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RESEARCH ARTICLE
Multiclass Classification of Agro-Ecological
Zones for Arabica Coffee: An Improved
Understanding of the Impacts of Climate
Change
Christian Bunn
1
*, Peter Läderach
2
, Juan Guillermo Pérez Jimenez
1
,
Christophe Montagnon
3
, Timothy Schilling
3
1International Center for Tropical Agriculture (CIAT), Km 17, Recta Cali-Palmira, Apartado Aéreo, 6713,
Cali, Colombia, 2International Center for Tropical Agriculture (CIAT), Hotel Seminole, 2 Cuadras al Sur,
Managua, Nicaragua, 3World Coffee Research, 578 John Kimbrough Blvd, College Station, Texas, 77843–
2477, United States of America
*Christian.bunn@gmail.com
Abstract
Cultivation of Coffea arabica is highly sensitive to and has been shown to be negatively
impacted by progressive climatic changes. Previous research contributed little to support
forward-looking adaptation. Agro-ecological zoning is a common tool to identify homolo-
gous environments and prioritize research. We demonstrate here a pragmatic approach to
describe spatial changes in agro-climatic zones suitable for coffee under current and future
climates. We defined agro-ecological zones suitable to produce arabica coffee by clustering
geo-referenced coffee occurrence locations based on bio-climatic variables. We used ran-
dom forest classification of climate data layers to model the spatial distribution of these
agro-ecological zones. We used these zones to identify spatially explicit impact scenarios
and to choose locations for the long-term evaluation of adaptation measures as climate
changes. We found that in zones currently classified as hot and dry, climate change will
impact arabica more than those that are better suited to it. Research in these zones should
therefore focus on expanding arabica's environmental limits. Zones that currently have cli-
mates better suited for arabica will migrate upwards by about 500m in elevation. In these
zones the up-slope migration will be gradual, but will likely have negative ecosystem
impacts. Additionally, we identified locations that with high probability will not change their
climatic characteristics and are suitable to evaluate C.arabica germplasm in the face of cli-
mate change. These locations should be used to investigate long term adaptation strategies
to production systems.
PLOS ONE | DOI:10.1371/journal.pone.0140490 October 27, 2015 1/16
OPEN ACCESS
Citation: Bunn C, Läderach P, Pérez Jimenez JG,
Montagnon C, Schilling T (2015) Multiclass
Classification of Agro-Ecological Zones for Arabica
Coffee: An Improved Understanding of the Impacts of
Climate Change. PLoS ONE 10(10): e0140490.
doi:10.1371/journal.pone.0140490
Editor: Juan A. Añel, Universidade de Vigo, SPAIN
Received: June 8, 2015
Accepted: September 25, 2015
Published: October 27, 2015
Copyright: © 2015 Bunn et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any
medium, provided the original author and source are
credited.
Data Availability Statement: According to CIAT's
institutional policy, all raw data are available on
dataverse: http://thedata.harvard.edu/dvn/dv/CIAT.
Funding: World Coffee Research funded this
research through the project "Identifying Long Term
Variety Trial Locations, Provide Climate Information to
Support World Coffee Research Variety Trials and
Support on Trial Data Analysis." http://
worldcoffeeresearch.org/who-we-are/contact-us.
World Coffee Research was represented by CM and
TS and continuously interacted with CB and PL to
design, implement and evaluate the research. CM
and TS from World Coffee Research supported the
Introduction
Most of the world's coffee comes from the perennial tree Coffea arabica [1], plantations of
which are productive for 20–50 years. Climate controls where coffee can be grown [2]. Arabica
coffee requires a climate with annual mean temperatures of about 20°C and over 1200 mm
annual rainfall to be economically viable [2]. Temperatures over 30°C for extended periods
reduce yields[3], while frost for a few days damages or even kills the plant [4]. A short dry
period of less than 40mm precipitation per month increases yield and promotes uniform flow-
ering, but more than 3 dry months reduces yield [5].
The livelihoods of about 25 million small producers globally depend on arabica coffee [6].
Because arabica requires a specific climate within narrow limits, these growers will see both
yield and quality fall as the climate changes. Rising temperatures were predicted to reduce
yields below economic viability in Veracruz, Mexico by 2020 [7]. For Chiapas in Mexico pro-
jections suggest that rising temperatures will eliminate coffee below 1100 masl by 2050 [8]. In
the center of origin of C. arabica, the Ethiopian highlands, the climatic niche of the indigenous
varieties may disappear by 2080 [9]. Brazil, which produces a third of the global crop of arabica
coffee, is projected to lose up to 95% of the suitable area by 2100 [10]. A study of the global
impact of climate change predicted that the area suitable for arabica coffee will be reduced by
50% by 2050, mostly caused by higher temperatures [11]. A more recent study showed that
these impacts vary by region [12].
It is a challenge to develop unambiguous strategies to adapt to the projected changes in cli-
mate. For well researched crops like wheat, rice or maize, mechanistic simulation models are
available to estimate the effects of changed climate on crop performance [13]. They can also be
used to forecast how agronomic management might be adapted to changed conditions. The
model available for coffee (Caf2007), however, could not be applied on larger spatial scales due
to limited spatial data [14], limited knowledge of the crop’s physiology [15], and its specifica-
tion for plot scale application [16].
Alternatively, the Veracruz [7] and Brazil [10] studies extrapolated known climatic limits of
arabica coffee in their respective regions to forecast the distribution of future climates suitable
for arabica coffee. While useful, the results could not be readily extrapolated globally. Other
studies used larger data sets and methods of machine learning to predict the spatial distribution
of coffee species [11,12,17]. As applied to coffee, these analyses relied on whether a site’s future
climate lay within the range of those that determine current distribution. The models estimated
the future distribution of arabica coffee with high confidence, but they only considered a binary
distinction between suitable and unsuitable climate. This allowed the reliable identification of
locations that will likely transition to other crops in the future. However, to guide adaptation
research a better identification of the climatic characteristics of the impacted regions will be
necessary. Furthermore, in regions that remain suitable guidance is needed to distinguish
zones that will require systemic or incremental adaptation measures [18].
To address these issues, we chose an agro-ecological zoning (AEZ) approach. AEZs for arab-
ica coffee were defined in Brazil using overlay maps of limiting climatic factors [19], or by clus-
ter analysis of several variables in Colombia [20]. In viticulture future changes in wine growing
zones have been projected using the Random Forest (RF) algorithm on local [21] and continen-
tal scale [22]. We extended this application to global scale to analyze how the different climates
of each coffee AEZ will be affected by climate change. We used this analysis to suggest options
to adapt and to identify homologous sites to facilitate technology transfer.
A recent review concluded that a globally-coordinated breeding program was needed to
confront the negative impacts of climate change[23]. We therefore show how the AEZ
Arabica Agro Ecological Zones and Climatic Change
PLOS ONE | DOI:10.1371/journal.pone.0140490 October 27, 2015 2/16
manuscript preparation with comments. World Coffee
Research is a 501 (c)(5) non-profit, collaborative
research and development program of the global
coffee industry to grow, protect, and enhance
supplies of quality coffee while improving the
livelihoods of the families who produce it. The
program is funded and driven by the global coffee
industry, guided by producers, executed by coffee
scientists around the world and supported by the
Norman Borlaug Institute, part of Texas A&M
University.
Competing Interests: The authors have declared
that no competing interests exist.
approach might be used to select sites for multi-location variety trials that such a program
would require.
Data and Methods
We used WorldClim’s bioclimatic variables [24] to define AEZs suitable for arabica coffee.
Although soil attributes, aspect, and local microclimate determine crop performance at local
scales, they are unimportant in defining the global distribution of AEZs. We assembled a data-
base of geo-references of sites where C.arabica is currently grown throughout the world. On
the climate data for these sites we then used cluster analysis to define the AEZs that are suitable
to grow arabica coffee. Next we trained the RF algorithm on the AEZ definition as response
variable and the climate variables as independent variables. These RF models were extrapolated
on maps of both current and future climates to predict the changes that each AEZs will con-
front with climate change. As a demonstration of how the method might be used, we identified
possible sites for an international multi-location variety trial (IMLVT).
Database of locations of arabica coffee
We used data of the current distribution of arabica coffee to define the climates that are suitable
for cultivation. The data came from four sources:
1. Geo-referenced coffee farms from a database of the location of 100,000 farms developed by
the International Center for Tropical Agriculture (CIAT) and its collaborators [11];
2. Geo-referenced municipalities in Brazil that produce arabica coffee [25]; Similarly, we pro-
duced a set of 5,666 locations for Indonesia by sampling within polygons where we knew
arabica is grown and stratification based on region-specific ranges of altitude [17]
3. For those regions where neither (1) nor (2) were available, we identified coffee plantations
from Google Earth images [11]; and
4. The Global Biodiversity Information Facility [26].
The raw database contained a total of 124,820 geo-referenced locations growing C.arabica.
Because coffee farms are often small, to avoid spatial bias we reduced the database to unique
pixel cells on a 5 arc-minute grid, which we call “occurrence pixels”. We stratified the database
to contain only locations with elevations above 100 masl. We also removed as outliers sites for
which one or more environmental variable exceeded 3.5 standard deviations from the mean.
Fig 1 shows the distribution of the 3545 occurrence pixels in the final dataset together with the
distribution of arabica area harvested.
Climate data
For the current climate (1950–2000), we used the WorldClim data set at 5 arc-minute resolu-
tion [24]. WorldClim provides data of monthly precipitation, mean monthly minimum and
maximum temperatures, and 19 bioclimatic variables derived from these data. We comple-
mented the latter with another derived variable of consecutive months with less than 40mm
precipitation (Table 1). As we point out in the introduction, a short dry season increases yields
but dry periods longer than three months reduce yields or require irrigation.
To predict climate in the period 2040 to 2069 (2050s), we used 19 global climate models
(GCMs) from the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate
Change (IPCC) [28]. We chose the representative concentration pathway (RCP) 6.0, an inter-
mediate scenario in which radiative forcing continues to increase until the end of the century
Arabica Agro Ecological Zones and Climatic Change
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[29].We downscaled the GCM outputs using the delta method [30], which computes the differ-
ence between model outputs for current conditions and the 2050s. We interpolated these data
Fig 1. Distribution of occurrence pixels (5 arc minutes, red dots). The brown colors indicate producer groups by area of arabica coffee harvested in each
country [27].
doi:10.1371/journal.pone.0140490.g001
Table 1. Bioclimatic variables used and mean values at occurrence pixels under current and 2050s conditions; the values for 2050s were calcu-
lated as mean impact across 19 GCMs.
Type Bioclimatic
variable
Description Current
mean
2050s
mean
Unit Clustering
1
BIO 1 Annual mean temperature 20.6 22.5 °C
BIO 2 Mean diurnal range (mean of monthly (max temp—min
temp))
11.6 11.7 °C
BIO 3 Isothermality (BIO2/BIO7) (*100) 72 71 - X
BIO 4 Temperature seasonality (standard deviation *100) 136.9 143.9 °C
BIO 5 Max temperature of warmest month 28.5 30.5 °C X
Temperature BIO 6 Min temperature of coldest month 12.2 13.8 °C X
BIO 7 Temperature annual range (BIO5-BIO6) 16.3 16.7 °C
BIO 8 Mean temperature of wettest quarter 21.6 23.4 °C
BIO 9 Mean temperature of driest quarter 19.3 21.2 °C
BIO 10 Mean temperature of warmest quarter 22.1 24.0 °C
BIO 11 Mean temperature of coldest quarter 18.7 20.4 °C
BIO 12 Annual precipitation 1637 1645 mm X
BIO 13 Precipitation of wettest month 280 289 mm
BIO 14 Precipitation of driest month 33 31 mm
Precipitation BIO 15 Precipitation seasonality (coefficient of variation) 66 67 -
BIO 16 Precipitation of wettest quarter 739 749 mm
BIO 17 Precipitation of driest quarter 122 121 mm
BIO 18 Precipitation of warmest quarter 492 479 mm X
BIO 19 Precipitation of coldest quarter 211 224 mm
BIO 20 Number of consecutive months <40mm precipitation 2.5 2.6 - X
1
X = variables used for agglomerative clustering.
doi:10.1371/journal.pone.0140490.t001
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to 5 arc-minutes resolution and applied them to the WorldClim data for current climate and
recalculated the bioclimatic variables (Table 1).
AEZs for arabica coffee
We transformed all 20 bioclimatic variables to z-scores. For those variables that were highly
correlated (Pearson coefficients |r|>0.7), we kept the one that we judged most informative in
the coffee context and discarded the others.
We then performed an agglomerative cluster analysis on the occurrence pixels using the
Ward algorithm in the statistics software R [31]. We determined the final number of clusters
using the indices of Ratkowsky and Lance [32] and Calinski and Harabasz [33] and by judging
the dendrogram of distances between clusters. From the clusters we described AEZs classified
by the bioclimatic variables that define each of them. We tested statistical significance of differ-
ences of climate data between the AEZs by one-way analysis of variance in R [31]. We based
the AEZ descriptions on the differences of the group means from the grand mean and calcu-
lated their confidence intervals using R’s multcomp package [34].
Current and future spatial distribution of AEZs for arabica coffee
We used the Random Forest package [35] to classify the climate in each pixel into AEZs for
arabica coffee. We then examined the spatial distribution of each of the defined AEZs to assess
the climate of global coffee growing regions, and to evaluate how climate change will affect
them.
The Random Forest package creates an ensemble of decision trees and selects the mode of
the individual trees, which reduces the risk of generating an overconfident classification (over
fitting) [35]. We trained the algorithm with random samples of occurrence pixels within the
AEZs and a random background sample of pixels within coffee-producing countries that did
not have coffee. From the occurrence pixels in each AEZ group, we selected samples the same
size of the smallest AEZ group and used 2.5 times as many background samples. For binary
classification problems a 1:1 sampling ratio is recommended to avoid the preferential predic-
tion of the majority class [36]. The sampling ratio we chose accounted for the trade-off between
the multi-class AEZ classification, as well as the binary classification in to suitable classes and
unsuitable background locations. Additionally, we constrained the background samples to con-
tain only pixels with annual mean temperatures within the range as the occurrence pixels to
exclude unfeasible locations [36]. We used all 20 bioclimatic variables as independent variables
(Table 1). For each RF model we grew 1000 decision trees with seven variables selected at each
node and replicated this process three times.
To make the process more robust, we divided the training sample into five random groups
and trained the package five times, withholding one of the groups each time. In summary, we
drew three random samples from the entire population of occurrence pixels, from each of
which we drew five random subsamples to give 15 individual models trained. We extrapolated
these onto maps of the 20 bioclimatic variables and determined the modal value across all 15
model results. We obtained maps of each AEZ plus class “0”background sites where Arabica
coffee is unlikely to be cultivated. We repeated the process for the 2050 data. The most likely
future AEZ was determined for each pixel by the mode across the results for the 19 GCMs.
To evaluate the classification we used the area under receiver operating characteristic curve
(AUC), which has values 0–1. An AUC of 0.5 indicates that the performance was no better
than random sampling, while 1.0 is perfect classification. We used the standard AUC to evalu-
ate the capacity of individual models to correctly discriminate occurrence pixels from the back-
ground sample. The definition of the AUC measure can be extended to multiclass problems by
Arabica Agro Ecological Zones and Climatic Change
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averaging all pairwise AUC comparisons to a multiclass AUC [37]. We used this measure to
evaluate the discrimination of AEZs by the models.
Example: Identify potential trial sites
We classified pixels to be appropriate sites for variety evaluation in the long-term if the charac-
teristics of the climate were stable over time and the classification was unambiguous. We there-
fore defined three conditions:
1. All repeats of the RF classification step had to agree on the AEZ classification under current
conditions;
2. All modal classifications across the 19 GCM’s had to agree, and
3. The classifications of (1) and (2) had to be identical in both time steps.
We produced additional maps that indicated sites with less stringent classification if there
was 80% agreement with the conditions above.
Results
AEZs for arabica coffee production
Among the temperature factors the annual mean temperature and the annual total precipita-
tion are most frequently mentioned in the literature to describe the spatial distribution of cli-
matic suitability for coffee production (e.g. [5]). Furthermore, maximum and minimum
temperatures were shown to be influential, but also temperature variability [15].
At the 3545 occurrence pixels of C.arabica Bio5 (the mean maximum temperature of the
warmest month) and Bio6 (the mean minimum temperature of the coldest month) were corre-
lated with Pearson coefficients |r| = 0.42. This was below the threshold (|r|>0.7), so we
included them for clustering. In contrast, Bio1 (the annual mean temperature) correlated with
several other temperature variables so we excluded all of them. Of the variables that represent
temperature variability, Bio3 correlated least with Bio5 and Bio6. It is the ratio of the mean
monthly temperature range to the annual range (Bio2/Bio7100) (|r|0.4), and we included it
in the analysis (Table 1).
The literature identifies annual precipitation (Bio12) and the length of the dry season
(Bio20) as the most important precipitation-related variables that influence the yield of coffee.
Their Pearson coefficient was |r| = 0.56 so we included them in the analysis. In general, coeffi-
cients among precipitation variables were high and we excluded them. The exception was vari-
able Bio18, the precipitation of the warmest quarter, which had acceptable coefficients with
both Bio12 (|r
BIO12
| = 0.45) and Bio20 (|r
BIO20
| = 0.21) and low coefficients with most other
precipitation variables; we therefore included it in the analysis (Table 1).
We obtained five distinct agro-ecological zones (AEZs, Fig 2) for arabica coffee within the
3545 occurrence pixels based on six standardized bioclimatic variables. We described the AEZs
in terms of their climatic characteristics. For some better insight into of Bio3 we included the
related variables Bio2 (Mean diurnal range) and Bio7 (Annual temperature range).
One way analysis of variance showed highly significant differences between all AEZs for all
variables (at p <0.001). We summarized the five AEZs as follows (the list numbers correspond
to the groups in Fig 2):
1. “Hot-wet”(HW), characterized high maximum temperature in the warmest month, high
annual precipitation, a short dry season and a humid warmest quarter of the year (Fig 3).
Arabica Agro Ecological Zones and Climatic Change
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2. “Constant”(Ct), characterized by lacking temperature seasonality. It had the highest iso-
thermality, and the lowest mean values for daily and annual temperature range. Ct’s precipi-
tation is similar to the HW zone with high annual precipitation and a short dry season (Fig
3).
3. “Hot-dry”(HD), characterized by high maximum temperatures were high and no cold
month. The annual total precipitation is low and has the lowest precipitation in the warmest
quarter of all groups and a long dry season (Fig 3).
The last two groups of occurrences are characterized by low minimum temperatures.
5. “Cool-variable”(CV) characterized by the highest annual temperature range, and the lowest
mean isothermality. Precipitation is moderate.
6. “Cool-dry”(CD), characterized by the lowest minimum temperature of the coldest month
but also the lowest annual precipitation with a long dry season.
Current and future spatial distribution of clusters
The RF classification gave maps of current distribution of the AEZs (Fig 4) and the changes
that climate change will bring as forecast by the 19 GCMs (Fig 5).
The Ct AEZ is mostly in highlands close to the equator: Colombia, Ethiopia, Kivu lake
region in Central Africa, Kenya and Indonesia (Fig 4B, 4D and 4E). Regions towards the latitu-
dinal margins were characterized by more variability. Southern Brazil was mostly dominated
by the related AEZs HD, CV and CD. The latter two AEZs show a high seasonal temperature
Fig 2. Dendrogram of agglomerative clustering. On the vertical axis is Euclidean distance. The final definition of clusters is indicated by the color codes:
Green-“Hot-Wet”, Violet-“Constant”, Red-“Hot-Dry”, Yellow-“Cool-Variable”, Turquoise–“Cool-Dry”.
doi:10.1371/journal.pone.0140490.g002
Arabica Agro Ecological Zones and Climatic Change
PLOS ONE | DOI:10.1371/journal.pone.0140490 October 27, 2015 7/16
variation and low minimum temperatures. While in southern Brazil the CV AEZ has a long
dry season, CD AEZ has lower maximum temperatures and a shorter dry season. In central
Brazil, the HD AEZ region has high maximum temperatures and a strong dry season (Fig 4A).
In the Central America region all AEZs were represented but HW and HD with high maximum
temperatures are most prevalent. Some regions experience high rainfall, however, while others
are characterized by lower precipitation and longer dry seasons. Southern Mexico was typical
for the HW AEZ with a hot and wet climate. Nicaragua was representative of the HD AEZ with
a long dry season and high temperatures. Costa Rica is an exception in this region with most of
its coffee region being of Ct climate with stable and moist conditions (Fig 4C).
By 2050, the spatial distribution of AEZs changes little, with Ct still mostly around the equa-
tor. The extent of the distribution will be reduced, however, especially in Brazil’s coffee growing
regions, with most of the HD AEZ pixels becoming unsuitable. The CD AEZ will also be
reduced substantially, especially in western São Paulo state. The areas that are currently in the
CV AEZ zone largely remain of the same climate. There will not be any migration to the south
(Fig 5A). Changes in Central America will be similar to those in Brazil with most of the HD
AEZ mostly becoming unsuitable. The location of the other AEZs persists but reduced extent
in extent (Fig 5C).
Under current conditions, arabica coffee grows on 7.2% of the total pixels in the latitudinal
belt 30°N–33°S. The Ct AEZ accounts for 26% of all suitable pixels, which is a little more than
the HD AEZ with 25%. The other three AEZs shared the remainder 49%. The total share of
suitable pixels in the 30°N–33°S belt will be halved to 3.6% by the 2050s. The Ct AEZ will be
Fig 3. Confidence intervals of group compared with the grand mean for descriptor variables. Group labels are the cluster numbers (C 1–5) in the
dendrogram (Fig 2). The dashed line is the grand mean, the value for which is given at the bottom.
doi:10.1371/journal.pone.0140490.g003
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less affected by climate change than the others and will make up for 34% of the area that
remains suitable for arabica coffee. Most of the loss will be in the HD AEZ, while the other
three AEZs will be reduced proportionally so that their relative share changes little (Table 2).
The median elevation of all suitable pixels was 1024 masl but the median elevations differed
between AEZs. The Ct AEZ lay at 1575 masl compared with the CD AEZ at 700 masl. By 2050,
the median elevation of pixels that remain suitable for arabica coffee will be more than 300m
higher. The effect will differ by AEZ; the elevation of the CV AEZ will not change, while the
HW AEZ will be nearly 500m higher (Table 2).
We disaggregated the AEZs for now and 2050 to determine in which AEZ each pixel will be
classified in 2050 (Fig 6). The Ct AEZ was least affected with 59% of the pixels unchanged, 4%
becoming each HW and HD, and 34% becoming unsuitable. The HD AEZ will be most
affected, with only 16% of current pixels remaining suitable by 2050s and 78% becoming
unsuitable. Of the other three AEZs about 40% of current grid cells remain suitable and 46–
49% will become unsuitable. The remainder shifted to the other AEZs (Fig 6).
Only half of the pixels that will be in the HD AEZ in 2050 currently belong to this AEZ,
with the remainder coming from other zones. In contrast, nearly all future area the Ct AEZ cur-
rently belong to it. Only a small part of the area that will be suitable for arabica coffee in 2050
will be land that was previously unsuitable. Half of this novel area will be in the CV AEZ (Fig
6), such as in the southern margin of Brazilian coffee zones (Fig 4A).
The average ability of the RF algorithm to discriminate AEZs was satisfactorily high across
all individual models, with the multiclass AUC averaging 0.84, which is much better than
chance. The conventional AUC measure averaged 0.91, which demonstrates the robustness of
the algorithm to discriminate between suitable and unsuitable pixel cells.
Example application: Spatial distribution of robust sites
We overlaid the maps in Fig 4 with pixels that met the 80% and 100% stability criteria defined
above in the Data and methods section.
We used these to make a list of recommended sites for trials for each country that is an
important arabica producer. For each site within an AEZ we listed geographic location, admin-
istrative description, altitude and values of bioclimatic variables were given (Table 3).
We could not identify robust sites for all countries that are important arabica producers.
Nor could we identify potential trial sites for all the AEZs that occur in each country, for exam-
ple in Nicaragua the only robust sites were in the HD AEZ. Although a part of Nicaragua’s cof-
fee is produced in the HW AEZ, none of these pixels were classified as robust.
Discussion
The agro-ecological zoning model developed here adds to our understanding of the climate
change impacts on the production of arabica coffee globally. The global AEZ approach we used
goes beyond previous regional AEZ research by demonstrating how different AEZs will be
affected differently, which can be used to guide research into adaptation to climate change. The
global impacts we project here agree with previous studies on the magnitude of impacts, which
is a reduction of area suitable for coffee production by about 50% until the 2050s [9,11,12].
Most areas that will become unsuitable to grow arabica coffee in the future now have cli-
mates with high maximum temperatures and long dry seasons (AEZ HD). These include some
Fig 4. Distribution of AEZs in regions important for arabica coffee; A Brazil; B East Africa; C Central America; D Indonesia; E Colombia; F India;
Colored grid cells represent the agro-ecological zone; dots indicate sites recommended for trial sites, hatching alternative locations with less
model agreement.
doi:10.1371/journal.pone.0140490.g004
Arabica Agro Ecological Zones and Climatic Change
PLOS ONE | DOI:10.1371/journal.pone.0140490 October 27, 2015 10 / 16
Fig 5. Change of agro-ecological zone in important arabica coffee production regions until the 2050s; A Brazil; B East Africa; C Central America; D
Indonesia; E Colombia; F India
doi:10.1371/journal.pone.0140490.g005
Arabica Agro Ecological Zones and Climatic Change
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areas that currently give high yields of arabica coffee (northern Minas Gerais state in Brazil,
parts of India, and Nicaragua). In contrast, substantial areas that currently lie in other AEZs
will become HD in the future. These scenarios will offer both challenges to and opportunities
for the coffee sector. On the one hand, important coffee-producing areas will struggle to remain
productive while currently less-favored areas may become more productive. Research to adapt
coffee production to climate change will thus have to make arabica coffee better adapted to
heat and drought stress. Other regions may have to change their agronomic practices to remain
competitive, for example by learning from farmers who are currently productive in the HD
AEZ.
The constant (Ct) AEZ, which has neither high nor cold temperatures, will be least affected
by climate change. It occurs close to the equator in Colombia, Ethiopia, Kenya and Indonesia
and produces high quality coffee. Despite the comparatively small effects of climate change on
Table 2. Distribution of grid cells in the agro-ecological zones under current and 2050s conditions.
Climate Unit Hot-wet Constant Hot-dry Cool-variable Cool-dry Total
Current Pixel count - 8943 14869 14337 11637 6479 56265
Pixel share % 16 26 25 21 12 7.2
1
Median elevation masl 946 1578 807 825 704 1024
2050 Pixel count - 4992 9710 4248 6944 2859 28753
Pixel share % 17 34 15 24 10 3.6
1
Median elevation masl 1429 1954 1185 812 835 1362
1
Percent share of all grid cells within a latitudinal belt 30°N–33°S
doi:10.1371/journal.pone.0140490.t002
Fig 6. Transition plot of the fate of suitable pixels in coffee AEZs from current conditions to 2050s; size of boxes and width of transition arrows is
representative of the number of pixels; the size of the box of the “unsuitable”category includes only pixels that become suitable by 2050 or were
suitable with current climate.
doi:10.1371/journal.pone.0140490.g006
Arabica Agro Ecological Zones and Climatic Change
PLOS ONE | DOI:10.1371/journal.pone.0140490 October 27, 2015 12 / 16
this AEZ, challenges arise due to decreased coffee quality [38]. Climate change will bring few
novel pixels in the Ct AEZ so that its increase in median elevation was caused by it losing low-
elevation pixels. Due to the conical form of land with elevation, there is more agricultural area
at lower elevations than there is upslope. The actual area lost to production may therefore be
larger than the number of pixels lost suggests. Moreover, land at high elevations is often diffi-
cult to access, is too steep for cultivation, or has shallow soil so that upward migration may not
always be an option. Furthermore, land at higher elevations often has high ecosystem value or
is used for forestry, which could be further barriers to upward migration of coffee.
The Hot-wet (HW) AEZ has high precipitation similar to the Ct AEZ. Temperatures are
higher, but seasonal variation is average. Typical locations are southern Mexico and central
Ethiopia. The HW AEZ occurs associated with both Ct and HD AEZs, sharing high maximum
temperatures with the latter. The strong effect of climate change on elevation in the HW and
Ct AEZs suggests that higher temperatures will be the most limiting factor. High temperatures
induce dehiscence of flowers and fruits [4] and make attacks from pests like the coffee berry
borer [39] more likely.
About half of the novel area for arabica coffee will be dispersed among existing coffee
regions at higher elevations. Substantial areas will become suitable south of the southern mar-
gin of the Brazilian coffee region. These areas have short dry seasons but temperature variabil-
ity and especially low minimum temperatures currently make them unsuitable for coffee. The
higher temperatures that climate change will bring will reduce frost risk [19]. Nevertheless,
there will be little expansion beyond the present latitudinal limits because temperature variabil-
ity will remain a limitation.
We aimed to identify homologous climatic zones in current and future conditions, which is
the basis of the AEZs. Locations that become another AEZ in the future may adapt to it guided
by how growers that are currently in that AEZ manage their crops. The AEZs that we defined,
however, are based on current climates, and we use the same criteria to classify future climates.
But climates that we consider unusual or marginal for arabica coffee today may become more
common by 2050. For example, the AEZ that dominates southern Brazil, CV, has low mini-
mum temperatures and high temperature variability. With climate change bringing reduced
frost risk, we speculate that in the future a novel AEZ with high temperature variability and
high maximum temperatures could become important.
We defined the AEZs using a database of occurrence pixels where C.arabica is grown that
included data from all the world’s coffee-producing regions. We excluded as outliers locations
with unusual climates. It might be useful to include marginal locations, however, as the envi-
ronmental limits for arabica coffee may provide insights into possible adaptation strategies.
The addition of occurrence records from marginal locations such as Zambia or Yemen may
Table 3. List of recommended trial sites for Nicaragua.
ID AEZ Lon Lat Country State District Elev bio_2 bio_3 bio_5 bio_6 bio_7 bio_12 bio_18 bio_20
° ° - - - masl 0.1°C - 0.1°C 0.1°C 0.1°C mm mm months
12 Hot-dry -85.958 13.125 Nicaragua Jinotega Jinotega 1330 98 70 252 113 139 1797 398 3
13 Hot-dry -86.458 13.208 Nicaragua Estelí Estelí 1164 105 73 274 132 142 1669 638 5
15 Hot-dry -86.125 13.292 Nicaragua Jinotega San Sebastián de Yalí 1369 101 70 262 119 143 1738 393 3
16 Hot-dry -86.042 13.292 Nicaragua Jinotega San Rafael del Norte 1032 105 73 274 131 143 1486 372 3
17 Hot-dry -86.625 13.375 Nicaragua Madriz San Lucas 1131 106 74 278 135 143 1929 777 3
19 Hot-dry -86.542 13.708 Nicaragua Nueva Segovia Macuelizo 1290 103 70 276 130 146 1864 558 2
20 Hot-dry -86.542 13.792 Nicaragua Nueva Segovia Dipilto 1370 101 69 271 126 145 1864 546 2
doi:10.1371/journal.pone.0140490.t003
Arabica Agro Ecological Zones and Climatic Change
PLOS ONE | DOI:10.1371/journal.pone.0140490 October 27, 2015 13 / 16
improve our ability to differentiate between marginal and unsuitable climates. This could con-
tribute to our ability to adapt to more extreme climates in the future.
Machine learning approaches, like the RF algorithm used here, have been criticized to be
prone to overfit to specific variable states. We applied the algorithm carefully, choosing vari-
ables that with low levels of correlation and achieved a high classification accuracy as shown by
the AUC metric. Moreover, the overall projected impact of climate change is similar to that
projected in other studies [11]. On the other hand, climate change impacts on coffee will poten-
tially be more severe as was demonstrated by models with more pessimistic emission scenario
choices or when considering a longer time horizon [9,10]).
We specified that robust sites for the long-term variety trials must unambiguously represent
an AEZ and that the fundamental climate characteristics will be unaffected by climatic change.
We specified these conditions because variety improvement in coffee can take several decades.
Fundamental changes in the climate during the course of a long-term trial would invalidate
comparison of data gathered over many years. For the identification of robust sites for each
AEZ we took account of variation between GCMs and selected only those sites that could be
classified unambiguously. Only a small number of such pixels could be identified.
In conclusion, we therefore urge coffee research to consider climatic change carefully when
taking decisions with a long time horizon such as selecting sites for variety trials. When com-
paring data from previous experiments, analysis often considers only the environmental
parameters of interest. This approach may result in erroneous conclusions if fundamental char-
acteristics of the climate change over the time interval under consideration.
We urge that tests of strategies to improve varieties and other agronomic measures consider
the locations we identify here. Nevertheless, future research will also have to expand the envi-
ronmental limits of arabica coffee to novel or marginal climates to minimize the worst impacts
of climate change on the coffee sector.
Acknowledgments
This research was conducted under the CGIAR Research Program on Climate Change, Agri-
culture and Food Security (CCAFS). It was funded and initiated by World Coffee Research
through the project “Identifying Long Term Variety Trial Locations, Provide Climate Informa-
tion to Support World Coffee Research Variety Trials and Support on Trial Data Analysis.”
We thank our colleagues at the Data and Policy analysis area at CIAT for their support and
helpful comments, and Myles Fisher for his suggestions that greatly improved the original
manuscript.
Author Contributions
Conceived and designed the experiments: CB PL CM TS. Performed the experiments: CB. Ana-
lyzed the data: CB JGPJ. Contributed reagents/materials/analysis tools: PL. Wrote the paper:
CB PL CM.
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