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Coffee has proven to be highly sensitive to climate change. Because coffee plantations have a lifespan of about thirty years, the likely effects of future climates are already a concern. Forward-looking research on adaptation is therefore in high demand across the entire supply chain. In this paper we seek to project current and future climate suitability for coffee production (Coffea arabica and Coffea canephora) on a global scale. We used machine learning algorithms to derive functions of climatic suitability from a database of geo-referenced production locations. Use of several parameter combinations enhances the robustness of our analysis. The resulting multi-model ensemble suggests that higher temperatures may reduce yields of C. arabica, while C. canephora could suffer from increasing variability of intra-seasonal temperatures. Climate change will reduce the global area suitable for coffee by about 50 % across emission scenarios. Impacts are highest at low latitudes and low altitudes. Impacts at higher altitudes and higher latitudes are still negative but less pronounced. The world’s dominant production regions in Brazil and Vietnam may experience substantial reductions in area available for coffee. Some regions in East Africa and Asia may become more suitable, but these are partially in forested areas, which could pose a challenge to mitigation efforts.
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A bitter cup: climate change profile of global production
of Arabica and Robusta coffee
Christian Bunn &Peter Läderach &
Oriana Ovalle Rivera &Dieter Kirschke
Received: 24 March 2014 /Accepted: 2 December 2014
#The Author(s) 2014. This article is published with open access at
Abstract Coffee has proven to be highly sensitive to climate change. Because coffee planta-
tions have a lifespan of about thirty years, the likely effects of future climates are already a
concern. Forward-looking research on adaptation is therefore in high demand across the entire
supply chain. In this paper we seek to project current and future climate suitability for coffee
production (Coffea arabica and Coffea canephora) on a global scale. We used machine
learning algorithms to derive functions of climatic suitability from a database of geo-
referenced production locations. Use of several parameter combinations enhances the robust-
ness of our analysis. The resulting multi-model ensemble suggests that higher temperatures
may reduce yields of C. arabica, while C. canephora could suffer from increasing variability
of intra-seasonal temperatures. Climate change will reduce the global area suitable for coffee
by about 50 % across emission scenarios. Impacts are highest at low latitudes and low
altitudes. Impacts at higher altitudes and higher latitudes are still negative but less pronounced.
The worlds dominant production regions in Brazil and Vietnam may experience substantial
reductions in area available for coffee. Some regions in East Africa and Asia may become
more suitable, but these are partially in forested areas, which could pose a challenge to
mitigation efforts.
1 Introduction
The livelihoods of 100 million people depend on coffee (Pendergrast 1999), many of whom
are vulnerable to climate change (Baca et al. 2014). In addition to its socio-economic
importance, the agronomy of coffee production justifies research on the crops adaptation to
Climatic Change
DOI 10.1007/s10584-014-1306-x
Electronic supple mentary m aterial The online version of this article (doi:10.1007/s10584-014-1306-x)
contains supplementary material, which is available to authorized users.
C. Bunn (*):D. Kirschke
Department of Agricultural Economics, Humboldt Universität zu Berlin, Philippstr. 13, 10115 Berlin,
C. Bunn :O. Ovalle Rivera
International Center for Tropical Agriculture (CIAT), Km 17, Recta Cali-Palmira, Cali, Colombia
P. Läderach
International Center for Tropical Agriculture (CIAT), Hotel Seminole 2 Cuadras al Sur, Managua, Nicaragua
climate change. The average lifespan of a coffee plantation is about 30 years (Wintgens
2009) but can be more than 50 years. Existing coffee plantations may thus experience the
climate change foreseen by global circulation models (GCMs). Commercial varieties in
current use have a narrow genetic base (Anthony et al. 2001) and therefore a narrow
climatic range (DaMatta 2004). The threat of climate change is further aggravated by the
long lead time of adaptation measures such as breeding for stress tolerance, which may
take decades (Eskes and Leroy 2008).
Most coffee is produced from two species. Robusta coffee (Coffea canephora var. Robusta)
accounts for 30 % of global production (USDA 2012). It is generally more heat tolerant, but is
more susceptible to low temperatures than Arabica coffee (Coffea arabica) (Wintgens 2009),
which accounts for the remaining 70 % of global production (USDA 2012). Climate change
has already been forecast to reduce productivity of Arabica coffee (e.g. Gay Garcia et al. 2006;
Zullo et al. 2011; Schroth et al. 2009). Coffee could migrate to higher latitudes (Zullo et al.
2011) or altitudes (Schroth et al. 2009) but this would not benefit current producers (Baca et al.
2014) and the migration could threaten ecosystems (Laderach et al. 2009). Although
C. canephora can sustain higher temperatues than the higher quality C. arabica, it is uncertain
whether it can replace the latter on commodity markets.
Studies have assessed the impact of climate change on coffee using one of three methods:
use of common denominators of climate suitability to map risk areas (Zullo et al. 2011;
Simonett 1988); or correlation between temporal (Gay Garcia et al. 2006) or spatial variability
of coffee production (Schroth et al. 2009; Davis et al. 2012). Simonett et al. (1988), with
Robusta in Uganda, used mean annual temperature to conclude that only high altitudes will
remain suitable. Zullo et al. (2011) included water deficit and frost risk in addition to mean
annual temperature to project a southward migration of Arabica production in Brazil. Gay
Garcia et al. (2006) used the correlation between yield and temperature in Mexico to suggest
that economical yields would not be viable by 2020. Schroth et al. (2009)foundasimilar
impact on Mexican coffee with increasing temperatures. Davis et al. (2012) concluded that
areas that are climatically suitable for indigenous coffee varieties in East Africa may be
substantially reduced in future scenarios.
These previous studies on the impact of climate change on coffee demonstrate latitudinal
and altitudinal migration or complete abandonment of coffee. The results, however, are limited
to local levels and global trends remain unclear.
Davis et al. (2012) and Schroth et al. (2009) used the MaxEnt species distribution
software (Phillips et al. 2006) to investigate the impact of climate impacts on C. arabica.
MaxEnt has been criticized as giving biased representation of suitable climates if the
parameters are not chosen carefully. There is a vast literature on defining appropriate
parameter values for robust models. But in the absence of reliable data to compare inter-
temporal climate and species distribution changes, there is no clear guidance for param-
eter values that allow reliable extrapolation (Elith and Graham 2009). This is despite
model uncertainties that are larger than those from stemming from GCMs (Diniz-Filho
et al. 2009).
This limitation may be overcome by using outputs from an ensemble of various models,
which provide a more robust assessment and allow for explicit uncertainty analysis (Araujo
and New 2007; Diniz-Filho et al. 2009). Weigel et al. (2008) demonstrated that outputs from a
multi-model ensemble improved prediction skill. Hannah et al. (2013)usedensemblesto
assess the potential indirect effects of land-use change on ecosystems by the migration of
viticulture. Bhatt et al. (2013) used them to generate risk maps of dengue fever.
The objective of this paper is to predict current and future climate suitability for
coffee (Arabica and Robusta) production on a global scale. The ensemble approach
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we chose improves the robustness of the analysis compared with previous studies. We
then compared the distribution of suitability under current and future conditions to derive
an impact profile of climate change on global coffee production.
We first assembled a global dataset of known present occurrence locations of both
coffee species. Using these data, we trained three popular machine-learning algorithms;
Support Vector Machines (Karatzoglou et al. 2006); Random Forest (Breiman 2001);
and MaxEnt (Phillips et al. 2006). We used distinct parameter combinations as outlined
below, to give a total of 135 models. We evaluated the model performance against the
performance of a trivial inverse-distance model. Finally, we extrapolated the models on
to interpolated climate data of current and future conditions and derived the mean
suitability score for each global pixel cell. We generated the future climate data by
downscaling GCM models run for the representative concentration pathways (RCP)
2.6, 6.0 and 8.5 (van Vuuren et al. 2011). We analyzed impacts for latitude, altitude,
regions and land-use classes to hypothesize future impact scenarios on global coffee
2 Materials and methods
2.1 Climate variables
For the current climate (19502000) we used the WorldClim global climate data set on
2.5 arcminute resolution (Hijmans et al. 2005). The dataset provides interpolated climate
layers for 19 bioclimatic variables based on historical data. These variables represent
patterns found in monthly weather station data, e.g. annual temperature and precipitation
extremes, seasonality and means.
We used five GCMs from the IPCCs 5th assessment report (Stocker et al. 2013)to
obtain future climate data (GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC-
ESM-CHEM, and NorESM1-M). These GCMs are representative of projected changes
of global mean temperature and precipitation (Warszawski et al. 2014). We down-
scaled the outputs of the GCMs using the delta method (Ramirez and Jarvis 2010)
and computed the difference between model outputs for current conditions and the
mean for the 20402069 time-slice. We smoothed the resulting layers to 2.5 arcminute
resolution and applied them to the WorldClim layers for current climate. The result
was a high-resolution surface corrected for bias for the current climate and the 2050
time-slice for the 19 bioclimatic variables.
2.2 Present occurrence data
Present occurrence location data identify climates currently suitable to produce coffee.
We derived the occurrence points from three sources: (i) Geo-referenced coffee farms;
(ii) geo-referenced municipalities in Brazil that produce coffee; and (iii) geo-
referenced coffee-growing areas identified from Google Earth where data sources (i)
or (ii) were not available.
Most occurrence points came from a global database of 62,000 geo-referenced
individual farms with predominantly C. arabica and some C. canephora.The
International Center for Tropical Agriculture (CIAT) developed the database during
several regional projects that were conducted in collaboration with coffee cooperatives
and cooperating research organizations.
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A comprehensive set of occurrence records in all coffee-producing regions is desirable so
that all suitable climates are represented in the database (Elith et al. 2011). We supplemented
the geo-referenced data by generating additional occurrence points using publicly-available
information about the distribution of coffee production. We used satellite imagery to identify
precise locations based on this information.
Unlike the C. canephora data, data of the C. arabica locations were not collected for
modeling so that they were highly clustered in the project regions. We stratified the
database to avoid bias using a principal-component analysis on the 19 bioclimatic
variables to identify typical climates. From each climate cluster we chose a random
representative sample. This reduced the original sample to 1772 unique presence loca-
tions for C. arabica.
Neither the Arabica nor the Robusta database included all of the dominant growing
regions in Brazil, where 36 % of global Arabica coffee is produced (USDA 2012). To
ensure sufficient representation of Brazilian sites and climates, we included data provid-
ed by IBGE (2012). Using these data, we identified municipalities where 75 % of the
coffee is from one or other of the two species. We then geo-referenced these municipal-
ities for the appropriate species.
The combined geo-reference dataset gave 2861 unique pixel cells for C. arabica in
26 countries that together accounted for 92 % of global Arabica output 19982002
(USDA 2012). For C. canephora the dataset included 364 unique pixel cells in 11
countries that together account for 92 % of global Robusta output 19982002 (USDA
2012) (Supplementary Material Table S1). Figure 1shows the distribution of present
coffee locations and major production regions.
2.3 Background sampling
To fit a function that describes suitable climates, the classification algorithms compare
the variable patterns found at present occurrence locations with the pattern found in
environments that are potentially suitable. To characterize these environments, we took
random samples from locations that were not known present locations.
We chose the background samples to avoid both trivial classification and overtraining
of the algorithms. In ecology, there is a trade-off between predictive performance and
Fig. 1 Global coffee location database and major coffee growing regions. Blue points represent C. canephora
occurrence locations; orange points locations of C. arabica based production. Grey shading andboldnames
represent regions of coffee production
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capability to generalize. For example, a model that always correctly separates known
occurrence locations from the random background samples may be undesirable. This is
because it underestimates the true environmental range in cases where the known
occurrence data incompletely represent the true distribution. A more general model,
however, that always correctly predicts unknown present locations may overestimate
the environmental range.
No optimization framework for the definition of background parameters and modeling
approaches exists to date (Elith and Graham 2009). Therefore, rather than using a single
sampling strategy we used a model ensemble. We based the ensemble on several
background sampling parameters within reasonable ranges for (i) the geographical extent
from which the background sample was drawn, and (ii) the number of samples.
Furthermore, we accounted for remaining sampling bias in the location database using
the biased-background sampling method Dudík et al. (2005)) (Supplementary Material
Table S1).
The geographic extent of background samples should reflect prior knowledge of thespecies
distribution and be adequate to the geographical scale of the study (VanDerWal et al. 2009).
We employed three different background concepts, political, biophysical, and geographic. We
defined the first background as all countries that produce either Robusta or Arabica (USDA
2012;ICO2013) respectively. We defined the second by limiting the environment to the
observed spread of annual mean temperature for each species location sample (C. arabica:
14 °C26.4 °C; C. canephora 19.2 °C27.8 °C). We defined the third by using a 4.5° buffer
around present locations (about 500 km at the equator).
The literature agrees that the ratio of background samples to occurrence locations should be
at least 1:1. Too few background samples do not allow for a clear distinction between
occurrence and background, commonly leading to an over-prediction of distribution, while
too many background samples result in under-prediction (Barbet-Massin et al. 2012). We used
occurrence location to background sample ratios of 1:1, 2:1, 4:1, 6:1, 8:1.
2.4 Model training
For the climate suitability mapping we relied on the classification probabilities provided
by three machine-learning algorithms: MaxEnt, Support Vector Machines (SVM) and
Random Forest. MaxEnt (Phillips et al. 2006) is widely used to model species distribu-
tion in ecology (Merow et al. 2013). SVM is a widely used classification algorithm; we
used the implementation in the R package kernlab(Karatzoglou et al. 2006). Random
Forests (Breiman 2001) is an ensemble learning method for classification of data using
multiple decision trees that has been shown to be useful in ecology (Prasad et al. 2006).
Machine learning algorithms include a regularization parameter that allows the user to
adjust a trade-off between optimal model fit and generalization. Optimal parameter
values are usually dependent on the characteristics of the input data. We therefore
initially defined relevant parameter values by conducting a grid search across the
relevant parameter ranges. To assess generalization capacity we selected 25 % of our
occurrence points that were most distant from other points as a test data set, and trained
on the 75 % of present locations that were not as dispersed. We chose three levels of
regularization per algorithm that improved model generalization compared to default
settings. For the MaxEnt regularization parameter βwe choose 0.01, 5 and 20: for
SVMs c-cost parameter 1, 0.5 and 0.05; and for the number of variables picked at nodes
by Random Forest 8, 4, and 2. The first value was meant to produce a well-fitted model,
while the last value gave a general model.
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2.5 Model evaluation
To assess the performance of the individual models we used two measures: the threshold
independent area under the receiver characteristic curve (AUC); and a calibrated AUC
measure (cAUC). AUCs were calculated using 10-fold subsampling of training and
testing data. Each model was thus trained on 90 % of the location database and evaluated
on the remaining 10 % in ten replications.
The AUC is the standard method of model evaluation in modeling predictive distri-
butions. It summarizes the ranking of occurrence points versus the ranking of back-
ground samples. If all present sites have a higher value than background sites its value is
1, while a value of 0.5 reflects a model that is no better than chance. The use of the AUC
statistic has been criticized, however, as being misleading when different background
samples are drawn from different background extents: low predictions on geographically
distant locations are often trivial and inflate the statistic (Lobo et al. 2008). This is to be
expected because climate patterns are usually auto-correlated. We therefore calculated a
cAUC as proposed by Hijmans (2012) by calibrating the model AUC using the AUC
derived from a trivial null model based on the inverse distance to the training presence.
We estimated variable importance by computing AUC values for each predictor
variable individually using the Caret package in R (Kuhn 2008). This method applies
cutoffs to the predictor data and then calculates sensitivity and specificity for each cutoff
to calculate the AUC. The AUC is then a measure for variable importance.
2.6 Impacts
We trained the three algorithms using the parameter spaces described above, five
different ratios of background to presence samples, three regularization choices, and
the three sampling extents. We therefore trained 3*5*3*3=135 distinct models per
species. We extrapolated the trained and tested models on raster data for the 19
bioclimatic variables from WorldClim and for the 2050 time-slice. This yielded maps
of continuous scores whether a pixel cell belonged to the absence or presence class.
This is equivalent to rating each global pixel cells climate as suitable or unsuitable
for coffee production. We normalized individual model outputs to scores from 0 to 1
and averaged them for each baseline and emission scenario. To define a threshold
between probabilities that represent marginal suitability and relevant suitability values
we chose the lowest value at a present location. We only included in the analysis
pixel cells that had suitability values above this threshold.
We compared impacts across latitude and altitude classes by comparing the sums of
suitability scores across 1° latitude classes and 100 m altitude classes. We analyzed
regional impacts for 12 regions of coffee production (Fig. 1).
We used the GLC2000 global land cover database (European Commission 2003)to
partition suitability changes to land with forest cover (GLC2000 global categories 1
9), land without forest cover, and agricultural land (GLC200 global categories 1018).
Tropical forests provide diverse ecosystem services, are more species rich and hold
higher carbon stocks than coffee plantations (De Beenhouver et al. 2013). Coffee
plantations, however, often have more biological diversity than other agricultural land
(Moguel and Toledo 1999) and hold more carbon stocks (van Rikxoort et al. 2014).
Therefore, conversion from natural forest to coffee would have a negative environ-
mental impact, but conversion from open land to coffee plantations could have a
positive effect.
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3.1 Current coffee suitability
The trained and tested models extrapolated on to raster data for WorldClims 19 biocli-
matic variables gave a global map of current suitability for coffee production (Fig. 2).
The largest areas suitable for C. arabica are in the Brazilian state of Minas Gerais. Other
highly suitable areas are in Central America and the Ethiopian highlands. Madagascar is
also highly suitable despite not being a major producer today. Other African and Asian
sites are rated as intermediate climatic suitability for production of Arabica.
Larger areas highly suitable for C. canephora are in the Brazilian Espirito Santo
region, West Africa, the lower regions of Central America and in mountainous locations
in Asia, especially the Philippines, Indonesia and Vietnam.
3.2 Future coffee suitability
We calculated the difference between current and future (2050) mean suitability scores.
For the RCP 6.0 scenario, Fig. 3(A-D) shows the changes in suitability for the current
dominant production regions of C. arabica in Latin America, Brazil, Asia and the center
of origin of the species in East Africa. The Brazilian production regions lose suitability
with possible positive changes at its southern margin. In the rest of Latin America, higher
altitudes become more suitable than at present. In East Africa there are positive changes
in suitability in the Ethiopian, Ugandan and Kenyan highlands. In Indonesia and the
Philippines there are patterns of altitudinal migration similar to South America.
Fig. 2 Current suitability distribution for coffee. Dark grey indicates high suitability, light grey intermediate
suitability. Hatching indicates the species
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Figure 3(E-G) shows the changes in suitability for C. canephora by 2050 in the RCP 6.0
scenario in Brazil, its center of origin in West Africa, and the most important region of Robusta
production in South East Asia and the Asian island states. The Brazilian states of Rondonia
and Espirito Santo may see severe losses of suitability. The Congo basin and coastal regions of
West Africa have decreased suitability. In contrast, suitability is likely to increase at higher
altitudes along the equator. In South East Asia the dominant Vietnamese production regions
lose suitability.
Maps for the RCP 2.6 and RCP 8.5 emission scenarios are in the Supplementary Material
(Fig. S1-2). The coefficient of variation across the 5GCMsisinSupplementaryMaterialFig.S3.
The CV is generally low, with the exception of the region around Brasilia in Brazil where it is up
to 100 % for C. arabica.
3.3 Distribution of climate change impacts
The suitability scores indicate how likely it is that a location is climatically suitable for coffee
production and a higher sum of scores means the suitable area is larger. The sum of suitability
scores across latitudinal meridians for current climate conditions and GCM outputs for scenario
RCP 2.6, RCP 6.0 and RCP 8.5 are shown in Fig. 4a.C. arabica loses suitability across all
Fig. 3 Suitability changes by the 2050s in the RCP 6.0 scenario; A-D: Arabica, E-G: Robusta. Hatching
indicates the current suitability distribution; Warm colors represent areas with negative climate change impacts
and cold colors positive changes
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latitudes, although at high latitudes the losses are not as pronounced. The only positive change can
be observed at around 27°S. Losses of suitability for C. canephora will mostly occur at low
The sum of suitability scores in discrete altitude classes for both C. arabica and C. canephora
by 2050 (meaned for each RCP scenario) compared with current conditions is shown in Fig. 4b.
Both species lose large shares of total suitability mostly in low altitudes below 1000 masl while
there will be less relative losses at higher altitudes.
The sum of suitability scores for major coffee regions for current conditions and means for
RCP scenarios by 2050is shown in Fig. 4c. The largest loss of suitability is in Brazil and South
East Asia for Arabica coffee, where accumulated suitability scores decrease by 85 % in RCP
8.5 and 30 % in RCP 2.6. The least impact on Arabica is projected for East Africa and the
Pacific Island region with 10 % of suitability lost in the RCP 2.6 scenario and up to 30 % in the
RCP 8.5 scenario. Globally, losses are projected to be 49 % of overall suitability score lost in
the RCP 6.0 scenario (Supplementary material Table S.3).
C. canephora suitability will be lost in the Congo basin with 60 % (RCP 2.6) to 95 % (RCP
8.5) of total suitability lost in the center of origin of the species. Again, East Africa is projected
to face the least impact. In the RCP 2.6 scenario the loss of suitability will be between 16 %
and up to 30 % in the RCP 8.5 scenario. Three of the important Robusta production regions,
Brazil, South-East Asia and West Africa, are projected to experience losses of about 60 % of
suitability score. The global losses are higher for Robusta (54 %) than for Arabica. Even in the
low impact scenario RCP 2.6 losses could be 51 % for Robusta (Supplementary Material
Table S.3).
In Fig. 5the changes in suitability are distributed according to land-use classes in the coffee-
producing regions by 2050 in the RCP 6.0 scenario. Globally, losses and gains in suitability are
nearly equally distributed across the area with forest cover and without forest cover. Novel areas
make up only about 10 % of lost suitability for both species, however. The exceptions for
C. arabica areinBrazil,EastAfricaandtheAsianislands.InBrazil90%ofsuitabilitylosses
are for areas without forest cover. In East Africa all the suitable area lost that is not currently
Fig. 4 Distribution of suitability changes by alatitude, baltitude, ccoffee regions; Continuous lines represent
C. arabica, dashed lines C. canephora, black lines the current distribution, colored lines future distribution; the
error bars indicate the minimum and maximum across RCP 6.0 model means
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forested may be replaced with novel area that is also not currently forested. In Asia, however,
nearly all suitability gains are in areas that are currently forested.
C. canephora shows a similar pattern. In West Africa 90 % of suitability losses occur on
land without forest cover, while in the Asian islands, Philippines and Indonesia, gains will be
on land with forest cover. For Robusta most of the suitability losses in East Africa also may be
replaced by gains on open land. In the Congo basin large losses of suitability for both species
will be on forested land.
3.4 Model validation
AUC values were consistently high across all model set ups. The lowest AUC value for Arabica
coffee was 0.92 and 0.73 for Robusta, indicating that the models perform much better than chance
at discerning presence from background locations. Considering the values that we compared to
the performance of a simple null model, cAUC, most of the models performed better than the
distance-based model. All models for Arabica coffee were better than the null model according to
cAUC. The Robusta models performed better than the null model in 74 % of the cases according
to cAUC.
3.5 Variable contribution
The most important variable for Arabica was the mean temperature of the warmest quarter. This
was followed by maximum temperature of the warmest month and mean temperature of the
wettest quarter. Precipitation variables ranked as least important, especially precipitation of the
driest quarter and month (Bio 14 and 16). Among the temperature variables, temperature
variability (Bio 2 and Bio 7) was least important.
In contrast, the variables that ranked consistently high for Robusta were the mean diurnal range
of temperature (Bio 2) and the annual temperature range (Bio 7). This was followed by maximum
temperature of the warmest month. Precipitation variables were more important for Robusta than
for Arabica, with intra-annual variation of precipitation (Bio 15) ranked the highest. Least
important were temperature in the coldest quarter (Bio 11) and precipitation during the coldest
quarter (Bio 19) (Supplementary Material Table S.2).
The goal of this study was to examine the implications of climate change for global coffee
production. Analysis of changes in suitability under the RCP 6.0 scenario shows that climate
Fig. 5 Distribution of suitability changes by region and the land use classes with forest cover and without forest
cover by 2050 under RCP 6.0; aC. arabica bC. canephora
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change may reduce production of Arabica coffee in many areas, especially in Brazil. Robusta
may also be less suitable in important regions in Brazil and Vietnam. Gains elsewhere will do
little to offset these losses, giving global losses in suitability for both species of about 50 %.
Only East Africa and the Asian island states show substantial gains in suitability for both
We developed a methodology that is based on the notion that an ensemble of models captures
more relevant information than a single model can. By using a mean of models based on several
feasible parameter combinations rather than a single model our analysis is more robust than
previous regional studies. The extrapolation of the models with spatially-explicit climate
information gave global maps of both C. arabica and C. canephora that indicate suitability
scores in regions of major production. We applied the underlying models to the outputs of five
global climate models for the RCP 2.6, RCP 6.0 and RCP 8.5 emission scenarios. We averaged
across emissions scenarios to produce maps and analyzed the change in suitability scores.
Both species show important changes in accumulated suitability scores at lower latitudes,
which become less negative, albeit not positive, at higher latitudes. A southward latitudinal
migration was also proposed by Zullo et al. (2011) in a regional study in Brazil. However, we
did not find such impacts of climate change in other regions. Moreover, the gains in suitability in
southern Brazil may not be enough to compensate for losses in suitability over large areas
elsewhere. Similarly, losses in suitability are mostly at low altitudes while higher altitudes gain
in suitability. Schroth et al. (2009)andSimonett(1988) identified similar altitudinal migration
for Arabica in Central America and for Robusta in Uganda, respectively. These local studies
confirm our analysis, which shows that altitudinal migration of coffee production will likely be a
global trend. The magnitude of this effect, however, depends on how climate change will impact
local conditions.
It has previously been hypothesized that Robusta production may be able to replace in part
the losses in Arabica production due to climate change. The hypothesis rests on the notion that
C. arabica is heat sensitive and would thus suffer in a hotter world. In contrast, C. canephora
can tolerate higher temperatures and could thus replace heat-stressed Arabica coffee. This
scenario may be viable in some regions, but our analysis emphasizes that C. canephora needs
climates with little intra-seasonal variability. This limits the Robusta crop to low latitudes. Also,
as climate may not only become hotter, but also more variable, this may aggravate negative
effects on Robusta coffee production. Thus, globally both species appear to be equally affected
by climate change. It is noteworthy that the Congo basin, the center of origin of C. canephora,
may become unsuitable for the species by 2050 in the high emissions scenario. This warrants
further investigation as many see indigenous varieties as the key to adapt coffee to climate
We found that Arabica production in Eastern Africa is less impacted than in other regions. In
contrast, Davis et al. (2012) proposed substantial reduction in the area suitable for indigenous
Arabica varieties in Eastern Africa. Our data are based on the distribution of commercial
plantations, which have adapted to a broader range of climates than those of Arabicas native
range. This difference suggests that in areas where coffee production remains feasible produc-
tion systems will have to be adapted. The necessary fundamental changes in local production
systems would pose substantial challenges to smallholder farmers.
Moreover, given the long lifespan of coffee plantations the feasibility of migrating coffee to
land that will be more suitable under climate change needs further study. The areas of East
Africa that will become more suitable for coffee are currently not forested, in contrast to the
Asian areas that will gain suitability, which currently are under forest. Climate-induced migra-
tion may thus result in further emissions from land-use change. Whether or not newly-suitable
areas will be threatened by conversion to agriculture depends on economic incentives. Our
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analysis shows that highly productive areas of coffee in Brazil and Vietnam may become
unsuitable for coffee in the future. World markets may thus create economic opportunities in
East Africa, but may induce additional deforestation in Asia, where coffee is already a frontier
crop. Policy-makers need to be aware that these are challenges that they will need to confront.
Acknowledgments This research was conducted under the CGIAR Research Program on Climate Change,
Agriculture and Food Security (CCAFS). ChristianBunn received a Klimafolgenforschungfellowship through
the Stiftung Humboldt Universität. We thank our colleagues at the Data and Policy Analysis group at CIAT for
their support and helpful comments. We also thank Dr. Myles Fisher for his careful revision of the manuscript.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution License which
permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are
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Climatic Change
... The vast majority of the world's coffee is made up of two species, Coffea arabica (Arabica) and Coffea canephora (robusta). Coffee, especially Arabica, is considered a sensitive crop, vulnerable to climate variability and change [3][4][5]. ...
... These climatic values are derived from historical surveys on the distribution of these species and are used to infer the climatic conditions suitable for coffee (i.e. if the species is able to survive and reproduce). Substantial departures from these climatic ranges are taken as an indication that an area is either currently, or in the future under climate change, unsuitable for growing coffee [3]. ...
... Anthropogenic climate change is expected to alter the global distribution of coffee suitability. The area of land suitable for coffee cultivation may be reduced by up to 50% [3]. Globally, rising temperatures are the main driver of these projected losses, but regional studies indicate that changes in precipitation totals and seasonality is crucial [5]. ...
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Global coffee production is at risk from synchronous crop failures, characterised by widespread concurrent reductions in yield occurring in multiple countries at the same time. For other crops, previous studies have shown that synchronous failures can be forced by spatially compounding climate anomalies, which in turn may be driven by large-scale climate modes such as the El Niño Southern Oscillation (ENSO). We provide a systematic analysis of spatially compounding climate hazards relevant to global coffee production. We identify 12 climate hazards from the literature, and assess the extent to which these hazards occur and co-occur for the top 12 coffee producing regions globally. We find that the number of climate hazards and compound events has increased in every region between 1980 and 2020. Furthermore, a clear climate change signature is evident, as the type of hazard has shifted from overly cool conditions to overly warm. Spatially compounding hazards have become particularly common in the past decade, with only one of the six most hazardous years occurring before 2010. Our results suggest that ENSO is the primary mode in explaining annual compound event variability, both globally and regionally. El Niño-like sea-surface temperatures in the Pacific Ocean are associated with decreased precipitation and increased temperatures in most coffee regions, and with spatially compounding warm and dry events. This relationship is reversed for La Niña-like signatures. The Madden Julian Oscillation also shows a strong association with climate hazards to coffee, with increased activity in the Maritime Continent related to a global increase in the number of cold or wet hazards and a decrease in the number of warm or dry hazards. With climate change projections showing a continued rise in temperatures in the tropics is likely, we suggest that coffee production can expect ongoing systemic shocks in response to spatially compounding climate hazards.
... Hasta el año 2013, se estimaron pérdidas de producción por 333 millones de toneladas de cereales, legumbres, carne, leche y otros productos básicos. En la producción de café, se menciona que más de 60% de las especies están en peligro de extinción (Davis et al., 2019); y se prevé qué a la mitad de la presente centuria, el 50% de los espacios productores de café en el planeta, sufrirán cambios climatológicos y que para el 2088, el café silvestre podría reducirse un poco más de 50% (Bunn et al., 2015). En México, se observa una tendencia decreciente en su producción, ya sea por la variabilidad climática o la presencia de plagas y enfermedades, y los bajos precios, en ese sentido, el Servicio de Información Agroalimentaria y Pesquera (SIAP, 2012(SIAP, -2019 menciona que en el 2012 se produjeron 1 358 840 toneladas de café y 910 063 toneladas en 2019. ...
... Until the year 2013, production losses were estimated of 333 million tons of cereals, legumes, meat, milk and other basic products. In coffee production, it is mentioned that more than 60% of the species are in danger of extinction (Davis et al., 2019); and it is foreseen that by the middle of this century, 50% of the coffee producing spaces in the planet will undergo climate changes and that by 2088, wild coffee could be reduced by slightly over 50% (Bunn et al., 2015). In Mexico, a decreasing trend in its production is observed, whether because of the climate variability or the presence of pests and diseases, and the low prices; in this sense, the Agrifood and Fishing Information Service (Servicio de Información Agroalimentaria y Pesquera, SIAP, 2012SIAP, -2019 mentions that 1 358 840 tons of coffee was produced in 2012 and 910 063 tons in 2019. ...
... Irrespective of the light levels, coffee plants have been assorted as highly susceptible to several stresses at current ambient C a (aC a ) [16][17][18]. Nevertheless, a greater intrinsic resilience of some coffee genotypes than anticipated, as well as the positive impact of eC a , can significantly attenuate stress impairments, namely of supraoptimal temperatures [19][20][21], and drought [22][23][24][25][26][27] in coffee. This mitigation by eC a has been linked to an amplified acclimation response associated with improved antioxidant system and other protective molecules, together with greater lipid dynamics, and enhanced photosynthetic performance with no apparent photosynthetic down-regulation [20][21][22][28][29][30][31]. ...
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Coffee (Coffea arabica L.) plants have been assorted as highly suitable to growth at elevated [CO 2 ] (eC a), although such suitability is hypothesized to decrease under severe shade. We herein examined how the combination of eC a and contrasting irradiance affects growth and photosynthetic performance. Coffee plants were grown in open-top chambers under relatively high light (HL) or low light (LL) (9 or 1 mol photons m −2 day −1 , respectively), and aC a or eC a (437 or 705 µmol mol-1 , respectively). Most traits were affected by light and CO 2 , and by their interaction. Relative to aC a , our main findings were (i) a greater stomatal conductance (g s) (only at HL) with decreased diffusive limitations to photosynthesis, (ii) greater g s during HL-toLL transitions, whereas g s was unresponsive to the LL-to-HL transitions irrespective of [CO 2 ], (iii) greater leaf nitrogen pools (only at HL) and higher photosynthetic nitrogen-use efficiency irrespective of light, (iv) lack of photosynthetic acclimation, and (v) greater biomass partitioning to roots and earlier branching. In summary, eC a improved plant growth and photosynthetic performance. Our novel and timely findings suggest that coffee plants are highly suited for a changing climate characterized by a progressive elevation of [CO 2 ], especially if the light is nonlimiting.
... Se han realizado estudios globales que señalan que la precipitación pluvial es menos importante que la temperatura en relación con la definición de áreas de producción cafetalera (Bunn et al., 2015;Pham et al., 2019). En contraste, estudios realizados a nivel nacional o por regiones al interior de los países productores de café, muestran que la precipitación pluvial es más significa-tiva en las zonas productoras del grano (Chemura et al., 2014;Pham et al., 2019). ...
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Objetivo: determinar la incidencia de la sequía en los niveles de productividad de café e identificar la percepción de los productores de café en la Huasteca Potosina. Diseño metodológico: se determinó el índice estandarizado de precipitación para 12 meses con datos de cinco estaciones pluviométricas para el periodo 1961-2018, localizadas en la zona cafetalera de la Huasteca Potosina. Se comparó con el comportamiento temporal de las superficies sembradas, cosechadas y el volumen de producción para el periodo 1985-2020. Se correlacionó la precipitación pluvial anual de las cinco estaciones con la superficie sembrada y la producción mediante la determinación del coeficiente r de Pearson. Se aplicaron 25 entrevistas estructuradas a campesinos productores de café. Resultados: los periodos ligeramente secos inciden en la superficie cosechada y en el volumen de producción, con valores cercanos a los valores promedio. Los valores de r de Pearson indican muy baja correlación de la precipitación pluvial con los volúmenes de producción, en tanto que 60% de los entrevistados señala que la variabilidad climática provoca efectos en la producción cafetalera. Limitaciones de la investigación: la determinación del índice estandarizado de precipitación con el que se estiman periodos secos y húmedos solo considera los valores de precipitación pluvial medidos en cada estación meteorológica. Hallazgos: la comparación del índice estandarizado de precipitación con el comportamiento anual de la superficie cosechada y el volumen de producción de café muestran que la precipitación pluvial es relevante en la producción cafetalera, eso coincide con la percepción de los productores cafetaleros.
... They referred to real climatic disturbances that cannot allow a smooth implementation of the cultural calendar by farmers, who no longer easily make distinctions between seasons. Like all perennial plants, coffee and cocoa trees have a phenological cycle that is entirely dependent to seasonal climate variations (Bunn et al., 2015). They are therefore annually subjected to completely unpredictable climatic disturbances (DaMatta et al., 2010). ...
The decline of Cameroon cocoa and coffee productions are increasingly designated as one of the negative consequences of climate change on plants development. The purpose of this study was to contribute to improving the productivity of cocoa and coffee trees, in their production areas, in Cameroon. Thus, 280 plots, located in three different agro-ecological zones, were monitored for five consecutive years (2014-2018). Meteorological data were also systematically collected at each site. Data analysis highlighted three classes of unstable meteorological profiles that reflect the non-recurrence of climatic events on the study sites. Multiple logistic regression analysis showed that the incidence of cocoa black pod disease and that of Arabica coffee berry disease increases with the quantity of rainfall and the number of rainy days. This increase rather induces a decrease in the attack rate of berry borer on the Robusta coffee trees. The results obtained made it possible to identify, by elucidating their respective roles, the climatic variables which have an effect on the productivity of cocoa and coffee trees. They have also led, for the first time, to the conceptualization of innovative technical processes, which can reduce the harmful effects of climatic disturbances on cocoa and coffee crops.
... These F1-hybrids developed in the last decades have shown better bean physical characteristics and better sensory quality 19,35,36 . Climate change across the globe is predicted to decrease the area suitable for coffee plantation and to create new frontiers for coffee establishment 37,38 , however the effects of climate change on the quality of coffee in these frontier areas with suboptimal conditions are not well studied. The Northwest of Vietnam presents suboptimal conditions for cropping C. arabica as it is situated at the latitudinal limit of the tropical belt. ...
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Background: The effects of the environment and genotype in the coffee bean chemical composition were studied using 9 trials covering an altitudinal gradient (600-1100 m.a.s.l) with 3 genotypes of Coffea arabica in the Northwest mountainous region of Vietnam. The impacts of the climatic conditions on bean physical characteristics and chemical composition were assessed. Results: We showed that the environment had a significant effect on the bean density and on all bean chemical compounds. The environment effect was stronger than the genotype and genotype-environment interaction effects for cafestol, kahweol, arachidic (C20:0), behenic acid (C22:0), 2,3-butanediol, 2-methyl-2-buten-1-ol, benzaldehyde, benzene ethanol, butyrolactone, decane, dodecane, ethanol, pentanoic acid, and phenylacetaldehyde bean content. A 2°C increase in temperature had more influence on bean chemical compounds than a 100 mm increase in soil water content. Temperature was positively correlated with lipids and volatile compounds. With an innovative method using iterative moving averages, we showed that correlation of temperature, VPD and rainfall with lipids and volatiles was higher between the 10th and 20th weeks after flowering highlighting this period as crucial for the synthesis of these chemicals. Genotype specific responses were evidenced and could be considered in future breeding programs to maintain coffee beverage quality in the midst of climate change. Conclusion: This first study of the effect of the genotype-environment interactions on chemical compounds enhances our understanding of the sensitivity of coffee quality to genotype environment interactions during bean development. This work addresses the growing concern of the effect of climate change on speciality crops and more specifically coffee. This article is protected by copyright. All rights reserved.
... The volcanoes in the Guatemalan Southern Volcanic Chain (GSVC) are an area of endemism for beetles in the family Passalidae that are restricted to high altitudes [16,17]; there are nearly 90 extant species of Passalidae in Guatemala of which at least 33 occur in the GSVC [18] (personal observation). In the GSVC, much of the forest at mid elevations has been cleared for agricultural use (mainly coffee plantations) [9], it faces the additional threat of a reduction in area due to rising temperatures resulting from climate change and an altitudinal shift of coffee plantations to accommodate these temperature changes [19,20]. Organisms with similar distributions and endemism patterns in Mesoamerica such as salamanders experienced a reduction in species diversity at high altitudes between 1975 and 2005, primarily due to changes in microclimate in the habitats where they occurred [21]. ...
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Two patterns are apparent in the altitudinal distribution of Neotropical passalid beetles: (a) species that occur only in lowland forest habitats but have broad geographic distributions, and (b) montane endemic species with relatively limited distributions. The transition zone between these distributions in upper Mesoamerica occurs, on average, at approximately 1500 m above sea level (a.s.l.). We studied the altitudinal stratification of passalid beetle communities living on two volcanoes in Guatemala (Atitlan and Santa Maria), revisiting a study conducted in 1981 by MacVean and Schuster. We collected passalid beetles at the same study sites and compared the community composition along the altitudinal gradient. We collected all but one of the species reported by MacVean and Schuster and found three additional species. We observed two key differences in the passalid communities observed in 1981 versus the present: (a) for the Atitlan site, the species’ turnover line from lowland to montane species shifted from 1600 to 1800 m a.s.l.; and (b) in both volcanoes, we collected passalid beetles well above 2700 m a.s.l., which was the upper limit at which they were found in 1981. Both observations are consistent with a shift of the passalid beetle community to higher elevations, perhaps in response to changes in local climate/habitat conditions, including increased temperatures and changes in forest composition.
... More than 25 million people depend on this production [18]. However, due to climate change, a significant reduction of coffee cultivation area (up to 50%) is expected by 2050 [2,3,13,21]. Consequently, it is imperative to develop new tolerant varieties. ...
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Faced with global warming, the surface area of coffee cultivation regions is expected to diminish significantly in the near future. As a result, new varieties or agronomical practices improving drought tolerance need to be found. The aim of this work is to characterize drought tolerance of Coffea canephora genotypes and their reciprocal grafted plants with physiological tools and biochemical analyses. Under greenhouse conditions, control plants (sensitive or tolerant) and reciprocal grafted plants submitted to 14 days of water deprivation show variations of the monitored parameters, such as soil and leaf water potential, stomatal conductance, and osmoprotectant compounds (sugars, polyols, amino acids). The variations observed confirm the differences between the phenotypes defined as drought-tolerant and drought-sensitive. Reciprocal grafting shows enhanced and contrasting situations. A sensitive clone grafted onto tolerant rootstock presents higher tolerance to drought and physiological or biochemical parameters similar to a drought-tolerant clone. The opposite is observed for tolerant clones grafted onto a sensitive one. More contrasted results are obtained with glucose, fructose, proline, and mannitol content which could be used as indicators for drought tolerance. Our finding shows strong variability for drought tolerance in our Robusta clones and demonstrates the impact of grafting on physiological and biochemical parameters linked to drought tolerance. The use of drought-tolerant rootstock leads to better regulation of water management and biochemical composition of the scion in drought-sensitive clones. This could be an approach to improving drought tolerance of Coffea canephora genotypes and to limiting the impact of global warming on coffee farming.
... With regard to temperature and precipitation, no relationship was found with the cup score (R 2 = 0.035 and R 2 = 0.039) as they have no identifiable statistical interactions, however, the attributes of acidity and flavor are linked to climatic conditions, where, for example, higher light and temperature conditions negatively affect the synthesis of various organoleptic-quality precursors in coffee, for example, caffeine, chlorogenic acids and sucrose (Geeraert et al., 2019), which in the face of a global warming scenario by 2050, an increase of 2.5 °C would modify the current distribution of the crop, which would go from elevations between 800 and 1400 m.a.s.l, to 1200 and 1600 m.a.s.l (Läderach et al., 2017). Likewise, Bunn et al. (2015), suggests that coffee is very sensitive to climate change and that an increase in temperature will reduce its yield. As for the influence of precipitation on the cup score, no reports were found that show a clear relationship between the two elements. ...
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The Mesoamerican region is considered to be one of the areas in the world most vulnerable to climate change. We developed a framework for quantifying the vulnerability of the livelihoods of coffee growers in Mesoamerica at regional and local levels and identify adaptation strategies. Following the Intergovernmental Panel on Climate Change (IPCC) concepts, vulnerability was defined as the combination of exposure, sensitivity and adaptive capacity. To quantify exposure, changes in the climatic suitability for coffee and other crops were predicted through niche modelling based on historical climate data and locations of coffee growing areas from Mexico, Guatemala, El Salvador and Nicaragua. Future climate projections were generated from 19 Global Circulation Models. Focus groups were used to identify nine indicators of sensitivity and eleven indicators of adaptive capacity, which were evaluated through semi-structured interviews with 558 coffee producers. Exposure, sensitivity and adaptive capacity were then condensed into an index of vulnerability, and adaptation strategies were identified in participatory workshops. Models predict that all target countries will experience a decrease in climatic suitability for growing Arabica coffee, with highest suitability loss for El Salvador and lowest loss for Mexico. High vulnerability resulted from loss in climatic suitability for coffee production and high sensitivity through variability of yields and out-migration of the work force. This was combined with low adaptation capacity as evidenced by poor post harvest infrastructure and in some cases poor access to credit and low levels of social organization. Nevertheless, the specific contributors to vulnerability varied strongly among countries, municipalities and families making general trends difficult to identify. Flexible strategies for adaption are therefore needed. Families need the support of government and institutions specialized in impacts of climate change and strengthening of farmer organizations to enable the adjustment of adaptation strategies to local needs and conditions.
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The Inter-Sectoral Impact Model Intercomparison Project offers a framework to compare climate impact projections in different sectors and at different scales. Consistent climate and socio-economic input data provide the basis for a cross-sectoral integration of impact projections. The project is designed to enable quantitative synthesis of climate change impacts at different levels of global warming. This report briefly outlines the objectives and framework of the first, fast-tracked phase of Inter-Sectoral Impact Model Intercomparison Project, based on global impact models, and provides an overview of the participating models, input data, and scenario set-up.
A quick pick-me-up or a subtle beverage with an aroma that conjures up images of special moments shared with special people? There's more to coffee than that. Apart from being a beautiful tree with fragrant flowers, coffee is also a culture, practically a religion to a certain elite and certainly a source of income to millions of people, rich and poor alike. Coffee professionals around the world will find the specific information they need in this lavishly illustrated and practical work designed to answer all their questions about the coffee plant and how it is grown, harvested, processed and refined. Specialists and experienced professionals were consulted and some 40 renowned international experts have contributed their specific knowledge and expertise to this comprehensive handbook, covering such topics as: Growing Pests, diseases, and their control Harvesting and processing Storage, shipment, quality The latest economical and technological aspects.In addition, special indexes demystify such confusing data as information sources, conversion tables and other technicalities. With its 40 chapters, over 1000 pages and 900 superb illustrations, this is a universally reliable manual, providing basic guidelines and recommendations applicable everywhere, and not geared to any specific country. © WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim 2004. All rights reserved.
The MaxEnt software package is one of the most popular tools for species distribution and environmental niche modeling, with over 1000 published applications since 2006. Its popularity is likely for two reasons: 1) MaxEnt typically outperforms other methods based on predictive accuracy and 2) the software is particularly easy to use. MaxEnt users must make a number of decisions about how they should select their input data and choose from a wide variety of settings in the software package to build models from these data. The underlying basis for making these decisions is unclear in many studies, and default settings are apparently chosen, even though alternative settings are often more appropriate. In this paper, we provide a detailed explanation of how MaxEnt works and a prospectus on modeling options to enable users to make informed decisions when preparing data, choosing settings and interpreting output. We explain how the choice of background samples reflects prior assumptions, how nonlinear functions of environmental variables (features) are created and selected, how to account for environmentally biased sampling, the interpretation of the various types of model output and the challenges for model evaluation. We demonstrate MaxEnt’s calculations using both simplified simulated data and occurrence data from South Africa on species of the flowering plant family Proteaceae. Throughout, we show how MaxEnt’s outputs vary in response to different settings to highlight the need for making biologically motivated modeling decisions.
Coffee production is impacting the climate by emitting greenhouse gasses. Coffee production is also vulnerable to climate change. As a consequence, the coffee sector is interested in climate-friendly forms of coffee production, but there is no consensus of what exactly this implies. Therefore, we studied two aspects of the climate impact of coffee production: the standing carbon stocks in the production systems and the product carbon footprint, which measures the greenhouse gas emissions per unit weight of coffee produced. We collected data from 116 coffee farms in five Latin American countries, Mexico, Guatemala, Nicaragua, El Salvador, and Colombia, for four coffee production systems: (1) traditional polycultures, (2) commercial polycultures, (3) shaded monocultures, and (4) unshaded monocultures. We found that polycultures have a lower mean carbon footprint, of 6.2-7.3 kg CO2-equivalent kg(-1) of parchment coffee, than monocultures, of 9.0-10.8 kg. We also found that traditional polycultures have much higher carbon stocks in the vegetation, of 42.5 Mg per ha, than unshaded monocultures, of 10.5 Mg. We designed a graphic system to classify production systems according to their climate friendliness. We identified several strategies to increase positive and reduce negative climate impacts of coffee production. Strategies include diversification of coffee farms with trees, the use of their wood to substitute for fossil fuel and energy-intensive building materials, the targeted use of fertilizer, and the use of dry or ecological processing methods for coffee instead of the traditional fully washed process.
The ecophysiological constraints on the production of the arabica and robusta coffee under shading or full sunlight are reviewed. These two species, which account for almost all the world’s production, were originally considered shade-obligatory, although unshaded plantations may out-yield shaded ones. As a rule, the benefits of shading increase as the environment becomes less favorable for coffee cultivation. Biennial production and branch die-back, which are strongly decreased under shading, are discussed. The relationships between gas exchange performance and key environmental factors are emphasized. Ecophysiological aspects of high density plantings are also examined.
Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, ***, 148–156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression.