ArticlePDF Available

Abstract and Figures

Coffee is one of the most important globally traded commodities and substantially contributes to the livelihoods of millions of smallholders worldwide. As a climate-sensitive perennial crop, coffee is likely to be highly susceptible to changes in climate. Using a systematic approach, we explore evidence from the published academic literature of the influence of climate change and variability, specifically drought, on coffee production. A number of mostly negative impacts were reported in the current literature, including declines in coffee yield, loss of coffee-optimal areas with significant impacts on major global coffee-producing countries and growth in the distribution of pest and disease that indirectly influence coffee cultivation. Current research also identified positive effects of climate change such as increases in coffee-producing niche, particularly in areas at higher altitudes; however, whether these gains might offset losses from other production areas requires further investigation. Other advantages include increases in pollination services and the beneficial effects of elevated carbon concentration, leading to potential yield improvements. Future priorities should focus on major coffee-growing regions projected to be adversely affected by climate change, with specific attention given to potential adaptation strategies tailored to particular farming conditions such as relocation of coffee plantations to more climatically suitable areas, irrigation and agroforestry. The majority of studies were based in the Americas and concentrated on Arabica coffee. A broader spread of research is therefore required, especially for the large growing regions in Asia and for Robusta coffee, to support sustainable production of the global coffee industry.
Content may be subject to copyright.
The impact of climate change and variability on coffee
production: a systematic review
Yen Pham
&Kathryn Reardon-Smith
&Shahbaz Mushtaq
&Geoff Cockfield
Received: 18 March 2019 /Accepted: 19 August 2019
#Springer Nature B.V. 2019
Coffee is one of the most important globally traded commodities and substantially contributes
to the livelihoods of millions of smallholders worldwide. As a climate-sensitive perennial crop,
coffee is likely to be highly susceptible to changes in climate. Using a systematic approach, we
explore evidence from the published academic literature of the influence of climate change and
variability, specifically drought, on coffee production. A number of mostly negative impacts
were reported in the current literature, including declines in coffee yield, loss of coffee-optimal
areas with significant impacts on major global coffee-producing countries and growth in the
distribution of pest and disease that indirectly influence coffee cultivation. Current research
also identified positive effects of climate change such as increases in coffee-producing niche,
particularly in areas at higher altitudes; however, whether these gains might offset losses from
other production areas requires further investigation. Other advantages include increases in
pollination services and the beneficial effects of elevated carbon concentration, leading to
potential yield improvements. Future priorities should focus on major coffee-growing regions
projected to be adversely affected by climate change, with specific attention given to potential
adaptation strategies tailored to particular farming conditions such as relocation of coffee
plantations to more climatically suitable areas, irrigation and agroforestry. The majority of
studies were based in the Americas and concentrated on Arabica coffee. A broader spread of
research is therefore required, especially for the large growing regions in Asia and for Robusta
coffee, to support sustainable production of the global coffee industry.
1 Introduction
The agricultural sector is expected to be substantially affected by climate change because of
the sensitivity of crops to increasing temperature and water shortages (Mendelsohn 2008;
Ramirez-Villegas and Challinor 2012). Apparent negative effects include declines in crop yield
Electronic supplementary material The online version of this article (
02538-y) contains supplementary material, which is available to authorized users.
*Yen Pham
Extended author information available on the last page of the article
Climatic Change (2019) 156:609630
/Published online: 4 September 2019
and quality and increases in pest and disease infestation, leading to reductions in crop
production worldwide (IPCC 2014). These pose significant challenges to smallholder farmers,
many of whom are dependent on rain-fed cultivation and have limited access to financial and
technical support (Cohn et al. 2017; Holland et al. 2017) that could help them to respond to
changing climatic conditions.
There has been a growing concern for coffee, a crop that is grown by over 25 million
mostly smallholder farmers in more than 60 countries throughout the tropics (Jayakumar et al.
2017) and that is highly sensitive to local climate (DaMatta and Ramalho 2006). Coffee yield
is strongly determined by climatic conditions, particularly during the vegetative and repro-
ductive phases of the plant (Tavares et al. 2018). Increasing temperatures and precipitation
shortages have negative impacts on flowering, fruiting and bean quality (Gay et al. 2006;Lin
2007). Furthermore, climate variables also control the incidence of serious pests and diseases
such as coffee leaf rust and coffee berry borer which could reduce coffee yield and quality and
increase production costs.
Coffee is the second-most globally traded commodity after oil (Davis et al. 2012)
and contributes significantly to the socio-economic development of many tropical
developing countries and the livelihoods of more than 120 million people worldwide
(TCI 2016). Coffee production has doubled during the last 30 years, amounting to
over 169 million bags in 2018 (ICO 2019b). The gross revenue of coffee production
was estimated at US$11.6 billion per year during 20002012 while the total value of
the entire coffee sector was more than US$173 billion in 2012 (ICO 2014). Brazil
makes up about 36% of the worlds production, followed by Vietnam (17%), Colom-
bia (8%) and Indonesia (6%) (ICO 2019b). Apart from substantially contributing to
agricultural GDP, coffee production provides millions of jobs and supports poverty
alleviation (Chemura et al. 2016;Laderachetal.2017). More than 70% of global
coffee is cultivated by smallholder growers in Africa, Asia and the Americas with
many of them relying on coffee as their major source of income (Fridell et al. 2008).
In addition to social and economic benefits, coffee plantations, particularly shaded
farms, also generate significant ecosystem services including biodiversity conservation
(Jha et al. 2014), carbon sequestration (van Rikxoort et al. 2014) and soil protection
(Meylan et al. 2017).
Globally, Arabica (Coffea arabica) and Robusta (Coffea canephora) coffees make up
approximately 99% of global coffee production (Jayakumar et al. 2017). Arabica, which is
often used in speciality coffees, grows best at 1822 °C, while Robusta is of lower quality but
hardier and productive at 2228 °C (Magrach and Ghazoul 2015). Bean quality and yield of
both species decline outside these optimum temperature ranges (Magrach and Ghazoul 2015),
suggesting significant sensitivity to shifts in climatic conditions. Further, as coffee plantations
have, on average, a 30-year lifespan and can remain productive for more than 50 years (Bunn
et al. 2015b), they are likely to be subjected to the influence of climate change and variability.
Smallholder coffee farmers might also be highly vulnerable to changes in climate as adaptation
in perennial crops like coffee may take several or even many years to take effect (Laderach
et al. 2017). From a socio-economic perspective, understanding the extent of climate-driven
impacts on coffee production and the benefits of potential adaptation strategies will be of vital
importance to maintaining and improving coffee productivity and profitability and sustaining
the livelihoods of smallholder producers all over the world.
This review assesses current research on the impacts of climate change and variability,
specifically drought, on coffee production. We systematically examined the literature to
Climatic Change (2019) 156:609630
determine: (i) the geographic distribution of the research; (ii) the types and characteristics of
the impacts investigated; (iii) the methods used to analyse the impacts; (iv) the adaptation
measures involved; and (v) any potential research gaps. On this basis, we identify target areas
for future research to better support sustainable and viable coffee production.
2 Methods
Using the methods outlined in Pickering and Byrne (2014), we conducted a systematic
quantitative review of the academic literature on climate-driven impacts on coffee production.
This is a robust systematic and reproducible approach used to comprehensively survey, select
and categorise the literature on a particular research topic (Pickering et al. 2015).
Applying a set of key search terms, we surveyed the literature in three scholarly electronic
databases (Scopus, Web of Science and Science Direct) in OctoberNovember 2018 to identify
relevant papers. The string of key search words used were combinations of coffeeand
climate,climatic,ENSO,El Niño,La Niña,drought,impact,effect,yield,
productionand productivity. We searched within the abstract, title and keyword database
categories of original research papers published in peer-reviewed English language academic
journals. Publications such as review articles, book chapters, reports and conference proceed-
ings were excluded. However, reference lists in review papers and in the original research
articles were checked for additional academic papers missed in the initial search.
Climate change and variability and drought are also likely to influence the entire coffee
supply chain including harvesting and processing activities; however, such impacts were not
included in this review as our focus was on direct and indirect impacts of climate on coffee
yield (i.e. tonnes of coffee harvested per hectare) and coffee production (i.e. tonnes of coffee
harvested in an area of cultivation).
We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses
(PRISMA) diagram (Moher et al. 2009) to track the process of identifying and selecting
relevant papers for this study (Fig. 1). Using the key search terms listed, we found 339 journal
and review articles in the three above-mentioned databases plus an additional 28 articles from
the citation lists of these, from which we excluded 171 duplicates and any review articles. We
then excluded 162 articles that were neither relevant nor sufficiently focused on the impacts of
climate change or variability or drought on coffee production. Finally, a total of 34 relevant
peer-reviewed articles was selected to be fully examined in this study.
Data on each article were recorded in a customised database, including information on
geographic distribution and spatial scale of studies and types of methods used to investigate the
impacts. Characteristics, sources and outcomes of impacts and adaptation and management
practices mentioned in the literature were also entered into the database to identify patterns and
gaps and to inform future research recommendations.
3 Results and discussion
A total of 34 peer-reviewed research articles that specifically discussed the impacts of climate
change or climate variability or drought, either directly or indirectly, on coffee production were
fully examined. These papers were published in 17 different journals (Table S1 in the
Electronic supplementary material), with the majority in the journals Climatic Change and
Climatic Change (2019) 156:609630 611
PLoS One (eight articles each). The journal Regional Environmental Change had three articles
and the journal Mitigation and Adaptation Strategies for Global Change had two, while each
of the remaining 13 journals had just one article.
Much of this research had been recently published (71% between 2014 and 2018),
indicating an increasing interest in the potential impacts of climate variability and change on
coffee production (Fig. S1 in the Electronic supplementary material).
Existing research mostly focused on Arabica (79%) with less consideration given to
both coffee species, Arabica and Robusta (15%) (Table S1). No study solely concen-
trated on Robusta, despite this variety accounting for approximately 40% of global
production (ICO 2019b). One explanation for this may be that many of the studies
included in this review were conducted in the Americas where Arabica predominates.
Another reason could be the greater heat tolerance of Robusta which might therefore
be considered less vulnerable to rising temperatures than Arabica (Chengappa et al.
2017). However, Robusta may be susceptible to increasing intra-seasonal variability in
temperatures (Bunn et al. 2015b), thus could still be negatively affected by changing
climatic conditions. Given the decreasing bioclimatic suitability for Robusta produc-
tion projected in some global studies, further research for this coffee species, partic-
ularly at finer spatial scales is necessary.
Papers idenfied through
database searching
(N = 339)
Inclusion Eligibility Idenficaon
Papers screened aer
removing all duplicates
and reviews
(N = 196)
Papers excluded through
screening tles and abstracts
(N = 141)
Full-text papers assessed
for eligibility
(N = 55)
Addional papers idenfied
through reference lists
(N = 28)
Papers included in the systemac quantave
literature review synthesis
(N = 34)
Full-text papers excluded due
to not focusing on impacts
on coffee producon or yield
(N = 21)
Fig. 1 Steps taken for the systematic quantitative literature review (adapted from Moher et al. 2009), N, number
of original research papers
Climatic Change (2019) 156:609630
3.1 Geographic distribution of the research
Research in the papers included in this review was predominantly from the Americas (19
papers) with a majority of studies based in Central America (12 papers). Seven papers focused
on coffee production in Africa and four in Asia (Fig. 2). Four papers reported on global studies
covering all three of these continents (Table 1).
Most studies in the Americas were conducted in Brazil (six papers), followed by Mexico and
Nicaragua (four papers each). The remaining research was limited to one or two papers per
country in all three continents. The predominance of research in the Americas might reflect the
fact that the worlds top ten coffee-producing countries in this continent account for more than half
of total global coffee production (Fig. 2). On the other hand, research from countries in Asia,
where many of the other major coffee producers of the world are located, was relatively limited
with only a small number of studies having been undertaken in large coffee-growing countries,
including India and Indonesia. Interestingly, there were no papers targeted at regional, national or
local levels for Vietnam, which is the worlds second largest coffee-producing country with 17%
of global coffee production (ICO 2019a). While Asia is expected to be negatively affected by
climate change (Field et al. 2014), more research on climate-driven impacts on coffee is needed to
support sustainable coffee development in regions with significant levels of production, particu-
larly where communities are highly dependent on coffee cultivation.
Research into climate-driven impacts on coffee production has to date also been limited in
scale (Table 1). Many of current studies primarily consider national (14 papers) and sub-
national (11 papers) scales of production with less attention given to regional (or multinational)
(four papers) or global scales (four papers). This is potentially because coffee data at large
spatial scales are reportedly inadequate and uncertain (Eriyagama et al. 2014) while results of
small-scale research are not easily extrapolated globally (Bunn et al. 2015a).
Asia Africa Americas
Percentage of total coffee production
Number of papers
Number of papers Percentage of total coffee production
Fig. 2 Number of papers by continent reviewed and continental percentage of total global coffee production
(ICO 2019b)
Climatic Change (2019) 156:609630 613
Table 1 List of reviewed original research papers on climate-driven impacts on coffee production published to November 2018
Reference Location Spatial
Method Type and overall result of impact
Bunn et al.
(2015a)Global G CC Modelling (random forest) ± An overall global loss of suitable areas for coffee by 2050s
Bunn et al.
(2015b)Global G CC Modelling (support vector
machines, random
forest, MaxEnt)
± A global loss of 50% of suitable areas by 2050
Magrach and
Global G CC Modelling (MaxEnt) ± A drop of 56% of current suitable areas for Arabica and 55% for Robusta
by 2050. Future suitable areas for Robusta could be double. Distribution
of coffee berry borer would increase
et al.
Global G CC Modelling (MaxEnt) ± An average loss of 19% of global suitable areas by 2050
The Americas
Imbach et al.
(2017)Latin America R CC Modelling (MaxEnt) ± ± A loss of 7388% of suitable areas by 2050. A drop of 818% in bee
richness in future suitable areas, but pollination services are expected to
Baca et al.
(2014)Mesoamerica (Mexico,
Guatemala and El
R CC Modelling (MaxEnt) Reductions of at least 40% of suitable areas in 28% of total areas; 2040%
in 34% of areas and under 20% in 36% of areas by 2050
Harvey et al.
(2018)Central America (Costa
Rica, Honduras and
R CC Interview −−Negative impacts on yield and increases in pest and disease outbreak
et al.
Central America and
Colombia R & N CV Document analysis −−A decline of 31% in production for 20082011 compared with 2007 in
Colombia; 16% for 20122013 compared with 20112012 in Central
et al.
Colombia N CV Modelling (econometric
model) ± Coffee production gains benefits from El Niño but loses from La Niña
Bacon et al.
(2017)Nicaragua N D &
CV Surveys, interviews, focus
groups and modelling
(statistical analysis)
−−Harvest losses of 6072% from 20112012 to 20132014
et al. Nicaragua N CC Modelling (MaxEnt and
CaNaSTA) ±±
Climatic Change (2019) 156:609630
Table 1 (continued)
Reference Location Spatial
Method Type and overall result of impact
(2017)Alossof1025% of currently suitable areas by 2050. A decline in
suitability to produce good quality coffee beans. Suitability will move to
higher elevations
Fain et al.
(2018)Puerto Rico N CC Weighted overlay analysis
in GIS
Alossof6084% of highly suitable municipalities by 2070
Alves et al.
(2011)Brazil N CC Modelling (non-linear
A shift toward the south in areas favourable for coffee rust
(2011)Brazil N CC GIS spatial analysis A decrease in the incubation period and thus more severe epidemics
(2008)Brazil N CC GIS spatial analysis An increase in pest infestation and number of generations
Verh ag e
et al.
Brazil N CC Modelling (Arabica coffee
yield model) ± Yield will reduce by 7.5% in 20402070 but can increase 0.8% due to CFE
Junior et al.
(2006)Brazil SN CC Modelling (agricultural
A reduction of 41 and 70% of suitable areas if temperature increases by 1
and 3 °C, respectively
Tavares et a l.
(2018)Brazil SN CC Modelling (agroclimatic
−− Losses of 3664% of current suitable areas and 25% of Arabica yield by
Estrada et al.
(2012)Mexico SN CC Modelling (econometric
Costs of climate change for coffee production are estimated to be 3 to 14
times (2731273 million dollars) the current value of coffee
Gay et al.
(2006)Mexico SN CC Modelling (econometric
Adropof1934% in production by 2020
et al.
Mexico SN CC Modelling (MaxEnt) A strong decline of 98% of currently highly suitable areas by 2050s
Rahn et al.
(2014)Nicaragua SN CC Modelling (MaxEnt) −− Adecrease of climatic suitability for coffee cultivation
Guido et al.
(2018)Jamaica SN D Interview and focus group Lower quality and quantity of coffee, leading to lower production
et al.
Nepal N CC Modelling (an ensemble of
19 SDM algorithms)
A drop of 72.6±4.4% of current suitable areas by 2050. Only 11.9± 2.3%
of new areas become suitable for coffee
Climatic Change (2019) 156:609630 615
Table 1 (continued)
Reference Location Spatial
Method Type and overall result of impact
et al.
Indonesia N CC Modelling (MaxEnt) ± An overall loss of 33% of suitable areas by 2050
et al.
India SN CV Interview ± A decrease in Arabica yield and an increase in Robusta in the past 10 years
et al.
India SN CV Modelling (statistical
analysis) ± A decline in production during 20012006 due to rising temperature.
Arabica yield was adversely impacted during strong El Niño years
et al.
East Africa R CC Modelling (CLIMEX) An increase in number of pest generations from 5 to 10/year
et al.
Zimbabwe N CC Modelling (boosted
regression trees and
generalised linear
An increase in suitable areas for the pest by 1662% by 2080
et al.
Zimbabwe N CC MaxEnt ± A loss of 8.313.8% of suitable areas by 2050
Moat et al.
(2017)Ethiopia N CC Modelling (an ensemble of
6 SDM methods) ± A decline of 3959% of current suitable areas by 2100
et al.
Tanzania N CC Modelling (statistical
A loss of 244 ±41 kg/ha in yield by 2030 and 145 ± 41 kg/ha by 2060
without adaptation
Davis et al.
(2012)Ethiopia, Sudan and
Kenya SN CC Modelling (MaxEnt) Reductions of 65100% of suitable localities; 3890% of suitable areas by
Rahn et al.
(2018)Uganda and Tanzania SN CC Modelling (process-based
model) ± A decline of 32% in yield at low altitude areas by a 2.5-degree temperature
increase without carbon fertilisation effect (CFE) consideration. If with
CFE, negative impacts can be offset by 1321%
G,global;R, regional; N, national; SN, sub-national; CC, climate change; CV, climate variability; D, drought; P, production; S, suitability; Q, quality; PE,pest;DI,disease;PS,
pollination services; +positive impact; negative impact
Climatic Change (2019) 156:609630
3.2 Sources and types of impacts
In this assessment, sources of impacts were classified into three groups: climate change,
climate variability and drought. Impacts were also categorised as direct (i.e. variations in yield
or production or in bioclimatically suitable areas for coffee cultivation) and indirect (i.e.
changes in coffee quality or in the distribution of pests or diseases or pollination services).
In total, 12 studies examined direct impacts of climate change or climate variability, while
only two addressed direct impacts of drought on coffee yield or production. Seventeen studies
analysed direct impacts of climate change on bioclimatic suitability for coffee cultivation,
driving changes in optimal coffee-growing areas. The remaining studies reported indirect
impacts of climate variability or climate change with ten studies on pest and disease distribu-
tion and one each on pollination activities and coffee quality (Table 1).
Much of the literature reviewed focused on the influence of climate change or climate
variability, indicating increasing recognition of their potential impacts on coffee production. In
contrast, the number of studies on drought impacts was small despite reports of severe
droughts in some coffee-growing areas such as Central America (Baca et al. 2014;Guido
et al. 2018). As drought is a major climatic constraint for coffee production (DaMatta and
Ramalho 2006) and expected to increase in frequency and severity in many regions across the
world under climate change (Field et al. 2014), more research specifically on its impacts and
on adaptation solutions should be considered for drought-prone coffee cultivation areas.
Further, current research is dominated by studies that project changes in the distribution of
areas suitable for growing coffee, with less consideration given to analysis of direct effects on
coffee yield, or indirect effects on pest and disease distribution as a result of changes in
climate. As some of the major coffee pests and diseases will likely benefit from rising
temperatures, more research on their responses to changing climatic conditions and on
adaptation mechanisms to minimise exposure and vulnerability of the coffee crop to these
risks is needed.
3.3 Methods used in the research
A variety of research methods has been used to investigate coffees exposure to climate risks.
Quantitative methods (29 papers) were predominant over qualitative methods (four papers),
with only one study using mixed methods.
Qualitative approaches used interviews (four papers), focus groups (two papers), household
surveys (one paper) and document analysis (one paper) to explore the influence of climate
change or climate variability or drought either directly on coffee production or indirectly on
pest and disease distribution. Further application of these methods in future research would
benefit assessments on climate-driven impacts and adaptation of coffee production systems as
they can provide context-specific information including the perceptions and experiences of
local farmers and their responses to climate change.
Quantitative studies included a range of modelling approaches aimed at investigating the
influence of climate variability and change in coffee production systems (Table 1). Many
studies used machine-learning techniques (15 papers), particularly Maximum Entropy
(MaxEnt; 13 papers), of which most focused on current and future climatic suitability for
coffee cultivation.
MaxEnt is a popular method for determining the spatial distribution and the environmental
niche of species (Elith et al. 2011; Merow et al. 2013). Its predominance is probably due to its
Climatic Change (2019) 156:609630 617
ability to easily extrapolate (Fitzpatrick et al. 2013) and provide improved outputs with
presence-only species data (Elith et al. 2011; Mateo et al. 2010) compared with other
correlative ecological niche models. MaxEnt has been widely used to project species distri-
bution ranges in ecology (Merow et al. 2013) and might be suitable for a climate-sensitive crop
such as coffee, especially in the context of data limitations in many coffee cultivation areas, as
noted above.
Other types of ecological niche modelling employed, included machine-learning techniques
such as random forest (four papers), boosted regression trees (three papers) and support vector
machines (two papers) and regression-based methods such as generalised linear model (three
papers), generalised additive model (two papers) and multivariate adaptive regression splines
(two papers).
Fewer studies applied statistical analysis (four papers) and econometric models (three
papers) to analyse direct impacts of climate change or climate variability on coffee production,
or on changes in pest and disease distribution. Several studies used other modelling methods
such as agricultural zoning (two papers) and other types of species distribution modelling (two
While studies using MaxEnt or other bioclimatic modelling approaches have estimated the
potential distribution in areas of suitability for coffee production under current and future
climates, they have yet to include phenotypic plasticity (Nicotra et al. 2010)ormechanistic
processes to predict the responses (Rahn et al. 2018) of the coffee plant to changes in climate
or the effect of adaptation measures. For example, the potential influence of carbon fertilisation
on coffee physiology as a result of rising carbon dioxide in the atmosphere could, if
considered, provide somewhat different results. Elevated carbon concentration might enhance
the photosynthetic process and increase yield (Ghini et al. 2015; Rodrigues et al. 2016),
potentially mitigating, at least partially, the harmful impacts of warming climatic conditions on
coffee yield (Verhage et al. 2017). Thus, projections that failed to take this into account might
have over-estimated yield impacts (Rahn et al. 2018). However, Moat et al. (2017)arguedthat
increasing drought stress, together with the potential effects of deforestation on local climate,
could outweigh this beneficial influence in the long run. These interactions depend on
particular contexts and therefore require further investigation.
The use of mechanistic or process-based models to analyse potential climate-driven impacts
on coffee production in current research was limited, being represented by one study (Rahn
et al. 2018) which explored responses of the coffee plant to interactions between atmospheric
carbon dioxide enhancement, increased temperature and water scarcity and the efficacy of
shade management. Mechanistic modelling has been widely applied in agricultural research
into the impacts of climate change on the performance of crops such as wheat, maize and rice
(Kang et al. 2009; White et al. 2011). Such models could be a valuable approach to better
understanding climate change impacts, including the effect of modified microclimate under
management practices on coffee production systems, allowing analysis of interactions between
climate, soil and coffee plant parameters (Rahn et al. 2018). However, uncertainties may arise
where there are insufficient data on coffee performance and ecological conditions for model
calibration (Luedeling et al. 2014), which might be the case for many coffee-producing
Correlative species distribution models have been broadly applied to predict potential shifts
in the distribution of species under scenarios of future climate (Franklin 2010; Kearney et al.
2010). These methods exclusively focus on geographic distribution and generally involve only
location data and corresponding environmental conditions of existing areas (Luedeling et al.
Climatic Change (2019) 156:609630
2014; Machovina and Feeley 2013). Future species distribution is projected solely based on
the relationship between current distribution assuming to remain constant and climate
(Dormann 2007;Thuilleretal.2005) without taking account of the speciesgenetic structure
and the influence of limiting factors, biotic interactions and other disturbances and processes
that may be affected by changing climatic conditions (Evans et al. 2016; Fitzpatrick and
Hargrove 2009). Process-based models, on the other hand, are able to capture the dynamics
underpinning species distributions across spatial and temporal scalesincluding physiology,
biotic interactions and other factorsunder environmental change, and hence can provide
more credible projections than species distribution modelling (Evans et al. 2016). Neverthe-
less, these models generally require many parameters for estimations, thus involve large data
requirements which often cannot be met due to limitations at high resolutions (Dormann et al.
2012). Application of process-based models, particularly for planning adaptation of coffee
production systems to climate change deserves additional examination.
Current studies on climate change impacts on the suitability of coffee-growing areas use a
range of climate models with diverse levels of spatial resolution, ranging from 30 arc-seconds
(1 km2) to 30 arc-minutes (50 km2), which may explain the wide range of reported estimates.
Coarse spatial resolutions may fail to capture local characteristics such as the heterogeneous
topography of coffee-growing areas. Uncertainties and errors may increase due to the process
of downscaling and interpolating climate projection data (Fain et al. 2018) where agricultural
landscapes exhibit topographic heterogeneity (Daly et al. 2003). Low temporal and spatial
resolution of climate models also pose challenges in linking climate scenarios to biological
responses, including pest or disease development, which entail daily or even hourly data
(Ghini et al. 2008,2011). The use of models with high spatial and temporal resolution would
benefit climate impact simulations, facilitating the capture of non-homogenous topographies
and thus better representing microclimatic characteristics (Tavares et al. 2018) and reducing
uncertainties through the use of more refined climate data (Ghini et al. 2011).
Assessment of uncertainties related to climate variables and scenarios, interpolation pro-
cesses used for climate projection data, model parameters, socio-economic factors and inter-
actions between the coffee plant and the environment is still limited in current research. Only a
few studies (Estrada et al. 2012;Rahnetal.2018; Verhage et al. 2017)partlyorexplicitly
analysed uncertainty. One suggested solution for minimising uncertainties due to biased
representation of suitable climate is to incorporate outputs from a multimodel ensemble to
provide improved predictions (Bunn et al. 2015b; Ranjitkar et al. 2016). It should be noted that
ensemble modelling, however, might produce incorrect outcomes resulting from errors and
biases in the individual species distribution models (Beaumont et al. 2016).
3.4 Impacts of climate variability and change on coffee production
Of all studies investigating the impacts of climate variability and change or drought on coffee
production examined in this review, 20 indicated negative impacts and 14 reported mixed
results (Table 1). Four papers using qualitative approaches described observed negative
consequences on coffee production and on the distribution of pests and diseases, and only
one paper presented mixed effects, with perceived declines in Arabica but increases in Robusta
yield in India (Chengappa et al. 2017). Quantitative studies, on the other hand, demonstrated
more varied results, specifically in projected outcomes under climate change scenarios.
However, none of the current studies reviewed suggested wholly positive outcomes.
Climatic Change (2019) 156:609630 619
Of studies on the direct impacts on coffee yield or production, nine papers indicated
negative outcomes and five revealed both positive and negative results. Harvest losses due
to drought and climate variability were reported mostly in the Americas and could be as much
as 70% (Bacon et al. 2017). Fewer studies analysed reductions in coffee production as a result
of climate change; such impacts were identified in Tanzania (Craparo et al. 2015), Mexico
(Estrada et al. 2012; Gay et al. 2006) and Brazil (Verhage et al. 2017). Studies showing mixed
results included positive outcomes of El Niño intra-decadal climate phases on coffee produc-
tion and exports in Colombia (Bastianin et al. 2018), increases in Robusta yield in India due to
climate variability (Jayakumar et al. 2017) and in Arabica yield in Brazil and Nicaragua owing
to carbon fertilisation effect (Rahn et al. 2018; Verhage et al. 2017).
In terms of suitability for growing coffee, all relevant studies revealed decreases or losses in
areas suitable for coffee. Bunn et al. (2015b) indicated an overall global loss of up to 50% of
optimal areas for both types of coffee by 2050, which is in line with other global studies (Bunn
et al. 2015a; Ovalle-Rivera et al. 2015) with large parts of major coffee producers such as
Brazil, Vietnam, Honduras and India becoming unsuitable. In studies at regional and national
levels, the greatest reductions in suitability were projected for Ethiopia, Sudan and Kenya (up
to 90% by 2080; Davis et al. 2012), Puerto Rico (84% by 2070; Fain et al. 2018), Mexico
(98% by the 2050s; Schroth et al. 2009); and Latin America (88% by 2050; Imbach et al.
Key drivers of projected shifts in bioclimatic suitability for coffee cultivation are temper-
ature and precipation variables. Global studies indicated that precipitation factors such as
annual and seasonal precipitation were of less importance compared with temperatures in
determining suitability (Bunn et al. 2015b; Ovalle-Rivera et al. 2015). In contrast, national
(Chemura et al. 2016) and sub-national (Rahn et al. 2014) studies revealed that the amount and
distribution of precipitation significantly influence coffee suitability. Despite recent improve-
ments in the simulation of changes in precipation patterns, there is currently greater confidence
in the ability of climate models to predict surface temperature changes (IPCC 2014). Increas-
ing certainty in predicting future precipitation patterns at all scales will likely improve
projections on coffee-favourable areas.
While a majority of existing literature specified substantial reductions in the suitability of
coffee-growing areas globally, regionally and nationally, a few papers indicated that, under a
changing climate, areas which are currently less optimal for coffee cultivation may become
more productive. For example, several studies projected increases in coffee-suitable areas in
South America, East and Central Africa and Asia (Bunn et al. 2015b; Magrach and Ghazoul
2015; Ovalle-Rivera et al. 2015; Schroth et al. 2015). Generally, suitability is predicted to shift
to higher altitudes by many studies. Globally, Bunn et al. (2015b) indicated that areas at higher
latitudes may be less affected while Ovalle-Rivera et al. (2015) suggested that they might
decline in suitability, particularly in South America. Some regions projected to be favourable
for coffee cultivation are open land such as those in East Africa (Bunn et al. 2015b;Ovalle-
Rivera et al. 2015) but others, particularly in the Amazon basin, Asia and Central Africa, are
currently under forest cover (Bunn et al. 2015b), protected areas (Schroth et al. 2015)orother
agricultural land uses (Magrach and Ghazoul 2015). The continued expansion of coffee
production to meet growing global demand (ICO 2019a) might generate economic opportu-
nities in some regions but induce adverse socio-economic and environmental impacts associ-
ated with deforestation for coffee cultivation (Gaveau et al. 2009; Meyfroidt et al. 2013)
elsewhere. Furthermore, open land at high elevations might be remote (Schroth et al. 2015)o
too steep for growing coffee (Bunn et al. 2015a) and operating farming machinery (Tavares
Climatic Change (2019) 156:609630
et al. 2018) or have soil that is too shallow (Bunn et al. 2015a; Chemura et al. 2016)orpoor
(Schroth et al. 2015). Shifting coffee-growing areas upslope might also incur conflicts with
protected areas with significant ecosystem service values or other land uses with crops in
higher demand than coffee (Magrach and Ghazoul 2015). Therefore, the feasibility of offset-
ting losses from areas with declining suitability by expansion or shifts to newcoffee-optimal
areas needs additional investigation. Explicit research on future distribution of climatically
favourable regions for coffee production which identifies and assesses potential conflicts and
trade-offs with existing land uses, particularly at local scales, is required.
Negative results of indirect climate-related impacts on coffee production were reported in
all studies on pests and diseases (ten papers), pollination services (one paper) and coffee
quality (one paper). These included expected increases in the distribution of pests such as the
coffee berry borer (Magrach and Ghazoul 2015) and coffee white stem borer (Kutywayo et al.
2013) and in their reproductive rate (Jaramillo et al. 2011). Diseases such as coffee rust already
damaged large parts of production areas in Colombia, Central America and Nicaragua
(Avelino et al. 2015; Bacon et al. 2017). There were projected decreases in the incubation
period of coffee rust which may result in more severe epidemics (Ghini et al. 2011) and in
future pollinator richness in Latin America (Imbach et al. 2017) which may affect coffee
production. One study, in Nicaragua, also suggested that the quality of coffee beans may be
negatively impacted (Laderach et al. 2017).
In summary, most of the current literature indicates negative consequences of climate
change and variability or drought on coffee production. However, positive impacts including
increases in coffee yield or in suitability of coffee-cultivating areas, particularly at higher
elevations, are also reported on all three coffee-producing continents. Climate change might
also bring other advantages, such as growth in pollination activities owing to increasing bee
richness (Imbach et al. 2017), resulting in positive effects on coffee yield (Roubik 2002). Some
coffee cultivation areas may also benefit from elevated carbon concentration, which may
enhance the photosynthetic rate (Trumble and Butler 2009) and heat tolerance of the plant,
leading to crop growth and yield improvements (DaMatta et al. 2016; Rodrigues et al. 2016).
Further work is needed to investigate the potential of pollination services and carbon
fertilisation effect to counteract negative impacts of climate change on coffee production.
3.5 Adaptation measures
Adaptation and management practices were identified by more than 70% of total studies (25
papers), of which agroforestry, either through intercropping or shading, was most common (18
papers), followed by irrigation and efficient use and management of water (12 papers),
development of new cultivars that are drought and heat-stress resistant and/or pest and disease
tolerant (ten papers) and diversification of cropping patterns or livelihood activities (nine
papers) (Fig. 3). Other measures included relocation of coffee plantations to more
bioclimatically suitable areas (six papers), crop insurance (three papers), off-farm livelihoods
(two papers),and shifts from Arabica to Robusta or cocoa (two papers).
Existing studies indicated that climate variability and change have directly or indirectly
affected global coffee production to varying extents, with the majority of these indicating
negative impacts. However, most did not quantitatively take account of the influence of
adaptation measures which, if adopted, could potentially reduce these impacts. Quantitative
analysis of adaptation was limited to just one study which demonstrated the beneficial effects
of shade trees on coffee yield at lower elevations (Rahn et al. 2018).
Climatic Change (2019) 156:609630 621
Relocation of coffee plantations to areas more climatically suitable for cultivation, partic-
ularly coolregions at higher altitudes (Laderach et al. 2017), was recommended in a number of
studies examining coffee suitability. However, migration to higher elevations might lead to
increased pressure on local ecosystems and might be challenged by topography and soil
characteristics (Chemura et al. 2016), land tenure rights (Schroth et al. 2009), access to
infrastructure (Moat et al. 2017) and ability and willingness of farming communities
(Chemura et al. 2016; Magrach and Ghazoul 2015). While high elevations might be more
climatically suitable for coffee, additional investigation is needed, with particular attention
placed on potential opportunities and challenges, to ensure viable and sustainable coffee
development in these areas.
Given the challenges associated with shifting coffee production to more climatically
favourable areas, various in situ strategies should be further examined, including irrigation
and shading existing coffee plantations to mitigate the adverse impacts of rising temperatures
and drought stress and diversification to encourage alternative crops or income sources to
assist coffee producers to cope with the impacts of declining coffee yields.
As a result of increasing temperatures and changes in precipitation, irrigation is considered
one of the most important adaptive responses in many coffee-growing regions. Optimal use of
water may include improved water storage and delivery (Baca et al. 2014; Chemura et al.
2016) through creating tanks and tube-wells and deepening existing bore-wells (Chengappa
et al. 2017; Jayakumar et al. 2017) to enable irrigating coffee, particularly during droughts and
dry periods. Surface water extraction from rivers and streams might be a cost-effective (Moat
et al. 2017) temporary solution but is likely to be constrained during prolonged dry spells or
Drip, supplemental full or deficit irrigation has been demonstrated to improve coffee quality
in Ethiopia (Tesfaye et al. 2013) and productivity in Brazil (Fernandes et al. 2016), especially
in periods of water scarcity. However, investment in irrigation infrastructure including storage
and transportation systems or in technologies like drip irrigation or water harvesting (Baca
et al. 2014; Chengappa et al. 2017) is likely to be resource and labour intensive and costly and
0 2 4 6 8 10 12 14 16 18 20
Irrigation and efficient water use
New cultivars
Crop insurance
Off-farm livelihoods
Arabica replacement
Number of papers
Fig. 3 Adaptation measures considered in the 34 reviewed studies
Climatic Change (2019) 156:609630
thus will be disadvantageous for small growers with limited capital and access to finance
(Bryan et al. 2013). Such technological adaptation measures will likely require substantial
government or industry support.
Agroforestry systems were mentioned as a potential adaptation strategy for coffee produc-
tion systems which may benefit from shading or inter-cropping with other crops. Inter-
cropping coffee with banana and macauba, for example, has proven more profitable than
mono-cropping in Africa and South America; such systems reportedly reduce air temperatures
and photosynthetic active radiation and increase coffee yield and productivity (Moreira et al.
2018; van Asten et al. 2011).
Shade trees may create a microclimate that provides various socio-economic and ecological
benefits, including improved coffee quality (Nesper et al. 2017;Vaastetal.2006), increased
diversity of income sources (Chengappa et al. 2017; Jezeer et al. 2018) and provision of
ecosystem services (Cerda et al. 2017;Meylanetal.2017). Specifically, shading could reduce
the mean and maximum air temperatures experienced by the coffee plants compared with full-
sun coffee systems (Ehrenbergerová et al. 2017; Moreira et al. 2018), lower wind speeds
(Pezzopane et al. 2011) and the risk of landslides (Philpott et al. 2008), enhance pest
suppression (Jaramillo et al. 2013) and pollination activities (Jha et al. 2014) and improve
soil conservation and water quality (Meylan et al. 2017).
Coffee grown under shade cover, however, might be less productive due to competition
with shade trees for water (Ehrenbergerová et al. 2017;Rahnetal.2018), light (Charbonnier
et al. 2013) and nutrients (van Oijen et al. 2010). Additional research into the microclimate
dynamics of shade systems, the selection of appropriate tree species, densities and technologies
and the interactions between coffee physiology and shade trees under various climatic
conditions will be necessary. Shade systems may also vary in their effects (positive or
negative) on pests and diseases subject to specific environmental conditions (Jonsson et al.
2015;Liebigetal.2016). Greater insight into potential synergies and trade-offs in shaded
coffee plantations is needed to ensure appropriate responses to climate change in coffee
production systems.
Other adaptation measures mentioned in current research involve opportunities to diversify
coffee farmerssources of incomesuch as off-farm labour, alternative cropping systems
including fruit tree production (Bacon et al. 2017) and multicrop cultivation including pepper
on shade trees (Chengappa et al. 2017)and introduction of coffee varieties with better
tolerance to high temperatures and pest and disease pressures (Ovalle-Rivera et al. 2015;
Schroth et al. 2009). While smallholder farmers, using existing resources, might have the
capacity to develop shading systems in coffee plantations and produce a variety of other crops,
technological solutions such as development of new cultivars will require significant govern-
ment or industry investment of capital, labour and expertise. A shift from Arabica to Robusta is
recommended for zones at low altitudes in Nicaragua where significant reductions in climatic
suitability for Arabica is projected (Laderach et al. 2017) and has been implemented in India to
confront coffee white stem borer caused by climate variability (Chengappa et al. 2017).
Improved profitability of Robusta in comparison with Arabica owing to its lower cultivation
costs and higher yield was reported by Indian producers (Chengappa et al. 2014). Neverthe-
less, Arabica is considered superior in beverage quality to Robusta and realises higher prices;
thus, whether and where it can be replaced by the latter require further examination. Crop
insurance against the increased risks of extreme events has been implemented to assist coffee
producers in Mexico but with limited success due to inadequate government funding and
coordination (Schroth et al. 2009).
Climatic Change (2019) 156:609630 623
In summary, a variety of adaptation measures to manage climate-driven impacts
on coffee production are identified in the literature. However, several qualitative
studies have indicated that, while most farmers were aware of the impacts of
climate on their farming and livelihoods, they were not active in adopting these
measures into their management practices (Chengappa et al. 2017;Harveyetal.
2018). Adaptation should be tailored to specific farming conditions and socio-
economic contexts and consider the capacity of coffee farmers, who are mostly
smallholders, to access finance, credit, resources and technologies. Temporal chal-
lenges required for some adaptation measures, such as replanting with new culti-
vars for heat-stress tolerance and agroforestry systems which might take several
years or even decades to become effective (Eske and Leroy 2008; Laderach et al.
2017), should also be taken into account. Raising awareness, building capacity,
enhancing knowledge and experience exchange and providing technical and finan-
cial support should be emphasised to facilitate adaptation implementation and
strengthen farmer resilience to climate variability and change. An integrated
approach that incorporates flexible strategies might be required to address interac-
tions between agricultural and ecological aspects of change (Hannah et al. 2017).
Finally, a combination of appropriate policy measures, technical solutions and
research outcomes and recommendations is crucial to facilitate adaptation process-
es amongst coffee smallholders.
4 Key conclusions and knowledge gaps
This paper offers a systematic quantitative analysis of the academic literature on the
impacts of climate change and variability and drought on coffee production. An array
of mostly negative outcomes was found in current studies. These included declines in
coffee yield and in areas of suitability for coffee cultivation and increases in the
distribution of pests and diseases that indirectly influence coffee production. Globally,
indications are that there will likely be a loss of coffee-optimal areas with consider-
able impacts in major coffee-growing countries such as Brazil and Vietnam. Suitabil-
ity is generally projected to shift to higher altitudes. Some areas of lower suitability
might become more productive in the future but many of them are currently under
other crops or forest cover. Investigationisrequiredtoevaluatewhethergainsin
coffee-growing niche in newareas might compensate for losses with declining
suitability in other areas, with particular attention given to trade-offs with existing
land uses. Further research on future distribution of coffee-favourable space with
consideration to potential ecological and socio-economic impacts and associated op-
portunities and challenges is necessary to better support sustainable coffee
Our selection criteria may have excluded relevant publications from other sources
including peer-reviewed literature published in non-English language journals and
greyliterature such as reports and conference proceedings. Despite this, the review
reveals some significant knowledge gaps on the topic. These include the dispropor-
tionate concentration of current studies in the Americas with less attention given to
Asia where a number of countries are amongst the worlds major coffee producers.
The predominance of current research in the Americas has drawn more focus of the
Climatic Change (2019) 156:609630
research on Arabica with limited consideration of Robusta, particularly at national
and sub-national scales, and of the influence of climate change on coffee suitability
rather than coffee yield or pest and disease distribution. As the risks of pest and
disease outbreaks are likely to increase, there is a need for research on these
pressures under changing climatic conditions. Further, little research has specifically
analysed the impacts of drought on coffee production in contrast to the more
extensive literature on the effects of climate variability and change. Apart from
relocating coffee plantations to more favourable areas, potential in situ adaptation
measures suggested in the literature included agroforestry, irrigation and water
management, development of new varieties and diversification of alternative crops
or livelihoods. However, quantitative analysis on the effects of adaptation in miti-
gating climate change impacts was notably absent due to limitations in the model-
ling approaches applied in the research.
A range of models was employed to investigate the influence of climate change
with the majority focused on the distribution of bioclimatic suitability for coffee
cultivation, using bioclimatic modelling approaches including machine-learning and
regression-based techniques. Due to the limited ability of correlative species distribu-
tion models to incorporate underlying factors and dynamic processes and their inter-
actions operating across spatial and biological scales, we suggest further exploration
of process-based models for coffee production systems such as those developed and
widely applied for wheat, rice and maize. This will generate improved analysis of
climate-driven impacts and of the effects of adaptation and management strategies to
support decision-making for sustainable coffee production.
Further, increased knowledge is required regarding positive influences on coffee produc-
tion, including the potential of elevated carbon concentration to offset negative impacts of
warmer conditions and of pollination activities.
Finally, there is a need for inclusion of socio-economic factors and detailed analysis of
the rationale of suggested response measures along with their quantified benefits in
adapting coffee to climate change. While the economic benefits of these measures under
changing climatic conditions are uncertain, a thorough evaluation for specific farming
contexts will likely be beneficial to coffee farmers. Given the long lifespan of coffee
plantations, a focus of research on these issues could mitigate some of the long-term
consequences of climate change on the coffee industry and on the livelihoods of many
smallholder farmers throughout the tropics.
In total, 34 relevant peer-reviewed journal articles were found and analysed in this
review, which is a relatively small number compared with studies on climate change
impacts on other crops such as wheat, maize and rice (Challinor et al. 2014;Knoxetal.
2016; White et al. 2011). Given the significant contribution of the coffee sector to global
socio-economic development, particularly to the livelihoods of millions of smallholders,
more research on the climate-driven impacts is required for coffee production systems.
This should focus on the direct and indirect effects on yield, particularly in production
areas across Asia, on Robusta coffee and on the efficacy of adaption in maintaining the
sustainability and viability of the coffee industry.
Acknowledgements The authors gratefully appreciate advice from Dr. Tricia Kelly for the literature search and
the valuable suggestions and feedback from two anonymous reviewers.
Climatic Change (2019) 156:609630 625
Funding information We would like to acknowledge the German Federal Ministry for the Environment,
Nature Conservation, Buildingand Nuclear Safety through the International Climate Initiative and the University
of Southern Queensland for funding this research.
Alves MdC, de Carvalho LG, Pozza EA, Sanches L, Maia JCdS (2011) Ecological zoning of soybean rust, coffee
rust and banana black sigatoka based on Brazilian climate changes. Procedia Environ Sci 6:3549.
Avelino J et al (2015) The coffee rust crises in Colombia and Central America (20082013): impacts, plausible
causes and proposed solutions. Food Sec 7:303321.
Baca M, Läderach P, Haggar J, Schroth G, Ovalle O (2014) An integrated framework for assessing vulnerability
to climate change and developing adaptation strategies for coffee growing families in mesoamerica. PLoS
ONE 9.
Bacon CM, Sundstrom WA, Stewart IT, Beezer D (2017) Vulnerability to cumulative hazards: coping with the
coffee leaf rust outbreak, drought, and food insecurity in Nicaragua. World Dev 93:136152. https://doi.
Bastianin A, Lanza A, Manera M (2018) Economic impacts of El Nino southern oscillation: evidence from the
Colombian coffee market. Agric Econ 49:623633.
Beaumont LJ et al (2016) Which species distribution models are more (or less) likely to project broad-scale,
climate-induced shifts in species ranges? Ecol Model 342:135146.
Bryan E, Ringler C, Okoba B, Roncoli C, Silvestri S, Herrero M (2013) Adapting agriculture to climate change
in Kenya: household strategies and determinants. J Environ Manag 114:2635.
Bunn C, Läderach P, Jimenez JGP, Montagnon C, Schilling T (2015a) Multiclass classification of agro-ecological
zones for Arabica coffee: an improved understanding of the impacts of climate change. PLoS ONE 10.
Bunn C, Läderach P, Ovalle Rivera O, Kirschke D (2015b) A bitter cup: climate change profile of global
production of Arabica and Robusta coffee. Clim Chang 129:89101.
Cerda R et al (2017) Effects of shade, altitude and management on multiple ecosystem services in coffee
agroecosystems. Eur J Agron 82:308319.
Challinor AJ, Watson J, Lobell DB, Howden SM, Smith DR, Chhetri N (2014) A meta-analysis of crop yield
under climate change and adaptation. Nat Clim Chang 4:287.
Charbonnier F et al (2013) Competition for light in heterogeneous canopies: application of MAESTRA to a
coffee (Coffea arabica L.) agroforestry system. Agric For Meteorol 181:152169.
Chemura A,Kutywayo D, Chidoko P, Mahoya C (2016) Bioclimatic modelling of current and projected climatic
suitability of coffee (Coffea arabica) production in Zimbabwe. Reg Environ Chang 16:473485. https://doi.
Chengappa PG, Rich KM, Rich M, Muniyappa A, Yadava CG, Pradeepa BB (2014) Promoting conservation in
India by greening coffee: a value chain approach. Norwegian Institute of International Affairs (NUPI)
working paper 831. Accessed 08/07/2019
Chengappa PG, Devika CM, Rudragouda CS (2017) Climate variability and mitigation: perceptions and
strategies adopted by traditional coffee growers in India. Clim Dev 9:593604.
Cohn AS et al (2017) Smallholder agriculture and climate change. Annu Rev Environ Resour 42:347375.
Craparo ACW, Van Asten PJA, Läderach P, Jassogne LTP, Grab SW (2015) Coffea arabica yields
decline in Tanzania due to climate change: Global implications. Agric For Meteorol 207:110.
Daly C, Helmer EH, Quiñones M (2003) Mapping the climate of Puerto Rico, Vieques and Culebra. Int J
Climatol 23:13591381.
DaMatta FM, Ramalho JDC (2006) Impacts of drought and temperature stress on coffee physiology and
production: a review. Braz J Plant Physiol 18:5581.
Climatic Change (2019) 156:609630
DaMatta FM et al (2016) Sustained enhancement of photosynthesis in coffee trees grown under free-air CO2
enrichment conditions: disentangling the contributions of stomatal, mesophyll, and biochemical limitations.
J Exp Bot 67:341352.
Davis AP, Gole TW, Baena S, Moat J (2012) The impact of climate change on indigenous Arabica
coffee (Coffea arabica): predicting future trends and identifying priorities. PLoS ONE 7:e47981.
Dormann CF (2007) Promising the future? Global change projections of species distributions. Basic Appl Ecol 8:
Dormann CF et al (2012) Correlation and process in species distribution models: bridging a dichotomy. J
Biogeogr 39:21192131.
Ehrenbergerová L, Šenfeldr M, Habrová H (2017) Impact of tree shading on the microclimate of a coffee
plantation: a case study from the Peruvian Amazon. Bois For Trop 4:1322
Elith J, Phillips SJ, Hastie T, Dudík M, Chee YE, Yates CJ (2011) A statistical explanation of MaxEnt for
ecologists. Divers Distrib 17:4357. htt ps://
Eriyagama N, Chemin Y, Alankara R (2014) A methodology for quantifying global consumptive water use of
coffee for sustainable production under conditions of climate change. J Water Clim Chang 5:128150.
Eske AB, Leroy T (2008) Coffee selection and breeding. In: Coffee: growing, processing, sustainable production.
pp 5786.
Estrada F, Gay C, Conde C (2012) A methodology for the risk assessment of climate variability and change under
uncertainty. A case study: coffee production in Veracruz, Mexico. Clim Chang 113:455479. https://doi.
Evans MEK, Merow C, Record S, McMahon SM, Enquist BJ (2016) Towards process-based range modeling of
many species. Trends Ecol Evol 31:860871.
Fain SJ, Quinones M, Alvarez-Berrios NL, Pares-Ramos IK, Gould WA (2018) Climate change and coffee:
assessing vulnerability by modeling future climate suitability in the Caribbean island of Puerto Rico. Clim
Chang 146:175186.
Fernandes ALT, Tavares TO, Santinato F, Ferreira RT, Santinato R (2016) Technical and economic viability of
drip irrigation of coffee in Araxá, MG. Coffee Science 11:347358
Field CB et al (2014) Technical summary. In: Field CB, Barros VR, Dokken DJ, Mach KJ, Mastrandrea MD,
Bilir TE, Chatterjee M, Ebi KL, Estrada YO, Genova RC, Girma B, Kissel ES, Levy AN, MacCracken S,
Mastrandrea PR, White LL (eds) Climate change2014: impacts, adaptation, and vulnerability. Part A: global
and sectoral aspects. Contribution of working group II to the fifth assessment report of the intergovernmental
panel on climate change. Cambridge university press, Cambridge, United Kingdom and New York, NY,
USA, pp 3594
Fitzpatrick MC, Hargrove WW (2009) The projection of species distribution models and the problem of non-
analog climate. Biodivers Conserv 18:2255.
Fitzpatrick MC, Gotelli NJ, Ellison AM (2013) MaxEnt versus MaxLike: empirical comparisons with ant species
distributions. Ecosphere 4:art55.
Franklin J (2010) Mapping species distributions: spatial inference and prediction. Ecology, biodiversity and
conservation. Cambridge University Press, Cambridge.
Fridell M, Hudson I, Hudson M (2008) With friends like these: the corporate response to fair trade coffee. Rev
Radical Polit Econ 40:834.
Gaveau DLA, Linkie M, Suyadi LP, Leader-Williams N (2009) Three decades of deforestation in Southwest
Sumatra: effects of coffee prices, law enforcement and rural poverty. Biol Conserv 142:597605. https://doi.
Gay C, Estrada F, Conde C, Eakin H, Villers L (2006) Potential impacts of climate change on agriculture: a case
of study of coffee production in Veracruz, Mexico. Clim Chang 79:259288.
Ghini R, Hamada E, Pedro MJ, Marengo JA, Goncalves RRD (2008) Risk analysis of climate change on coffee
nematodes and leaf miner in Brazil. Pesq Agrop Brasileira 43:187194.
Ghini R, Hamada E, Pedro MJJr, Gonçalves RRV (2011) Incubation period of Hemileia vastatrix in coffee plants
in Brazil simulated under climate change. Summa Phytopathol 37:8593.
Ghini R et al (2015) Coffee growth, pest and yield responses to free-air CO2 enrichment. Clim Chang 132:307
Guido Z, Finan T, Rhiney K, Madajewicz M, Rountree V, Johnson E, McCook G (2018) The stresses and
dynamics of smallholder coffee systems in Jamaicas Blue Mountains: a case for the potential role of climate
services. Clim Chang 147:253266.
Climatic Change (2019) 156:609630 627
Hannah L et al (2017) Regional modeling of climate change impacts on smallholder agriculture and ecosystems
in Central America. Clim Chang 141:2945.
Harvey CA, Saborio-Rodríguez M, Martinez-Rodríguez MR, Viguera B, Chain-Guadarrama A, Vignola R,
Alpizar F (2018) Climate change impacts and adaptation among smallholder farmers in Central America.
Agric Food Secur 7.
Holland MB et al (2017) Mapping adaptive capacity and smallholder agriculture: applying expert knowledge at
the landscape scale. Clim Chang 141:139153.
ICO (2014) World coffee trade (19632013): A review of the markets, challenges and opportunities
facing the sector. Int Coffee Organ
pdf. Accessed 05/07/2019
ICO (2019a) Annual Review 2017/18. International Coffee Organization. http://www.ico.
org/documents/cy2018-19/annual-review-2017-18-e.pdf. Accessed 05/07/2019
ICO (2019b) International Coffee Organization Statistics. Int Coffee Organ.
asp. Accessed 05/07/2019
Imbach P et al (2017) Coupling of pollination services and coffee suitability under climate change. Proc Natl
Acad Sci U S A 114:1043810442.
IPCC (2014) Climate change 2014: synthesis report. Contribution of working groups I, II and III to the fifth
assessment report of the intergovernmental panel on climate change. Intergovernmental panel on climate
change (IPCC), Geneva
Jaramillo J, Muchugu E, Vega FE, Davis A, Borgemeister C, Chabi-Olaye A (2011) Some like it hot: the
influence and implications of climate change on coffee berry borer (Hypothenemus hampei) and coffee
production in East Africa. PLoS ONE 6.
Jaramillo J et al (2013) Climate change or urbanization? Impacts on a traditional coffee production system in East
Africa over the last 80 years. PLoS ONE 8.
Jayakumar M, Rajavel M, Surendran U, Gopinath G, Ramamoorthy K (2017) Impact of climate variability on
coffee yield in Indiawith a micro-level case study using long-term coffee yield data of humid tropical
Kerala. Clim Chang 145:335349.
Jezeer RE, Santos MJ, Boot RGA, Junginger M, Verweij PA (2018) Effects of shade and input management on
economic performance of small-scale Peruvian coffee systems. Agric Syst 162:179190. https://doi.
Jha S, Bacon CM, Philpott SM, Mendez VE, Laderach P, Rice RA (2014) Shade coffee: update on a disappearing
refuge for biodiversity. Bioscience 64:416428.
Jonsson M, Raphael IA, Ekbom B, Kyamanywa S, Karungi J (2015) Contrasting effects of shade level and
altitude on two important coffee pests. J Pest Sci 88:281287.
Junior JZ, Pinto HS, Assad ED (2006) Impact assessment study of climate change on agricultural zoning.
Meteorol Appl 13:6980.
Kang Y, Khan S, Ma X (2009) Climate change impacts oncrop yield, crop water productivity and food security
a review. Prog Nat Sci 19:16651674.
Kearney MR, Wintle BA, Porter WP (2010) Correlative and mechanistic models of species distribution provide
congruent forecasts under climate change. Conserv Lett 3:203213.
Knox J, Daccache A, Hess T, Haro D (2016) Meta-analysis of climate impacts and uncertainty on crop yields in
Europe. Environ Res Lett 11:113004.
Kutywayo D, Chemura A, Kusena W, Chidoko P, Mahoya C (2013) The impact of climate change on the
potential distribution of agricultural pests: the case of the coffee white stem borer (Monochamus leuconotus
P.) in Zimbabwe. PLoS ONE 141.
Laderach P, Ramirez-Villegas J, Navarro-Racines C, Zelaya C, Martinez-Valle A, Jarvis A (2017)
Climate change adaptation of coffee production in space and time. Clim Chang 141:4762.
Liebig T et al (2016) Towards a collaborative research: a case study on linking science to Farmersperceptions
and knowledge on Arabica coffee pests and diseases and its management. PLoS ONE 11:23. https://doi.
Lin BB (2007) Agroforestry management as an adaptive strategy against potential microclimate extremes in
coffee agriculture. Agric For Meteorol 144:8594.
Luedeling E, Kindt R, Huth NI, Koenig K (2014) Agroforestry systems in a changing climatechallenges in
projecting future performance. Curr Opin Environ Sustain 6:17.
Machovina B, Feeley KJ (2013) Climate change driven shifts in the extent and location of areas suitable for
export banana production. Ecol Econ 95:8395.
Climatic Change (2019) 156:609630
Magrach A, Ghazoul J (2015) Climate and pest-driven geographic shifts in global coffeeproduction: implications
for forest cover, biodiversity and carbon storage. PLoS ONE 10:e0133071.
Mateo RG, Croat TB, Felicísimo ÁM, Muñoz J (2010) Profile or group discriminative techniques? Generating
reliable species distribution models using pseudo-absences and target-group absences from natural history
collections. Divers Distrib 16:8494.
Mendelsohn R (2008) The impact of climate change on agriculture in developing countries.J Nat Resour Pol Res
Merow C, Smith MJ, Silander JA Jr (2013) A practical guide to MaxEnt for modeling speciesdistributions:
what it does, and why inputs and settings matter. Ecography 36:10581069.
Meyfroidt P, Vu TP, Hoang VA (2013) Trajectories of deforestation, coffee expansion and displacement of
shifting cultivation in the central highlands of Vietnam. Glob Environ Chang 23:11871198. https://doi.
Meylan L, Gary C, Allinne C, Ortiz J, Jackson L, Rapidel B (2017) Evaluating the effect of shade trees on
provision of ecosystem services in intensively managed coffee plantations. Agric Ecosyst Environ 245:32
Moat J et al (2017) Resilience potential of the Ethiopian coffee sector under climate change. Nat Plants 3.
Moher D, Liberati A, Tetzlaff J, Altman DG, The PRISMA Group (2009) Preferred reporting items for
systematic reviews and meta-analyses: the PRISMA statement. PLoS Med 6:e1000097. https://doi.
Moreira SLS, Pires CV, Marcatti GE, Santos RHS, Imbuzeiro HMA, Fernandes RBA (2018) Intercropping of
coffee with the palm tree, macauba, can mitigate climate change effects. Agric For Meteorol 256-257:379
Nesper M, Kueffer C, Krishnan S, Kushalappa CG, Ghazoul J (2017) Shade tree diversity enhances coffee
production and quality in agroforestry systems in the Western Ghats. Agric Ecosyst Environ 247:172181.
Nicotra AB et al (2010) Plant phenotypic plasticity in a changing climate. Trends Plant Sci 15:684692.
Ovalle-Rivera O, Läderach P, Bunn C, Obersteiner M, Schroth G (2015) Plant phenotypic plasticity in a changing
climate. PLoS ONE 10.
Pezzopane JRM, de Souza PS, de Souza Rolim G, Gallo PB (2011) Microclimate in coffee plantation grown
under grevillea trees shading. Acta Sci Agron 33:201206.
Philpott SM, Lin BB, Jha S, Brines SJ (2008) A multi-scale assessment of hurricane impacts on agricultural
landscapes based on land use and topographic features. Agric Ecosyst Environ 128:1220. https://doi.
Pickering C, Byrne J (2014) The benefits of publishing systematic quantitative literature reviews for PhD
candidates and other early-career researchers. High Educ Res Dev 33:534548.
Pickering C, Grignon J, Steven R, Guitart D, Byrne J (2015) Publishing not perishing: how research students
transition from novice to knowledgeable using systematic quantitative literature reviews. Stud High Educ 40:
Rahn E et al (2014) Climate change adaptation, mitigation and livelihood benefits in coffee production: where are
the synergies? Mitig Adapt Strateg Glob Chang 19:11191137.
Rahn E, Vaast P, Laderach P, van Asten P, Jassogne L, GhazoulJ (2018) Exploringadaptation strategies of coffee
production to climate change using a process-based model. Ecol Model371:7689.
Ramirez-Villegas J, Challinor A (2012) Assessing relevant climate data for agricultural applications. Agric For
Meteorol 161:2645.
Ranjitkar S et al (2016) Suitabilityanalysis and projected climate change impact on banana and coffee production
zones in nepal. PLoS ONE 11.
Rodrigues WP et al (2016) Long-term elevated air [CO2] strengthens photosynthetic functioning and mitigates
the impact of supra-optimal temperatures in tropical Coffea arabica and C. canephora species. Glob Chang
Biol 22:415431.
Roubik DW (2002) The value of bees to the coffee harvest. Nature 417:708.
Schroth G et al (2009) Towards a climate change adaptation strategy for coffee communities and ecosystems in
the Sierra Madre de Chiapas, Mexico. Mitig Adapt Strateg Glob Chang 14:605625.
Climatic Change (2019) 156:609630 629
Schroth G, Läderach P, Blackburn Cuero DS, Neilson J, Bunn C (2015) Winner or loser of climate change? A
modeling study of current and future climatic suitability of Arabica coffee in Indonesia. Reg Environ Chang
Tavares PD, Giarolla A, Chou SC, Silva AJD, Lyra AD (2018) Climate change impact on the potential yield of
Arabica coffee in Southeast Brazil. Reg Environ Chang 18:873883.
TCI (2016) A brewing storm: the climate change risks to coffee. The Climate Institute. http://www. Accessed 05/07/2019
Tesfaye SG, Ismail MR, Kausar H, Marziah M, Ramlan MF (2013) Plant water relations, crop yield and quality
of Arabica coffee (Coffea arabica) as affected by supplemental deficit irrigation. Int J Agric Biol 15:665672
Thuiller W, Lavorel S, Araújo MB, Sykes MT, Prentice IC (2005) Climate change threats to plant diversity in
Europe. Proc Natl Acad Sci U S A 102:82458250.
Trumble JT, Butler CD (2009) Climate change will exacerbate California's insect pest problems. Calif Agric 63:
Vaast P, Bertrand B, Perriot J-J, Guyot B, Génard M (2006) Fruit thinning and shade improve bean characteristics
and beverage quality of coffee (Coffea arabica L.) under optimal conditions. J Sci Food Agric 86:197204.
van Asten PJA, Wairegi LWI, Mukasa D, Uringi NO (2011) Agronomic and economic benefits of coffeebanana
intercropping in Ugandas smallholder farming systems. Agric Syst 104:326334.
van Oijen M, Dauzat J, Harmand JM, Lawson G, Vaast P (2010) Coffee agroforestry systems in CentralAmerica:
II. Development of a simple process-based model and preliminary results. Agrofor Syst 80:361378.
van Rikxoort H, Schroth G, Laderach P, Rodriguez-Sanchez B (2014) Carbon footprints and carbon stocks reveal
climate-friendly coffee production. Agron Sustain Dev 34:887897.
Verhage FYF, Anten NPR, Sentelhas PC (2017) Carbon dioxide fertilization offsets negative impacts of climate
change on Arabica coffee yield in Brazil. Clim Chang 144:671685.
White JW, Hoogenboom G, Kimball BA, Wall GW (2011) Methodologies for simulating impacts of climate
change on crop production. Field Crop Res 124:357368.
Publishers note Springer Nature remains neutral with regard to jurisdictional claims in published maps and
institutional affiliations.
Yen Pham
&Kathryn Reardon-Smith
&Shahbaz Mushtaq
&Geoff Cockfield
School of Agricultural, Computational and Environmental Sciences, University of Southern Queensland,
Toowoomba, Australia
Centre for Applied Climate Sciences, University of Southern Queensland, Toowoomba, Australia
Centre for Sustainable Agricultural Systems, University of Southern Queensland, Toowoomba, Australia
Climatic Change (2019) 156:609630
... Coffee (Coffea sp.) is the second most globally traded commodity after petroleum [1] and accounts for broad market and high economic values. It is estimated that the worldwide production of coffee increased to 9.9 million tons in 2019 [2]. ...
Full-text available
Coffee brewing is a complex process from roasted coffee bean to beverage, playing an important role in coffee flavor quality. In this study, the effects of hot and cold brewing on the flavor profile of coffee were comprehensively investigated on the basis of chromatographic and sensory approaches. By applying gas chromatography–mass spectrometry and odor activity value calculation, most pyrazines showed higher contribution to the aroma profile of cold brew coffee over hot brew coffee. Using liquid chromatography, 18 differential non-volatiles were identified, most of which possessed lower levels in cold brew coffee than hot brew coffee. The sensory evaluation found higher fruitiness and lower bitterness and astringent notes in cold brew coffee than hot brew coffee, which was attributed by linalool, furfural acetate, and quercetin-3-O-(6″-O-p-coumaroyl) galactoside. This work suggested coffee brewing significantly affected its flavor profile and sensory properties.
... Numerosos estudios(Läderach et al., 2011; Bunn et al., 2014;Pham et al., 2019;Jawo et al., 2022) han demostrado que el cambio climático afectará la aptitud climática del cultivo de café.El aumento de la temperatura y los cambios en los patrones de precipitación disminuirán el rendimiento, reducirán la calidad y aumentarán la incidencia de plagas y enfermedades.El deterioro en las condiciones de su producción puede inducir el abandono de esta actividad y propiciar cambios en el uso del suelo, lo que resultaría en pérdida de los servicios ecosistémicos que los cafetales proveen. Por ello, es necesaria la implementación de medidas de adaptación que permitan reducir los impactos negativos del cambio climático sobre el cultivo del café e incrementar su resiliencia, así como el fortalecimiento de las capacidades de los cafeticultores para enfrentar los efectos adversos del cambio climático(Jawo et al., 2022).En este contexto, este documento presenta un conjunto de prácticas que pueden contribuir a la adaptación al cambio climático en el cultivo de café. ...
Full-text available
En el marco del proyecto CityAdapt “Construcción de resiliencia climática en sistemas urbanos mediante la adaptación basada en ecosistemas en América Latina y el Caribe” se realizó el estudio de vulnerabilidad ante el cambio climático en los municipios de Xalapa y Tlalnelhuayocan. Los resultados de este estudio destacan la importancia de los cafetales bajo sombra, ya que estos sistemas coinciden con los remanentes de Bosque Mesófilo de Montaña (BMM) y proveen servicios ecosistémicos que contribuyen al bienestar de la sociedad. Los cultivos de café fueron introducidos en el año de 1808 en la región central montañosa del estado de Veracruz (Williams-Linera, 2007). En un inicio, la expansión de este cultivo provocó la degradación del BMM (Williams-Linera, 2002), sin embargo, posteriormente contribuyó a la conservación de fragmentos de este ecosistema al evitar que fuera transformado a otros usos de suelo como potreros y cañaverales (Williams-Linera, 2007). El cultivo de café bajo sombra conserva una elevada diversidad de árboles y arbustos nativos del BMM o introducidos. La presencia de árboles y arbustos de distintos tamaños crea estratos en el dosel que son el hábitat de un elevado número de especies (Sosa-Fernández et al., 2017). Esto contribuye a que los cafetales bajo sombra sean uno de los sistemas agroforestales con una mayor diversidad de especies de flora y fauna pertenecientes a distintos grupos (Pineda et al., 2005). Tan solo en los sistemas cafetaleros de la región central montañosa de Veracruz, se han identificado 2197 especies (Manson et al., 2008).
... In reality, the severity of infection within farms and within plants is more of a gradient and may influence the rate of spread by influencing the number of spores that are released from an infected plant (Kushalappa and Eskes 1989). We also do not include environmental variability, such as seasonality, extreme events, or long-term changes including climate change (Pham et al. 2019;White and Hastings 2020). Despite these limitations, our model successfully outlines broad patterns of rust spread and the landscape factors influencing outbreaks, setting the stage for future work. ...
Full-text available
Context Landscape structure influences the spread of plant pathogens, including coffee leaf rust, a fungal disease affecting the coffee industry. Rust transmission is likely affected by landscape structure through the dispersal of wind-borne spores. Previous studies found positive associations between rust incidence and the proportion of pasture cover, suggesting deforestation may facilitate spore dispersal. Objectives We explored the links between landscape structure and coffee rust by modeling the spread of rust transmission. We investigated how (1) spatial clustering of coffee farms, (2) proportion of landscape deforestation, and (3) clustering of deforestation affects the speed of rust transmission. Methods We developed a probabilistic model to simulate within-patch and between-patch transmission in simulated and real landscapes. We modeled within-patch transmission using a probabilistic cellular automata model and between-patch transmission using a random walk with spore movement inhibited by canopy cover. Results Clustering of coffee farms is the primary driver of rust transmission. Deforestation is a secondary driver of rust spread: outbreaks spread more rapidly in landscapes where deforested areas are evenly dispersed throughout the landscape. When applied to real landscapes in Costa Rica, the model yields the same trends as simulated landscapes and suggests increased amounts of coffee near the starting location of the outbreak are correlated with more rapid rust spread. Conclusions It is essential to consider landscape structure when managing the spread of crop diseases. Increasing the spacing between coffee farms and reducing forest fragmentation in coffee-growing regions can benefit biodiversity conservation and reduce the economic impacts of coffee rust.
... Coffee is a climate-sensitive perennial plant likely to be highly influenced by changes in climate. Increasing climate variability may lead to coffee yield decrease and coffee area damage and threaten coffee production in producing areas worldwide [5,6]. province of Vietnam [21]. ...
Full-text available
Given the present climate change context, accurate and timely coffee yield prediction is critical to all farmers who work in the coffee industry worldwide. The aim of this study is to develop and assess a coffee yield forecasting method at the regional scale in Dak Lak province in the central highlands of Vietnam using the Crop Growth Monitoring System Statistical Tool (CGMSstatTool— CST) software and vegetation biophysical variables (NDVI, LAI, and FAPAR) derived from satellite remote sensing (SPOT-VEGETATION and PROBA-V). There has been no research to date applying this approach to this specific crop, which is the main contribution of this study. The findings of this research reveal that the elaboration of multiple linear regression models based on a combination of information from satellite-derived vegetation biophysical variables (LAI, NDVI, and FAPAR) corresponding to the first six months of the years 2000–2019 resulted in coffee yield forecast models presenting satisfactory accuracy (Adj.R2 = 64 to 69%, RMSEp = 0.155 to 0.158 ton/ha and MAPE = 3.9 to 4.7%). These results demonstrate that the CST may efficiently predict coffee yields on a regional scale by using only satellite-derived vegetation biophysical variables. This study findings are likely to aid local governments and decision makers in precisely forecasting coffee production early and promptly, as well as in recommending relevant local agricultural policies.
... As coconut and arecanut are grown across different agro-ecological zones of India, evaluating the impacts of climate change scenarios on the potential cultivable area will be helpful in understanding the relationships between crops niches and the corresponding environment, identifying priority cultivation areas, planning adaptation strategies (Davies et al., 2009;Xu et al., 2018). Species distribution models like Maxent are extensively used to predict the change in climate of some of the plantation growing areas like cocoa in African countries (Läderach et al., 2013;Schroth et al., 2016), coffee at Zimbabwe (Pham et al., 2019) coconut in India and other agricultural crops (Kogo et al., 2019, He andZhou, 2016;Jayasinghe and Kumar, 2019). In this study the MaxEnt model prediction for coconut having the mean AUC values of coconut 0.899+0.002 ...
... Since 2010, annual global production of robusta and arabica has increased by 3% and 2%, respectively [1]. Nevertheless, coffee production is threatened by several factors including: (a) climate change which may result in reduction of coffee yield and area climatically suitable for coffee by, e. g., favoring pests and diseases outbreaks [4][5][6][7][8][9]; and, (b) biodiversity loss which affects coffee agroecosystem resilience by reducing, e.g., biocontrol interactions [10][11][12]. ...
Full-text available
Little is known on what impact shade trees have on the physiology of Coffea canephora (robusta coffee) under tropical humid conditions. To fill this gap, a field experiment was conducted in the Ecuadorian Amazon to investigate how growth, nutrition (leaf N), phenological state (BBCH-scale) and yield of 5-year-old robusta coffee shrubs are affected by the presence or absence of leguminous trees, the type (organic v conventional) and intensity of management. The experiment was a factorial 5 × 4 design with four cropping systems: intensive conventional (IC), moderate conventional (MC), intensive organic (IO) and low organic (LO), and with five shading systems in a split-plot arrangement: full sun (SUN), both Erythrina spp. and Myroxylon balsamum (TaE), M. balsamum (TIM), E. spp. (ERY) and Inga edulis (GUA). Three monthly assessments were made. Cherry yields of coffee shrubs under moderate shade (c. 25%) were similar to those under high light exposure. Coffee shrubs grown with either E. spp. or I. edulis were taller (+10%) and had higher leaf N concentrations (22%) than those grown without consistent shade. Unless receiving c. 25% of shade, coffee shrubs grown under organic cropping systems showed reduced growth (25%). No correlation was found between height, cherry yield and leaf N. Both shading and cropping systems affected leaf N concentration, also depending on phenological state and yield. Further research is needed to confirm our findings in the long-term as well as to elucidate how leguminous trees may induce physiological responses in robusta coffee under humid tropical conditions.
Full-text available
Food wastes are generated in all stages of the life cycle of a given food product. They could be produced during the crops production phase; the food industrial manufacturing and processing; their storage and distribution phases, and their consumption. Fruit and vegetable losses are relatively high in developing nations during the agricultural stage, but they are mostly justified by the processing phase, which represents about 25% of losses (Gustavsson, 2011).Food waste is also generated in household activities, food manufacturing includes 39% loss in the food industry and 14% loss in the service sector (ready to eat, catering and hotels), while during distribution, 5% is lost. The amount of produced food wastes depends mainly on the country’s life quality and economic growth. For example, this quantity was estimated to be 35 to 103 million tons in the United States of America (USA) (Gustavsson, 2011) while it was “only” 102 thousand tons in Tunisia (Chaher et al., 2020). This chapter is intended to give an outline of the biochemical active substance present in the waste of palm trees and their properties, utilization, and some other aspect of palm fruit waste such as palm kernel, oil palm, and date palm waste which is produced during the life cycle of palm tree. This study focused on making a product from Palm residues as well as developing an alternative product that will reduce social and environmental challenges.
Phytophthora palmivora is a destructive plant pathogenic oomycete that has caused lethal diseases in a wide range of hosts. It is a pan-tropical distributed pathogen that can infect plants at all growth stages. Extensive studies have linked P. palmivora to severe diseases in several crops, such as black pepper, rubber, cocoa, and durian, causing global economic losses. This review covers the following topics in depth: (i) P. palmivora as phytopathogen; (ii) identification and infection mechanism in rubber, cocoa, and durian; and (iii) management and control applied for P. palmivora diseases. Effective management strategies were studied and practiced to prevent the spread of P. palmivora disease. Genetic resistance and biocontrol are the best methods to control the disease. A better understanding of P. palmivora infection mechanisms in our main crops and early disease detection can reduce the risk of catastrophic pandemics.
The production of coffee in Ecuador a family activity carried out in rural areas. Due to the economic importance of this crop and its ability to adapt to different ecosystems, it has been widely introduced in government conservation and economic reactivation programs. At the present, it is cultivated in the four Ecuadorian natural regions that comprise the Amazon rainforest, the Andean mountains, the Pacific coast, and the Galapagos Islands. The different climate and altitude characteristics of these regions allow Ecuador to grow all commercial varieties of coffee. The variety planted, the region of origin, and the type of post-harvest processing gives each cup of coffee a unique flavor and aroma. To recovery the knowledge behind each production process, a complete review of the whole coffee productive chain was made. The information reviewed was compared with the available information of other neighboring countries and complemented with experiences described by small farmers. The analysis confirms that Ecuador has a competitive advantage due to its ecosystem diversity. However, the development of this industry depends on the correct implementation of policies that cover three main aspects: (1) farmers’ quality of life, (2) training and research programs, and (3) fair trade for small producers.
Full-text available
Sustainable coffee production is significantly threatened by climate change. While implementing CSA practices offers numerous benefits, adoption rates remain low. Coffee plantations are dominated by smallholders and located in rural areas, making them more complex and requiring a comprehensive analysis and intervention. This study used an exploratory approach to assess farmers’ preferences for CSA practices, identify barriers to implement, and design a support system model. The investigation focused on Arabica and Robusta farmers, with case studies from two Indonesian production centres. Preferences assessment used conjoint analysis, barriers evaluation used Mann–Whitney analysis, model development used synthetic approaches, and priority analysis used the Analytical Hierarchy Process. The study revealed that diversification is more desirable than cultivation, soil management, and water management. Arabica farmers preferred intercropping with annual crops, whereas Robusta farmers preferred perennials crops. Robusta farmers assessed that agricultural inputs, such as labor, capital, climatic data, and farm equipment and machinery, existed as barriers. However, these represent a lesser issue for Arabica farmers. We proposed agricultural innovation support system, consisting of innovation support facilities and services, as a comprehensive support system model to accelerate CSA implementation. Further analysis showed that the priority strategy for Arabica farmers is support services that focus on network development, while for Robusta farmers is support facilities that focus on climate information system development.
Full-text available
Background Smallholder farmers are one of the most vulnerable groups to climate change, yet efforts to support farmer adaptation are hindered by the lack of information on how they are experiencing and responding to climate change. More information is needed on how different types of smallholder farmers vary in their perceptions and responses to climate change, and how to tailor adaptation programs to different smallholder farmer contexts. We surveyed 860 smallholder coffee and basic grain (maize/bean) farmers across six Central American landscapes to understand farmer perceptions of climate change and the impacts they are experiencing, how they are changing their agricultural systems in response to climate change, and their adaptation needs. Results Almost all (95%) of the surveyed smallholder farmers have observed climate change, and most are already experiencing impacts of rising temperatures, unpredictable rainfall and extreme weather events on crop yields, pest and disease incidence, income generation and, in some cases, food security. For example, 87% of maize farmers and 66% of coffee farmers reported negative impacts of climate change on crop production, and 32% of all smallholder farmers reported food insecurity following extreme weather events. Of the farmers perceiving changes in climate, 46% indicated that they had changed their farming practices in response to climate change, with the most common adaptation measure being the planting of trees. There was significant heterogeneity among farmers in the severity of climate change impacts, their responses to these impacts, and their adaptation needs. This heterogeneity likely reflects the wide diversity of socioeconomic and biophysical contexts across smallholder farms and landscapes. Conclusions Our study demonstrates that climate change is already having significant adverse impacts on smallholder coffee and basic grain farmers across the Central American region. There is an urgent need for governments, donors and practitioners to ramp up efforts to help smallholder farmers cope with existing climate impacts and build resiliency to future changes. Our results also highlight the importance of tailoring of climate adaptation policies and programs to the diverse socioeconomic conditions, biophysical contexts, and climatic stresses that smallholder farmers face
Full-text available
Tropical agroforestry systems provide a number of ecosystem services that might help sustain the production of multiple crops, improve farmers' livelihoods and conserve biodiversity. A major drawback of agroforestry coffee systems is the perceived lower economic performance compared to high-input monoculture coffee systems, which is driving worldwide intensification practices of coffee systems. However, comprehensive cost-benefit analyses of small-scale coffee plantations are scarce. Consequently, there is a need to improve our understanding of the economic performance of coffee systems under different shade and input management practices. We provide a comprehensive economic analysis of Arabica coffee farming practices where we compare productivity, costs, net income and benefit-cost ratio (BCR) of 162 small-scale, Peruvian coffee plantations under different shade and input management practices along an elevation gradient. By using a cluster analysis, three shade and three input classes (low, medium and high) were defined. We found similar economic performance for all shade classes, but reduced net income and BCR in the High-Input class. More specifically, there was no difference in net income or BCR between low, medium and high shade classes. The High-Input class had significantly lower net income and BCR, mainly due to increased costs of (hired) labour, land, and fertilizer and fungicides; costs which were not fully compensated for by higher coffee yields. Coffee yield decreased with elevation, whereas gate coffee price and quality, as well as shade levels, increased with elevation. Additional revenues from timber could increase farmers' income and overall economic performance of shaded plantations in the future. Our analysis provides evidence that for small-scale coffee production, agroforestry systems perform equally well or better than unshaded plantations with high input levels, reinforcing the theory that good economic performance can coincide with conservation of biodiversity and associated ecosystem services. Additional comprehensive and transparent economic analyses for other geographic regions are needed to be able to draw generalizable conclusions for smallholder coffee farming worldwide. We advise that future economic performance studies simultaneously address the effects of shade and input management on economic performance indicators and take biophysical variation into account.
Full-text available
L’agroforesterie est considérée comme l’une des stratégies agricoles pouvant contribuer à l’adaptation des cultures au changement climatique. La présente étude de cas visait à comparer les conditions microclimatiques d’une parcelle de Coffea arabica cultivée sous ombrage, principalement Inga spp., et celles d’une parcelle de C. arabica menée en monoculture sans ombrage dans la même plantation de café, dans la région de Pasco au Pérou. La température et l’humidité de l’air, la température du sol et la disponibilité en eau du sol ont été mesurées pendant trois ans. Les résultats indiquent que l’ombrage des arbres réduit la température moyenne de l’air de 0,4 ± 0,04 °C et la température du sol de 1,7 ± 0,3 °C, et augmente l’humidité de l’air de 3,9 ± 0,4 % par rapport à la zone sans ombrage. Cependant, la moyenne mensuelle des températures de l’air dans la zone non ombragée, et même la température maximale, ne dépassent pas outre mesure la limite permettant la photosynthèse (seuil 34 °C). De plus, les températures minimales mensuelles diffèrent peu entre les zones ombragées et non ombragées, alors que la fluctuation des températures du sol est plus marquée dans la zone non ombragée. Un des principaux constats de cette étude concerne la sécheresse plus marquée des sols dans la zone ombragée, surtout au début et à la fin de la saison sèche. Ceci s’explique probablement par l’augmentation de la transpiration totale par celle des arbres d’ombrage. L’absorption d’eau plus importante en agroforesterie pourrait ainsi avoir un impact négatif sur la croissance des caféiers dans les situations où la disponibilité en eau est un facteur limitatif.
Full-text available
Access to climate information has the potential to build adaptive capacity, improve agricultural profitability, and help manage risks. To achieve these benefits, knowledge of the local context is needed to inform information development, delivery, and use. We examine coffee farming in the Jamaican Blue Mountains (BM) to understand farmer livelihoods, opportunities for climate knowledge to benefit coffee production, and the factors that impinge on farmers’ ability to use climate information. Our analysis draws on interviews and 12 focus groups involving 143 participants who largely cultivate small plots. BM farmers currently experience stresses related to climate, coffee leaf rust, and production costs that interrelate concurrently and with time lags. Under conditions that reduce income, BM farmers compensate by adjusting their use of inputs, which can increase their susceptibility to future climate and disease stresses. However, farmers can also decrease impacts of future stressors by more efficiently and effectively allocating their limited resources. In this sense, managing climate, like the other stresses, is an ongoing process. While we identify climate products that can help farmers manage climate risk, the local context presents barriers that argue for interactive climate services that go beyond conventional approaches of information production and delivery. We discuss how dialogs between farmers, extension personnel, and climate scientists can create a foundation from which use can emerge.
Full-text available
The Intergovernmental Panel on Climate Change (IPCC) projections of global mean temperature rises are worrisome for coffee crop due to the intolerance of the Arabica species to high air temperature variations. The crop has a large participation in the Brazilian trade balance; therefore, in this study, the impacts of climate change on the potential yield of Arabica coffee (Coffea arabica L.) were assessed in the areas of Southeast Brazil in future climate change scenarios. Simulations of the Eta Regional Climate Model at 5-km resolution used in this study were generated from a second dynamic downscaling of the HadGEM2-ES model runs. The projections adopted two scenarios of greenhouse gas concentration, the RCP4.5 and RCP8.5, and considered the period 2011–2100. The projections indicated a large reduction of about 20 to 60% of the areas currently suitable for coffee cultivation in Southeast Brazil. In the RCP8.5 scenario, at the end of century, coffee cultivation is suitable only in elevated mountain areas, which would pose difficulties to farming management due to the operation of agricultural machinery in mountain areas. In addition, coffee cultivation in these regions could produce environmental impacts in the remnant Brazilian Atlantic Forest. Areas of high climatic risk increase due to temperature increase. The projections showed that the potential yield could be reduced by about 25% by the end of the twenty-first century. These results of potential coffee yield in the future climate indicate a need for adaptation studies of Arabica coffee cultivation.
Full-text available
Study on variability in area, production, and productivity of coffee in India during last decade indicates that area and production of coffee is increasing whereas yield of coffee is decreasing trend during the period 1990–1991 to 2015–2016. There was increasing trend of Robusta coffee and decreasing trend of Arabica coffee yields in India with three distinct periods due to climate change. Micro-level study was conducted on variability in yield of Arabica and Robusta coffee vis-à-vis climate change, variability of parameters like rainfall (RF), maximum temperature (Tmax), minimum temperature (Tmin), and mean relative humidity (RH) was undertaken with data recorded at Regional Coffee Research Station, Chundale, Wayanad, Kerala State. The yield data for 35 years (1980 to 2014) revealed that the yield of Robusta coffee was higher than that of Arabica coffee in most of the years due to favorable climate requirements in Kerala. There was increasing trend of yield of Robusta coffee in Kerala and decreasing trend of Arabica coffee. Blossom showers had significance influence in increasing the yield of coffee rather than total annual rainfall in Robusta coffee. El Niño events have little effect on coffee production in India in general, and out of 11 El Niño years, only 3 years coffee productivity was adversely affected. However, with respect to Kerala, Arabica yield was adversely affected in strong El Niño years, which was again confirmed with NDVI anomaly too.
Full-text available
Arabica coffee production provides a livelihood to millions of people worldwide. Climate change impact studies consistently project a drastic decrease of Arabica yields in current production regions by 2050. However, none of these studies incorporated the beneficial effects that elevated CO2 concentrations are found to have on Arabica coffee yields, the so-called CO2 fertilization effect. To assess the impacts of climate change and elevated CO2 concentrations on the cultivation of Arabica coffee in Brazil, a coffee yield simulation model was extended with a CO2 fertilization and irrigation factor. The model was calibrated and validated with yield data from 1989 to 2013 of 42 municipalities in Brazil and found to perform satisfactorily in both the calibration (R² = 0.91, d = 0.96, mean absolute percentage error (MAPE) = 8.58%) and validation phases (R² = 0.96, d = 0.95, MAPE = 11.16%). The model was run for the 42 municipalities from 1980 to 2010 with interpolated climate data and from 2040 to 2070 with climate data projected by five global circulation models according to the Representative Concentration Pathway 4.5 scenario. The model projects that yield losses due to high air temperatures and water deficit will increase, while losses due to frost will decrease. Nevertheless, extra losses are offset by the CO2 fertilization effect, resulting in a small net increase of the average Brazilian Arabica coffee yield of 0.8% to 1.48 t ha⁻¹ in 2040–2070, assuming growing locations and irrigation remain unchanged. Simulations further indicate that future yields can reach up to 1.81 t ha⁻¹ provided that irrigation use is expanded.
El Niño Southern Oscillation (ENSO) is a naturally occurring phenomenon that affects weather around the world. Past ENSO episodes have had severe impacts on the economy of Colombia. We study the influence of ENSO on Colombian coffee production, exports and price. Our structural econometric specification is consistent with an economic model of the market for Colombian coffee which, in the short–run, is characterized by a downward–sloping demand curve and by a vertical supply curve. We show that El Niño (i.e. positive shocks to ENSO) is beneficial for Colombian production and exports and decreases the real price of Colombian coffee. On the contrary, La Niña (i.e. negative shocks to ENSO) depresses Colombian coffee production and exports and increases price. However, the overall impact of ENSO shocks is small. Both in the short–run and in the long–run, shocks to international demand for Colombian coffee are more relevant than supply–side shocks in Colombia in explaining the dynamics of the price of Colombian coffee. Our results suggest that a given coffee price shock can have beneficial, detrimental or negligible effects on the Colombian economy, depending on its underlying cause. As a consequence, policy responses to coffee price shocks should be designed by looking at the causes of the shocks. This article is protected by copyright. All rights reserved
Global climate changes can affect coffee production in Brazil, and in other coffee producing countries. We examined the potential for an agroforestry system with the native species, macauba (Acrocomia aculeata), to mitigate impacts on coffee production by reducing maximal air temperature and photosynthetic active radiation. The objective of this study was to investigate the influence of an agroforestry system with macauba on productivity, microclimatic characteristics and soil physical quality on a coffee plantation in the Atlantic Rainforest biome, in Southern Brazil. We measured soil attributes (moisture, temperature, and physical properties), microclimate conditions (air temperature, photosynthetic active radiation) and coffee production parameters (productivity and yield). Macauba palm trees were planted at different planting densities on the rows and distances from the coffee rows. Planting density of macauba and their distance from the coffee rows affected soil thermal-water regime. Compared with the traditional unshaded sole coffee planting, the intercropped cultivation provided more coffee yield on both macauba density planting and distance evaluated. On the other hand, coffee productivity was increased by agroforestry systems just for 4.2 m distance between palm trees and coffee rows. Planting density of macaubas did not affect coffee yield and productivity. Best coffee harvest in agroforestry systems with macauba was related to higher soil moisture at the depth of 20–40 cm, higher photosynthetic active radiation, and maximum air temperatures lower than 30 °C. Agroforestry with coffee and macauba trees can be an adaptation strategy under future climatic variability and change related to high temperatures and low rainfall.
The response of coffee (Coffea arabica L.) agronomical performance to changes in climate and atmospheric carbon dioxide concentration ([CO2]) is uncertain. Improving our understanding of potential responses of the coffee plant to these changes while taking into consideration agricultural management is required for identifying best-bet adaptation strategies. A mechanistic crop modelling approach enables the inclusion of a wide range of prior knowledge and an evaluation of assumptions. We adapt a model by connecting it to spatially variable soil and climate data, by which we are able to calculate yield of rain-fed coffee on a daily time-step. The model takes account of variation in microclimate and water use as influenced by shade trees. The approach is exemplified at two East African sites with distinctly different climates (Mt. Elgon, Uganda, and Mt. Kilimanjaro, Tanzania) using a global sensitivity analysis for evaluation of model behavior and prior parameter uncertainty assessment. We use the climate scenario driven by the Hadley Global Environment Model 2-Earth System representative for the year 2050 to discuss potential responses of the coffee plant to interactions of elevated [CO2], temperature, and water availability. We subsequently explore the potential for adaptation to this scenario through shade management. The results indicate that under current climatic conditions optimal shade cover at low elevations (1000 m.a.s.l.) is 50%, provided soil water storage capacity is sufficient, enabling a 13.5% increase in coffee yield compared to unshaded systems. Coffee plants are expected to be severely impacted (ranging from 18% to 32% coffee yield reductions) at low elevations by increased temperature (+2.5 °C) and drought stress when no elevated [CO2] is assumed. Water competition between coffee and shade trees are projected to be a severe limitation in the future, requiring careful selection of appropriate shade tree species or the adoption of other technologies like conservation measures or irrigation. The [CO2]-fertilization effect could potentially mitigate the negative effect of temperature increase and drought stress up to 13–21% depending on site conditions and will increase yield at higher altitudes. High uncertainty remains regarding impacts of climate change on flowering. The presented model allows for estimating the optimal shade level along environmental gradients now and in the future. Overall, it shows that shade proves to be an important adaptation strategy, but this requires improved understanding regarding site-specific management and selection of tree species. Moreover, we do not yet include climate change uncertainty.