b) and in
mountain regions where the altitude is above 1000
or 1200 m (Schroth et al 2015). Both very high and
very low temperatures can result in yield and qual-
ity losses (Zullo et al 2011c, Camargo 1985b). Climate
change is expected to alter temperatures and precip-
itation, and increase extreme events in many coffee
producing regions throughout the world (L¨
aderach et
al 2010, Ovalle-Rivera et al 2015). These impacts will
undoubtedly compound the economic and demo-
graphic challenges currently faced by small-scale cof-
fee farmers.
Throughout the world, there is increasing evid-
ence that coffee farmers are already suffering from
the impacts of climate change (Baker and Haggar
2007, Haggar 2016). In their study of Mesoamerican
coffee producing regions, L¨
aderach and co-authors
concluded that by 2050, changes in temperature and
rainfall will decrease the area suitable for coffee pro-
duction by at least 40% (L¨
aderach et al 2010). In
Brazil, the world’s largest Arabica and Robusta cof-
fee producing region, climate change is predicted to
substantially reduce the amount of suitable area for
production (Zullo et al 2006). In the Brazilian state
of S˜
ao Paulo, for example, the proportion of climatic-
ally low-risk areas for coffee production may decrease
by ~20% by 2050 under a 1 ◦C increase in temper-
ature relative to the current climate and as much as
75% under a 5.8 ◦C increase (Coltri et al 2012). A
majority of the 308 000 coffee farms in the country are
operated by ‘family’ (or smallholder) farmers,6who
are potentially less capable of adapting to changes in
temperatures and precipitation (IBGE 2017a). In the
absence of adaptation, these shifts in climatic suitab-
ility are likely to substantially reduce coffee farmers’
incomes and potentially force them to abandon pro-
duction.
In this study, we assess the relative climate risk of
coffee producing regions in Brazil through the con-
cepts of hazard (potential harms to humans, infra-
structure, and ecosystem services), exposure (the
presence of humans, infrastructure, and ecosystem
services in places that experience hazards), and vul-
nerability (the propensity of a system to be negatively
affected by hazard) as defined by the IPCC (IPCC
6The legal definition (Lei 11.326/2006) of an ‘agricultor familiar’
is an agricultural producer with a property that: (i) is less than
four fiscal/tax modules in size, (ii) relies primarily on family labor,
(iii) meets a certain minimum threshold of how much income they
derive from their farm, and (iv) runs the farm with their family.
The specific size of a fiscal module varies across municipalities and
the threshold for income is defined by the executive branch (IBGE
2017a).
2014). The intersection of climate hazards, exposure,
and vulnerability influences the level of risk that a
community faces. Where risk is high, it is very likely
that losses in well-being will occur if steps are not
taken to reduce risk, either by reducing exposure to
the hazard or by increasing communities’ ability to
adapt to the changes induced by the hazard.
The impacts of climate hazard on crop yields, par-
ticularly annual crops, are becoming increasingly well
understood (Knox et al 2012). Yet, the impacts of cli-
mate hazard on farmers’ livelihoods are more diffi-
cult to assess due to a lack of longitudinal data about
changes in rural livelihoods. Despite these data limit-
ations, snapshots of the material measures of farmer
livelihoods and broader socio-economic conditions
do exist and can be used as a proxy to assess farm-
ers’ potential adaptive capacity in each region. Once
potential adaptive capacity is assessed, it is possible to
understand the degree to which high climate hazards
and exposure are likely to overlap with high vulner-
ability. In regions where there is high overlap between
hazards, exposure, and vulnerability, additional insti-
tutional support for climate adaptation will be most
urgently needed.
To date, few studies have combined analysis of
hazards, exposure, and risk at broad spatial scales.
Nor have any studies in Brazil focused on identify-
ing the relative vulnerability of different coffee com-
munities and how it intersects with climate hazards
and exposure. To contribute to this knowledge gap,
our paper has two aims (i) to assess spatial and tem-
poral variations in climate hazards, exposure, and vul-
nerability and (ii) to identify which regions have the
highest overall climate risk (overlap between high haz-
ards, exposure, and vulnerability).
To identify regions where coffee communities are
most at risk to climate change, our methodological
approach combines: (i) econometric modelling of
the historical relationship between climate and cof-
fee yield (climate hazard), (ii) mapping of coffee pre-
valence (climate exposure), and (iii) newly developed
municipal-level estimates of relative climate vulnerab-
ility, based on underlying socio-economic character-
istics. To draw inferences for the future, our analysis
focuses on changes in the overlap of hazards, expos-
ure, and vulnerability over time (2006 and 2017)
and space (coffee producing municipalities in Brazil)
using all available historical data.
2. Existing empirical work on climate risk
Many studies have analyzed climate hazards through
the lens of potential impacts on crop yields. These
studies draw on panel statistical models of histor-
ical weather data to explain the relationship between
climate change and crop production at the global,
national, and regional level, particularly for indi-
vidual annual crops (Lobell and Field 2007, Schlenker
and Roberts 2009, Schlenker and Lobell 2010, Butler
2

Environ. Res. Lett. 15 (2020) 104015 I Koh et al
and Huybers 2015). To date, the focus of most cli-
mate and agriculture studies has been annual crops,
such as maize, wheat, and rice, which are critical to
global food security (Knox et al 2012). There are
fewer studies of perennial species, which occupy less
area globally, yet represent an integral component of
many rural livelihoods (Samberg et al 2016, Hong
et al 2020).
The existing literature on coffee-related climate
change impacts and risks focuses heavily on ecolo-
gical niche modelling, often coupled with machine
learning techniques, to identify transitions in the suit-
ability of regions for coffee production (Davis et al
2012, Ovalle-Rivera et al 2015, Schroth et al 2015,
Bunn et al 2015, Pham et al 2019). Relatedly, several
studies determine changes in climate risk by assess-
ing to what degree future climate projections fall out
of the optimal temperature or water deficit range,
and exceed the frost probability threshold (Zullo et al
2006, 2011c, da Silva Tavares et al 2018). Statist-
ical assessments of the impacts of historical changing
temperatures and precipitation on coffee yields are
limited and thus far constrained to India, Mexico, and
Tanzania (Gay et al 2006, Craparo et al 2015, Jayaku-
mar et al 2016). These studies in India, Mexico, and
Tanzania all found significant negative yield impacts
from increasing temperatures and decreasing rainfall
(Gay et al 2006, Craparo et al 2015, Jayakumar et al
2016). To date no paper (to the best of our knowledge)
has empirically examined the relationship between
historical changes in coffee yield and historical cli-
mate change in Brazil. Instead, existing research on
the impacts of climate change on coffee production in
Brazil has focused on simulating potential changes in
yields (Verhage et al 2017) and climate zoning (Zullo
et al 2006, 2011, da Silva Tavares et al 2018).
The concepts of vulnerability and risk encom-
pass a wide variety of potential components, many
of which are difficult to measure, and few of which
have been analyzed for causal impacts. Given these
challenges, one leading empirical approach to both
vulnerability and risk research has been to develop
quantitative indices that vary over space and time
(Cutter et al 2003, Cutter and Finch 2008, Hahn et al
2009, Cutter et al 2010, Gbetibouo et al 2010, Pandey
and Jha 2012, Shah et al 2013, Ahsan and Warner
2014). These indices are based on existing theory and
available data and provide estimates based on a range
of variables that are theoretically likely to influence
societies’ ability to prepare for or adjust and respond
to stress and mediate risk. By quantifying the con-
ditions and assessing the variations in a single met-
ric, indices are particularly useful for comparing the
relative levels of social vulnerability over time and
space (rather than absolute vulnerability). For work
that spans larger geographical areas, rather than indi-
vidual communities, vulnerability and risk research
has highlighted the importance of the attributes of
place, and thus many indices focus on the aggregate
social aspects of a geographical location (Cutter 1996,
Cutter et al 2003).
Given that overall risk encompasses both phys-
ical stress and human’s capability to adapt, vul-
nerability indices tend to include both physical or
socioeconomic data (Harlan et al 2006, Johnson and
Wilson 2009). Whereas specifically ‘social’ vulnerab-
ility indices (e.g. Cutter et al 2003, Cutter and Finch
2008) encompass only socioeconomic and geograph-
ical characteristics. Since climate hazard and expos-
ure are measured seperately, our vulnerability index
focuses on the socio-economic dimensions of vul-
nerability. The interplay of climate hazard, expos-
ure, and vulnerability then determine overall climate
risk—the likelihood that climate hazards will negat-
ively impact human well-being.
3. Methods
3.1. Study region
Our analysis centers on the South, Southeastern, and
Center-west coffee producing states of Brazil (Goi´
as,
Mato Grosso do Sul, Paran´
a, Minas Gerais, S˜
ao Paulo,
Rio de Janeiro, Bahia, and Distrito Federal), which
produce approximately 90% of the country’s Arabica
coffee (IBGE 2017b) (figure 1). This region is expec-
ted to encounter substantial changes in climate; mean
annual temperature is expected to increase by 4 ◦C in
the summer and 2 ◦C to 5 ◦C in the winter by 2100 in
the RCP 8.5 emissions scenario (Pachauri et al 2014).
Higher temperatures are expected to reduce coffee
bean quality and generate more favorable conditions
for pests and diseases. Under all scenarios, the ideal
climatic conditions for coffee production are expec-
ted to shift to the south of Brazil (Zullo et al 2011).
Our unit of analysis is all of the municipalities in this
region that produce coffee and have data for all vari-
ables of interest (n =935). This is the smallest spatial
scale at which socioeconomic data are available across
the study region.
3.2. Assessing climate risk
Our methodological approach for determining over-
all risk follows the conceptual approach of the IPCC
(IPCC 2014)—a score for overall risk defined as the
sum of individual estimates of climate hazards, expos-
ure, and vulnerability:
Risk =Hazard +Exposure +Vulnerability.(1)
To generate this overall risk score, the estimates of
hazard, exposure, and vulnerability, explained below,
are converted into indices along a uniform scale (1–
5) using Jenks natural breaks and then summed (res-
ulting in a total value ranging from 0 to 15). We also
present results using breaks defined by Equal Weights
Intervals and Quantiles.
3

Environ. Res. Lett. 15 (2020) 104015 I Koh et al
Figure 1. Study region—major coffee producing states of
Brazil (Goi´
as, Mato Grosso do Sul, Paran´
a, Minas Gerais,
S˜
ao Paulo, Rio de Janeiro, Bahia, and Distrito Federal).
3.2.1. Climate hazard
To measure climate hazard, we examine how histor-
ical changes in precipitation and temperature have
influenced coffee yields. We used global gridded (0.25
degree) monthly average air temperature and total
precipitation data from 1974 to 2017 (the period for
which we also have data on municipality-level cof-
fee yields) by Willmott and Matsuura.7This dataset
interpolates weather station data and is drawn from
recent versions of the Global Historical Climatology
Network (GHCN version 2) and the Global Surface
Summary of Day archive. The version of the temper-
ature and precipitation data are 1900–2017 Gridded
Monthly Time Series V 5.01. From the dataset, the
gridded data are averaged across each coffee growing
municipality.
We average the gridded climate data at the
municipality level for each coffee phenological sea-
son. Given the differential exposure of coffee to
weather fluctuations across different phenological
stages (Camargo 2010), we separate out the blooming
(September to November), ripening (December to
May) and harvesting periods (June to August) for cof-
fee plants in the study region. The blooming period
is the time where coffee flower bud blooms, initi-
ated by the first rains after the dry season (Barros
et al 1999). During the blooming period, high tem-
peratures coupled with a lack of rainfall can impact
coffee flower buds (Camargo 1985a). High temper-
atures (above 23 ◦C) are also thought to be detri-
mental during the ripening period (when the coffee
7http://climate.geog.udel.edu/~climate/html_pages/download.
html#ghcn_T_P_clim.
beans develop and mature) and harvesting period, by
influencing both yield and quality (Camargo 1985a).
Finally, excess rain during the harvest period can
inhibit optimal harvesting.
The historical relationship between coffee yield,
temperature, and precipitation is examined using the
following panel econometric model:
Log(Yield)it =Tmp_bloomingit +Tmp_ripeningit
+Tmp_harvestingit
+Tmp_blooming2
it
+Tmp_ripening2
it
+Tmp_harvesting2
it
+Log(Pcp_blooming)it
+Log(Pcp_ripening)it
+Log(Pcp_harvesting)it
+Log(Pcp_blooming2)it
+Log(Pcp_ripening2)it
+Log(Pcp_harvesting2)it
+municipalityi+yeart+state_trendj
(2)
where Log(Yield) is the log of coffee yield (kg per
hectare) in municipality iand year t, which spans
from 1974 to 2017. Tmp_ is the average monthly
temperature and Log(Pcp_) is the log of the aver-
age monthly precipitation during a particular season.
Both the yield and precipitation variables are highly
non-normally distributed. This non-normal distri-
bution leads to heteroscedasticity in the relationship
between precipitation and yields. To avoid this prob-
lem, we log-transform both variables, including the
squared precipitation terms. The temperature vari-
ables are normally distributed so they are not log-
transformed. We included municipality and year fixed
effects, as well as a state (j) time trend, to control for
unobserved, non-climatic factors influencing yields.
Predicting yield on climate factors may be too
complex to describe using only linear relationships
(Watson 1963). Using the Ramsey RESET test, which
tests whether the model is missing important non-
linearities, we confirmed that the results supported
the use of quadratic terms for average temperature
and precipitation to specify a quadratic functional
form. In addition, a quadratic functional form gener-
ates optimum values by calculating the highest point
in the curve and enables better modeling of crop yield
responses to climate variables (Gay et al 2006).
We conducted a Hausman test to test whether
fixed effects or random effects are more suitable for
the panel data analysis. The p-value is significant at
the 99.9% level, supporting the use of fixed effects.
We include municipality fixed effects to account
for unobservable spatial variation and time-invariant
effects such as soil type and elevation (Welch et al
2010, Blanc and Schlenker 2017). This makes the
model less prone to omitted variable bias because
4

Environ. Res. Lett. 15 (2020) 104015 I Koh et al
Figure 2. Historical temperature (top panel, a-c) and precipitation (bottom panel, d-f) trends for the blooming period (a), (d),
ripening, (b), (e), and harvesting period (c), (f ) between 1974 and 2017. The regression line for the point data is indicated in blue,
with a confidence band of 95% in grey shading.
the unobserved factors that influence yield are con-
trolled. Additionally, the model estimators of the
fixed effects enable consistent estimation of the effect
of independent variables. Likewise, a year fixed effect
is added to control for time-varying shocks, such as
macroeconomic factors. After conducting a joint test
to find whether the dummies for all years are equal to
0, the F test was significant at 99.9% confidence level,
suggesting that year fixed effects are needed. Lastly, a
state- trend is used to control the broad regional pro-
ductivity improvements that may be occurring due to
technological change or changes in economic policies.
The state-specific technological trends control the
long-term trends in yields unrelated to weather vari-
ability.
The climate hazard of each municipality is
then defined by combining our coffee yield model
with historical trends in climate, following exist-
ing approaches to calculate the impacts of histor-
ical climate trends on yield (Lobell et al 2011, Butler
et al 2018). First, for each municipality, we calcu-
lated the temporal trends in temperature and pre-
cipitation within each phenological stage using a
linear regression. We then identify the net yield
impacts of these climate trends by using Eq. 2 to
model yields at the beginning and end of our 44-year
period, given state-year yield trends. The difference
in predicted yields due to climate is defined as the
climate hazard, with more negative yield impacts
indicative of a greater climate hazard. A limita-
tion of this approach is that it relies on a determ-
inistic model of climate-yield impacts, which may
not be as suitable as a flexible time series approach
for capturing climate-yield relationships, given the
heterogeneity in coffee yield trends and variability
across Brazilian states (figure S1 (available online at
stacks.iop.org/ERL/15/104015/mmedia)) (Agnolucci
and De Lipsis 2020).
3.2.2. Climate exposure
Our definition of climate exposure focuses on the
presence of rural livelihoods in places that are likely to
incur climate hazards. In case of rural coffee produ-
cing communities in Brazil, their exposure to climate
hazards is proxied by the prevalence of coffee produc-
tion in that region. As a whole, municipalities with a
large coffee cultivation area as a proportion of their
total area are likely to be more exposed to changes in
climate that impact coffee yields than municipalities
where coffee production occupies only a small pro-
portion of the agricultural area. To map exposure, we
generated a variable called coffee prevalence, defined
by the percentage of agricultural area that is in cof-
fee production using agricultural census data from
the Brazilian Institute for Geography and Statistics for
both 2006 and 2017.
3.2.3. Climate vulnerability
Here we develop a Vulnerability Index (VI) to charac-
terize spatial variability in factors likely to influence
communities’ propensity to be negatively affected
by climate change, following several prior studies
(Cutter et al 2003a, 2010, Cutter and Finch 2008,
Hahn et al 2009, Gbetibouo et al 2010, Pandey and
Jha 2012, Shah et al 2013, Ahsan and Warner 2014).
Prior social VIs have included age, race, health,
income, type of dwelling unit, employment of people
living within a region, but are not specific to rural or
5

Environ. Res. Lett. 15 (2020) 104015 I Koh et al
agricultural vulnerability. In our study, we develop a
VI that focuses on variables for which there is a clear
causal pathway linking the condition to the ability of
people in rural communities to prepare for, respond
to, or adapt to climate stresses. Due to data availab-
ility, our analysis focuses on average levels of vulner-
ability at the municipality level (aggregate statistics of
individual households within each municipality) in
regions where coffee is grown.
The factors considered in our VI include aver-
age household and property conditions (age, know-
ledge and social capital, technology, and household
and farm economy), as well as regional conditions
(infrastructure and yields) in 2006 and 2017. The
data source for the household and property condi-
tions is the Brazilian Agricultural Census (table S2),
while infrastructure comes from OpenStreeMap and
yields from the Brazilian Agricultural Municipal Sur-
veys. Variables included in the VI were selected based
on their theoretical and empirical impacts on vulner-
ability or its sub-components, from previous studies
(Kellerman 1983, Hahn et al 2009, Pandey and Jha
2012, Shah et al 2013, Garrett et al 2013). Specific jus-
tifications for each variable in the index are included
in the SI.
To generate the VI from these six groups of vari-
ables, we use a composite index approach, whereby
sub-components are first normalized and then aver-
aged at the group level (i.e. major component) and
then each group is averaged into a single index value.
The composite approach is a standard way of achiev-
ing a single numerical value when the amount of data
per group is unbalanced. It also allows the user to
weight each group differently based on their theor-
etical importance. This approach was chosen over a
PCA approach due to the small number of candidate
variables and the low correlation between them. The
SI discusses the justifications for the weightings used
in the main text results, as well as the results of a sens-
itivity analysis using different weightings. The sensit-
ivity analysis indicates that both weightings produce
the same distribution of relative vulnerability over
space, except that municipalities in Rio de Janeiro
have an even higher relative vulnerability using an
equal weights method over an approach that more
heavily weights baseline yields and household assets
(figure S3).
3.3. VI validation
To assess how well the VI is picking up important
socioeconomic variability for coffee production in
each municipality we examined its relationship to the
unexplained variance in the yield model (after con-
trolling for climatic factors). The results, which indic-
ate a moderate positive relationship between the VI
and unexplained yield variance, are explained in the
SI.
4. Results
4.1. Climate hazard
Since 1974, temperatures in Brazilian coffee growing
municipalities have been increasing by ~0.25 ◦C per
decade (figures 2(a)–(c)). Annual precipitation has
been decreasing during the blooming and ripening
period, and until 2002 during the harvesting period as
well (figures 2(d)–(f)). However, since 2002 precip-
itation during the harvesting period has been increas-
ing (figure 2(f)). This recent trend may help offset the
increasing temperature, sustaining the development
of coffee beans and preventing cherries from ripen-
ing too soon during the dry season.
Municipalities in the north of the study region
(Bahia, northern Goi´
as, and Minas Gerais) have the
highest mean temperature. Since 2010 mean temper-
atures in this region have frequently exceeded the
optimal range for Arabica coffee (>23 ◦C). Dur-
ing the flowering period temperatures in all states
increased by more than 1.2 ◦C. In Bahia, Minas
Gerais, and S˜
ao Paulo these large increases in tem-
perature were coupled with large decreases in rainfall
(>10% decrease).
Like past studies of annuals, we found that tem-
perature and precipitation increase yields up to a
point, but then detrimentally influence yields (table
S1). This concave relationship is constrained to
the blooming and ripening period. This supports
the hypothesis that excessively high temperatures
coupled with a lack of rainfall can inhibit both the ini-
tial flower budding and the development and matur-
ing of ripening beans. During the harvest period,
temperatures had a weakly convex relationship with
yields (the linear term was significant, large and neg-
ative, while the squared term was significant and
positive, but very small). This suggests non-linearly
increasing benefits to cooler temperatures during the
coffee harvest period, which contradicts established
agronomic understanding of temperature-yield rela-
tionships and may reflect a limitation of the model’s
structural form. Precipitation had a monotonically
positive relationship with yields.
The net impact of climate trends since 1974 have
been negative overall, with the biggest impacts con-
centrated in Minas Gerais. The average yield loss
ranged from 9% to 29% across the study region.
The differences in climate hazard across individual
municipalities within each state are separated into
quantiles and mapped in figure 3for climate trends
from 1974 to 2006 and 1974 to 2017.
4.2. Climate exposure
Municipalities with the highest coffee climate expos-
ure are clustered in Minas Gerais (figure 3). This state
has the highest ratio of coffee area as a proportion of
crop area. Between 2006 and 2017, climate exposure
6

Environ. Res. Lett. 15 (2020) 104015 I Koh et al
Figure 3. Climate risk by component (top three panels) and cumulative risk (bottom panel) for each coffee producing
municipality in 2006 (left) and 2017 (right). There are 935 municipalities in the study region with data for all variables across
both time periods. Grey areas indicate municipalities that do not produce coffee or were missing data and were not included in
the study. BA =Bahia, DF =Federal District of Brazil, GO =Goi´
as, MG =Minas Gerais, MS =Mato Grosso do Sul,
PR =Paran´
a, RJ =Rio de Janeiro, SP =S˜
ao Paulo. The classes (and associated scores) presented here were created using
quantiles to transform the continuous data for each component. The use of quantiles was more illustrative then other methods,
e.g. Jenks natural breaks and equal length intervals, as quantiles maximized the variation across municipalities.
in the southern region of Brazil, including Paran´
a and
S˜
ao Paulo, decreased because these states have been
diversifying their cropping systems and reducing their
reliance on coffee.
4.3. Climate vulnerability
On average, we observe that northern municipalit-
ies in Minas Gerais, and Rio de Janeiro have the
highest vulnerability due to lower baseline coffee
yields, knowledge and social capital, and access to
technical assistance, as well as poor transportation
infrastructure (figure 3) (see SI, figure S2 for more
details). These results are largely robust to an alternate
component weighting method that treats weights for
household age structures and local infrastructure as
equal to social, knowledge, and financial household
assets and yields. Only the relative vulnerability of
Rio de Janeiro is systematically different under the
two methods (figure S3). Under the equal weighting
scheme, the relative vulnerability in Rio de Janeiro
would be lower.
4.4. Overall risk (overlap of climate hazard,
exposure, and vulnerability)
We find that Minas Gerais and Rio de Janeiro have
the highest mean risk under all classification systems
7

Environ. Res. Lett. 15 (2020) 104015 I Koh et al
Figure 4. Mean state climate risk in 2006 and 2017 by classification method (Jenks natural breaks, equal intervals, and quantiles)
and component (hazard, exposure, and vulnerability). Overall risk declined between 2006 and 2017 in nearly all regions, largely
due to decreases in exposure (reduced dependence on coffee production as a rural livelihood).
due to the combination of high exposure, hazard, and
vulnerability (figures 3and 4). Paran´
a and Goaís have
the lowest mean climate risk due to a combination of
low exposure, hazard, and vulnerability.
Cumulative risk decreased in all states except for
Goi´
as and Minas Gerais, largely due to decreases
in exposure. This decline in risk is accentuated
when equal length intervals are used (shown in
8

Environ. Res. Lett. 15 (2020) 104015 I Koh et al
Figure 5. Change in mean state climate risk between 2006 and 2017 by component (hazard, exposure, and vulnerability) using the
equal length interval classification. Equal length intervals are displayed because they produced the greatest changes between 2006
and 2017. BA =Bahia, DF =Federal District of Brazil, GO =Goi´
as, MG =Minas Gerais, MS =Mato Grosso do Sul,
PR =Paran´
a, RJ =Rio de Janeiro, SP =S˜
ao Paulo.
figure 5). Since each component of risk is a relat-
ive score, this means that the number of municip-
alities that fell into moderate to very high vulner-
ability and hazard classes in most states decreased
over time. Decreases in mean climate exposure
were partially offset by increases in vulnerability
in several states. Risk decreased the most in Mato
Grosso do Sul and Paran´
a through reductions in
exposure.
5. Discussion and limitations
While prior climate zoning studies forecast major
reductions in the area suitable for Arabica coffee
production in Brazil by 2050 (Zullo et al 2006,
2011), our study shows that historical climate change
is already having a substantial negative impact on
yields. However, this climate hazard is not evenly
distributed. Worryingly, it is concentrated in the
Southeast of Brazil, including Minas Gerais, the
region with the highest Arabica coffee produc-
tion. Little adaptation has occurred in the form of
reducing exposure by diversifying away from cof-
fee production. In contrast, the states in Brazil
that had the lowest climate hazard did reduce
their exposure to climate hazards via agricultural
diversification.
Differences in all aspects of climate risk mirror
biophysical conditions and rural development levels.
Many of the mountainous regions in Brazil, which
have the highest climate risk, rely heavily on cof-
fee production as a farming activitiy due to the high
slope, lack infrastructure and rural services, and lack
of other economic development opportunities (Wat-
son and Achinelli 2008). Mountainous regions also
tend to experience more dramatic shifts in climate
than flatter areas, explaining higher climate hazards
(Diffenbaugh and Giorgi 2012).
Yet, our municipality-level estimates of climate
hazards and vulnerability may not accurately rep-
resent the conditions for all farms in each region.
Across all data sources, the mismatch between sub-
municipal areas and municipal averages is likely to be
larger in regions with greater variation in altitude, ori-
entation, slope, road density, and distance to muni-
cipal centers. Additionally, measurement error, par-
ticularly with respect to the agricultural censuses, may
be larger in more remote regions where it may not be
possible to interview all farmers. Another limitation
of this work is that it relies heavily on existing under-
standing of the correlates of climate vulnerability in
rural regions to construct the vulnerability index. In
much of Brazil, and for coffee specifically, the causes
of climate vulnerability are poorly understood. Future
studies should supplement this research by analyzing
the mechanisms underlying coffee farmers’ vulnerab-
ility, including through in-depth field interviews.
Since livelihood risks are not constrained to a
single crop, future work should examine climate risks
across the whole livelihood portfolio of rural house-
holds, including off-farm activities. Climate hazards
may damage people’s ability to maintain or secure
material assets and resources, as well as their abil-
ity to live a ‘good life’ in other ways, including non-
material goals (Chambers 2013). However, it is also
possible that climate change could create new oppor-
tunities for regions that become more favorable for
coffee farming or new crops, such as cocoa (de Sousa
et al 2019). Further in-depth fieldwork is urgently
needed to investigate these issues in the Brazilian con-
text.
6. Conclusion
In this study we sought to quantitatively assess the
spatial and temporal variation in the climate risk of
coffee communities in Brazil. Unlike past work, we
9

Environ. Res. Lett. 15 (2020) 104015 I Koh et al
measure climate hazard, exposure, and vulnerability
independently and then identify where they overlap
to increase overall risk. This approach allows us to
assess which regions have the highest overall risk, as
well as the major sources of that risk.
Our study finds that since 1974, temperatures
in Brazilian coffee growing municipalities have been
increasing by ~0.25 ◦C per decade and annual pre-
cipitation has been decreasing during the blooming
and ripening periods. This historical climate change
has already resulted in reductions in coffee yield by
more than 20% in the Southeast of Brazil. The South-
east, particularly Minas Gerais, is the largest coffee
producing region in Brazil, so this high climate haz-
ard translates into high overall climate risk for much
of the country’s core coffee producing regions. In
the mountainous Brazilian highlands, where farm-
ers are generally poorer and more disconnected from
markets, the risk posed by high climate hazards and
exposure is exacerbated by high vulnerability.
Our results provide useful information for the tar-
geting of agricultural policies and climate planning.
They indicate that federal and civil society efforts
to prepare for climate change in coffee production
regions should focus on Minas Gerais, where a major-
ity of production occurs, rural economic dependence
on coffee is very high, and climate hazards and vul-
nerability are the highest. Minas Gerais has already
experienced high climate hazards from rising tem-
peratures and declining precipitation in the bloom-
ing and ripening period. Improved coffee variet-
ies, agricultural loans for irrigation and agroforestry
might enable coffee farmers to maintain or improve
their yield under climate hazard, while infrastruc-
ture development and capacity building within exist-
ing cooperatives could help increase access to higher
value marketing opportunities to offset lower yields.
Given its high climate vulnerability, but lower haz-
ard, climate adaptation strategies for the more moun-
tainous Eastern Minas Gerais could focus on broader
development interventions, such as improved ser-
vices, infrastructure, and market access. In contrast,
climate adaptation strategies in Southwestern Minas
Gerais would be better off focusing on diversifying
production, off-farm income diversification, increas-
ing access to irrigation, or expanding climate-related
crop insurance.
Acknowledgments
We thank the three anonymous reviewers for their
careful comments, which substantially improved the
manuscript. This work would not have been possible
without the generous contributions of our co-author
Anthony Janetos, who sadly passed away earlier this
year. We further thank Mark Friedl, Anne Short
Gianotti, Robert Kaufmann, and Ian Sue Wing for
their suggestions and comments on earlier versions
of this research. This research was supported by the
Fulbright Foundation NEXUS Program for the
Western Hemisphere, the Summer Fellows program
at the Boston University Fredrick S. Pardee Center for
the Study of the Longer-Range Future and the Boston
University Global Development Policy Center Land
Use and Livelihoods Initiative.
Data availability
The data that support the findings of this study are
available from the corresponding author upon reas-
onable request.
ORCID iDs
Rachael Garrett https://orcid.org/0000-0002-
6171-263X
Nathaniel D Mueller https://orcid.org/0000-0003-
1857-5104
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