Risk perception and decision-making: do farmers
consider risks from climate change?
&Claudia R. Binder
&Markus A. Meyer
Received: 21 July 2017 /Accepted: 30 October 2018 /Published online: 8 November 2018
#The Author(s) 2018
Small-scale farmers are highly threatened by climate change. Experts often base their interven-
tions to support farmers to adapt to climate change on their own perception of farmers’livelihood
risks. However, if differences in risk perception between farmers and experts exist, these
interventions might fail. Thus, for effective design and implementation of adaptation strategies
for farmers, it is necessary to understand farmers’perception and how it influences their
decision-making. We analyze farmers’and experts’systemic view on climate change threats in
relation to other agricultural livelihood risks and assess the differences between their perceptions.
For Cauca, Colombia, we found that experts and farmers perceived climate-related and other
livelihood risks differently. While farmers’perceived risks were a failure in crop production and
lack of access to health and educational services, experts, in contrast, perceived insecurity and the
unreliable weather to be the highest risks for farmers. On barriers that prevent farmers from
taking action against risks, experts perceived both external factors such as the national policy and
internal factors such as the adaptive capacity of farmers to be the main barriers. Farmers ranked
the lack of information, especially about weather and climate, as their main barrier to adapt.
Effective policies aiming at climate change adaptation need to relate climate change risks to other
production risks as farmers often perceive climate change in the context of other risks.
Policymakers in climate change need to consider differences in risk perception.
Climate change poses major challenges to our society, especially in the agricultural sector in
developing countries (Vermeulen et al. 2011). Experts have argued that adaptation and
Climatic Change (2018) 151:507–524
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10584-018-
2320-1) contains supplementary material, which is available to authorized users.
Claudia R. Binder
Markus A. Meyer
mitigation actions are urgently needed to pave climate-resilient pathways for the future (IPCC
2014a). One major challenge with the design and implementation of adequate actions is the
complexity of the systems characterized by interactions between environmental and human
dynamics at different scales (Turner et al. 2003). Delayed and unexpected feedback loops,
nonlinearities, and abrupt rather than gradual changes render the climate system exceedingly
hard to predict and the reactions of the exposed human system even less foreseeable (Alley
et al. 2003). These entailed uncertainties make decision- and policymaking a difficult task.
The difficulties in climate-relevant decision- and policymaking in agriculture are further
aggravated by differing perceptions of climate change by experts and farmers. Despite the
scientific consensus about existence, risks, and possible solutions to climate change, nonspe-
cialists largely seem to underestimate and misinterpret these causes and risks (Ding et al.
2011). This is partly due to two key facts: first, most people do not differentiate between
weather and climate (Weber 2010) and are thereby unable to distinguish climate variability
from climate change (Finnis et al. 2015). Second, most people still perceive the likelihood that
climate change might affect them directly as low (Weber 2010; Barnes and Toma 2011;Lee
et al. 2015). When taking decisions towards adaptation, people tend to relate possible actions
to probable consequences in a linear manner without considering feedback loops, delays, and
nonlinearities (Weber 2006). The success of agricultural climate policies relies to a large extent
on farmers’awareness of climate change including their knowledge and beliefs regarding
climate change and how it will affect them (Patt and Schröter 2008; Carlton et al. 2016).
Scholars have found that small-scale farmers in Latin America are highly vulnerable to
climate change (Baca et al. 2014; Eitzinger et al. 2014). While farmers have adapted contin-
uously to social and environmental change in the past, the magnitude of climate change strikes
the already stressed rural population. In Latin America, inequality and economic vulnerability
call for an approach that tackles the underlying causes of vulnerability before implementing
adaptation strategies (Eakin and Lemos 2010). Without visualizing climate change as one of
the multiple exposures, small-scale farmers rarely adapt their farming practices even if
suggested by climate policies (Niles et al. 2015). This reluctance is greatly influenced by the
farmers’beliefs and perception concerning causes and local impacts of climate change (Haden
et al. 2012).
Furthermore, adaptive actions are driven by individuals and groups ideally supported by
institutions and governmental organizations. In many countries in Latin America, the influence
of governments has become weaker due to economic liberalization. Thus, governance mech-
anisms have lost their capacity to manage risks and to address issues of social vulnerability,
especially in rural areas (Eakin and Lemos 2006).
“By 2050, climate change in Colombia will likely impact 3.5 million people”(Ramirez-
Villegas et al. 2012, p. 1), and scenarios of impacts from long-term climate change will likely
threaten socioeconomics of Colombian agriculture. In Colombia’s southwestern department
Cauca, the average increase in annual temperature to the 2050s is estimated to be 2.1 °C with a
minor increase in precipitation (Ramirez-Villegas et al. 2012). In this region, coffee farmers
face several challenges through climate change, like shifting suitable areas into higher
altitudes, implying reduced yields and increasing pest and disease pressure (Ovalle-Rivera
et al. 2015). Ovalle-Rivera et al. (2015) estimate a national average of 16% decrease of climate
suitability for coffee in Colombia by 2050, mostly for areas below 1800 m a.s.l.
During the twentieth century, Colombia’s agrarian reform was the best example of failed
top-down approaches to promote self-reliant grassroots organizations in agriculture (Gutiérrez
2014), which might be more likely to adapt to climate change. Vulnerabilities in Colombia are
508 Climatic Change (2018) 151:507–524
structural and need to be addressed through transformative adaptation (Feola 2013). First, rural
populations in Colombia, and especially resource-limited farmers, depend on natural resources
and are particularly sensitive to environmental stress. Second, the level of human security is
low and tied to deeply rooted socioeconomic and political inequality. Third, the institutional
setting is a mix of formal and informal institutions that facilitate or impede building adaptive
capacity of farmers (Eakin and Lemos 2010;Feola2013).
For the successful adaptation of Colombian agriculture to agricultural risks from
climate, the government should set up enabling policies and release funds for research
and development to subsectors (Ramirez-Villegas and Khoury 2013). Adaptation options
should be developed based on underlying vulnerability analysis and participatory processes
with farmers and experts (Feola 2013). The interaction between grassroots organizations
(bottom-up) and institutions (top-down) is crucial for transformative adaptation (Bizikova
et al. 2012).
The development of adaptation options is hampered by the fact that experts often
have an incomplete view of farmers’perceptions which might have vast implications
for effective risk communication, e.g., regarding climate change, and during the
participatory design process of adaptation strategies (Thomas et al. 2015). These
findings imply that an improved, in-depth understanding of the differences in risk
perception between farmers and experts is necessary for the design of more effective
and successful policies to promote adaptation initiatives.
To gauge the prevailing perception of various groups, mental models (MMs) have
been successfully employed in the past, for example, to elicit farmers’perceptions and
underlying views on livelihood risks (Schoell and Binder 2009; Binder and Schöll
2010; Jones et al. 2011). MMs provide insight into perceptions and priority setting of
individuals (Morgan et al. 2002) and can help to understand risk perceptions and to
inform the design of effective risk communication strategies. In risk analysis, MMs
have been used to identify how individuals construct representations of risk (Atman
et al. 1994; Schöll and Binder 2010; Binder and Schöll 2010).Basedonthemental
model approach (MMA) (Morgan et al. 2002), Binder and Schöll (2010) developed
the structured mental model approach (SMMA). The SMMA combines the so-called
sustainable livelihood framework (SLF) (Scoones 1998)—a framework that shows
how sustainable livelihoods are achieved through access to resources of livelihood
capitals with the MMA (Morgan et al. 2002). The SMMA can help to understand how
farmers perceive and balance livelihood risks for their agricultural practices (Schoell
and Binder 2009; Binder and Schöll 2010).
This study aims (i) to understand how climate risks are integrated in the context of
other risks in the farmers’perception and decision-making process for taking action,
(ii) to identify differences between farmers’and experts’mental models regarding
farmers’agricultural risk perception, and (iii) to elaborate on possible consequences
for policies addressing farmers’livelihood risks and their agricultural adaptation
strategies in the face of climate change.
The paper is structured as follows: first,we present material and methods on how we
analyze climate risks in the context of farmers’livelihood risks and analyze differences in
perception between farmers’and experts’MMs. Second, we present results from applying our
approach to the Cauca Department in Colombia (South America) as an exemplary study for a
region for small-scale farmers in a developing country. Finally, we discuss our findings
concerning other literature and draw our conclusions.
Climatic Change (2018) 151:507–524 509
2 Material and methods
2.1 Study area
The Cauca Department is located in the southwestern part of Colombia with a size of
approximately 30,000 km2. Cauca is composed of a lowland coastal region, two Cordilleras
of the Andes, and an inner Andean valley. Agricultural land is concentrated in the inner
Andean valley. According to the latest agricultural census (DANE 2014), 83% of the farmers
in Cauca have a low educational achievement (elementary school only), 22% are illiterate, and
52% live in poverty according to Colombia’s Multidimensional Poverty Index (Salazar et al.
2011). The main stressors for agriculture and farmers alongside climate change are trade
liberalization and violent conflicts (Feola et al. 2015). Colombia has one of the longest
ongoing civil conflicts and one of the highest rates of internal displacement, estimated to be
7% of the country’s population and 29% of the rural population (Ibáñez and Vélez 2008).
Cauca is one of the regions in Colombia with a high rate of violence from armed conflicts
(Holmes et al. 2006). Especially for small farm households, weak institutional support and
absence of the state in rural areas have led to unequal land distribution and lacking technical
assistance as well as financial services for agricultural transformation (Pérez Correa and Pérez
Due to Cauca’s proximity to the Pacific Ocean, the region is subject to inter-annual climate
variability mainly driven by the El Niño Southern Oscillation (ENSO) (Poveda et al. 2001), a
feature that has great influence on agricultural productivity and, in consequence, farmers’
livelihood. A study by the CGIAR Research Program on Climate Change, Agriculture and
Food Security (CCAFS) shows that farmers in the study area are mostly affected by more
frequent droughts, storm and hail events, more erratic rains, and landslides as a consequence of
heavy rains (Garlick 2016). Even if uncertainty in future scenarios of extreme events is still
high, changes in inter-annual climate variability are of high relevance for farmers; there is
agreement that more intense and frequent extreme events are likely to be observed in the future
The Cauca region is particularly relevant for these types of analyses as (i) the region has a
high potential of being affected by climate change, (ii) interventions for rural development by
the government have been weak in the past, and (iii) because of the national and international
efforts to implement the peace process, Cauca has caught attention for implementing devel-
opment interventions. Many of these interventions could benefit from an in-depth understand-
ing of farmers’perceptions regarding the climate and nonclimate risks affecting their
Exemplary for Cauca, we selected a geographical domain of 10 km2with altitudes between
1600 and 1800 m a.s.l. within the boundaries of the municipality Popayan. We conducted the
interviews with experts and farmers in five rural villages and selected randomly 11 to 12
farmers each village (see details on sampling design in Chapter 2.4). The farm size of
interviewees was between 1 and 4 ha, half of them (45%) possessed legal land titles, and
41% of farmers have started the legalization process recently. The average age of interviewees
was 47 years old, 48% of them were women farmer, and the average household size was five
people. Overall, 74% of farmers depend on coffee (Coffea arabica) as their main agricultural
livelihood besides other crops and some livestock to complement income and for self-
consumption. Other crops and livestock that are managed in the farming systems are cassava
(Manihot esculenta), dry beans (Phaseolus vulgaris), maize (Zea mays), banana (Musa
510 Climatic Change (2018) 151:507–524
acuminata), cattle, and poultry. As the second most important crop, 19% of interviewed
households depend on sugarcane (Saccharum officinarum) and the derived product panela,
which is unrefined sugar in compact loaves of a rectangular shape. Most of farmers’income is
coming from on-farm agricultural activities and also from off-farm day labor activities in the
agricultural sector (harvest coffee in other farms). Generally, there are few job opportunities in
the study area.
2.2 Assessment of climate risks
Before we started analyzing risk perceptions, we conducted an assessment of climate risks and
impacts on main crops grown in the region and reviewed existing literature on the vulnerability
of farmers in the study area. First, we compared anomalies of precipitation, maximum
temperature, and minimum temperature in the study area with records about ENSO events.
We used data of a local weather station from the Instituto de Hidrologia, Meteorologia y
Estudios Ambientales de Colombia (IDEAM) and data of the Oceanic Niño Index (ONI) from
the National Oceanic and Atmospheric Administration (NOAA) (NOAA 2014). Second, we
used a simple climate envelope model to analyze the current and future climate suitability of
six crops in the study area. Finally, we reviewed the existing literature on climate change
impact assessment for Colombia. Detailed results of climate risk assessment in the study area
are presented in Online Resource 1.
2.3 Analyzing mental models to understand perceptions
Figure 1presents the conceptual approach of the study. Farmers’perceptions regarding climate
risks are shaped by their knowledge about the causes of climate change, their beliefs, social
norms, and values as well as through their experience with climate-related information and
past climate-related events. However, farmers’decision-making is not only shaped by climate
risks, but other agricultural production risks are also equal or even more important for farmers.
Farmers consider the complete mental model of risks when envisioning goals concerning their
livelihood strategy and make appropriate decisions about investments and adaptations of the
agricultural production system. In applying our approach, we captured experts’external views
of farmers’perception and compared it to the farmers’internal views.
To assess the importance of climate risks in the context of another risk in farmers’
agricultural production system, we identified differences between the perception of farmers
and that of experts regarding climate risks as placed in the context of other risks within the
farmers’livelihood system by analyzing and comparing each group’s MMs. The experts’
perspective on farmers’perceptions represented the external view, whereas the perspective of
the farmers themselves represented the internal one. We captured the external and internal
views on climate risks with two sets of structured interviews with experts and farmers, and we
used ranking techniques to show differences in perception.
2.4 Interviews with experts and farmers
A qualitative semi-structured interview study was conducted between June and September
2014 to examine perceptions of experts and farmers about farmers’livelihood risks and
farmers’barriers for adaptation to cope with risks they face in agricultural production. In a
first step, we conducted open interviews with 13 experts. In order to obtain a holistic view of
Climatic Change (2018) 151:507–524 511
experts’perceptions, we included regional, national, and international experts from different
fields of the analyzed agro-environmental system, namely four agronomists, three economists,
one environmental lawyer, one public government administrator, one nutritionist, one climate
change scientist, one ecologist, and one veterinarian. All experts have been regularly working
with farmers in the study region during the last 5 to 10 yrs and have still been working with
them at the time of the study. Following the expert interviews, we conducted 58 semi-
structured interviews with farmers from five different villages in the municipality of Popayan,
performing between 10 and 12 farmer interviews from different households and for each
village. The total population of farmers of the five villages was 499 at the time of the
interviews. We included farmers aged 20 to 60, and we designed the sample to ensure an
equal representation of women and men. Morgan et al. (2002) judge a small sample for
interviews within a population group that has relatively similar beliefs as reasonable. Schoell
and Binder (2009) found for the case of small farmers in Boyacá, Colombia, that after 5–10
interviews, no more new concepts emerged (Binder et al. 2015). To avoid interruption from
notes taken by the interviewer and to keep the natural flow of conversation, we recorded all
interviews with the consent of the participants. Subsequently, we transcribed the records of the
interviews for the analysis. The used guidebook for expert interviews can be found in
Online Resource 2and the guidebook for farmer interviews in Online Resource 3.
First, we assessed the experts’views on the farmers’concerns,risks,barriersfortaking
action, and enablers to take action by asking the following questions:
&What are the farmers’main livelihood concerns?
&Which risks do farmers face in agricultural production?
&Which are farmers’barriers to cope with these risks?
&What motivates (enablers) farmers to cope and adapt?
In the expert interviews, we received answers and explanations to the four guiding questions
about farmers’concerns, risks, barriers for taking action, and enablers to take action when
Fig. 1 Approach used for understanding climate risks in the context of farmers’livelihood risks
512 Climatic Change (2018) 151:507–524
facing risks in agricultural production. We noted all answers of experts for each question on
small cards. Answers from all experts were pooled after finishing all the interviews; we got 16
concerns, 10 risks, 13 barriers for taking action, and eight enablers to take action. Based on the
pooled elements, we used an online survey tool to ask the same group of experts to rank all
compiled elements according to the importance of the elements for farmers. The highest
ranked elements by experts were then selected to start the farmer interviews.
Second, we carried out the farmers’interviews. After explaining the overall purpose of the
study briefly as part of informed consent with farmers, we visualized the elements of the
experts through drawings we created for each question and then asked farmers to rank them
according to their priorities. After piloting the interviews with farmers, we decided to use only
the six highest ranked elements by experts to keep the ranking exercise for farmers simple. In
addition, we asked farmers at the end of each ranking if they would consider other elements to
be more important for them that the ones we used for the ranking (see Online Resource 3). We
did not mention climate change during the interviews for a specific purpose. Farmers should
rank the card elements without being biased by knowing the purpose of the interview, namely
to understand how they perceive climate risks in relation to other livelihood risks.
After finishing both interview series, we analyzed the differences in perception between
experts and farmers. To aggregate the individual rankings, we calculated a weighted average
based on the ranking of each element for the four questions. The overall ranking of experts and
farmers was calculated separately as follows:
where wis the weight, xthe response count of an answer choice of each question, and nthe
total number of answer elements. In our case of six elements per question, we calculated the
average ranking using weights starting at 6 for the highest ranked element and decreasing
towards 1 for the lowest ranked element.
We compared the average experts’rankings to farmers’rankings stratified by gender and
age group and then applied the hierarchical clustering approach (Ward 1963) to the farmers’
rankings to obtain groups of farmers with similar choices. The hierarchical clustering approach
by Ward (1963) is a widely used data analysis approach for similarity grouping to determine
distinct subgroups with similar characteristics (Vigneau and Qannari 2003). After obtaining
groups of farmers from clusters, we described them based on high ranks using first and second
ranked answers each question and demographic variables collected during the surveys.
3.1 Climate change risks in the study area
Figure 2shows that inter-annual rainfall variability is high. High variability in rainfall can be
observed between October and February for long-term weather records since 1980. Inter-
annual climate variations in the study area are mainly driven by the ENSO. The consequences
of ENSO for farmers and agricultural production are prolonged droughts (El Niño) or intense
rainfall over more extended periods (La Niña). The assessment of the six most relevant crops
in the study area revealed that variation in crop exposure to climate variability in Cauca is high
(see Online Resource 1). Farm households in the study area grow coffee, sugarcane, maize, dry
Climatic Change (2018) 151:507–524 513
beans, banana, and cassava. While banana, sugarcane, and cassava can better cope with
long-term climate change scenarios, dry beans and coffee are more likely affected by
increasing mean annual temperatures. Production of coffee and dry beans represents
an important livelihood for farmers in the study region but will likely face impacts
through climate change in the future. See Online Resource 1for more details on
climate change risks in the study area.
3.2 Farmers’rankings and differences to experts’rankings
We found that experts and farmers perceived farmers’livelihood concerns and enablers for
adaptation to agricultural production risks similarly, but risks and barriers for adaptation
differently (see Fig. 3). Also, farmers agreed on the selected answers as the most relevant
for them for each question; only a few farmers mentioned other elements. Beyond, the most
mentioned elements by farmers were concerns about health (five times) and access to tap water
Older farmers are more worried about climate change than younger farmers but rank
production failure low as risk (see Fig. 4). Interestingly, older farmers saw insecure transport
as a major risk and production failure as a lower risk, whereas this was the opposite for
Fig. 2 aInter-annual precipitationvariability calculated from weather records from a station (Apto G LValencia,
elevation of 1749 m a.s.l.) in the study area, and bONI and precipitation anomalies show the frequent influence
of ENSO episodes
514 Climatic Change (2018) 151:507–524
Regarding farmers’concerns (Fig. 3a), we found two issues experts and farmers
agreed upon: poverty is a chief concern in this region (ranked first by experts and
second by farmers) and neither climate change nor security problems are perceived to
be relevant in the study area. The key differences in perceived concerns were related to
government policies, access to credit, and market opportunities. Farmers were highly
worried regarding government policies (rank 1). They argued: “The government in the
capital, Bogota, is too far away and does not take into account the context of our region
when making new laws”(farmer’s interview, translated from Spanish, Colombia, Octo-
ber 2014). Experts ranked government policies lower with respect to concerns (rank 3),
but they agreed in their explanations with farmers that: “The government is focusing on
international trade agreements and is supporting medium-sized and large farmers, they
are not investing in small-scale farmers’production”(expert’s interview, translated from
Spanish, Colombia, August 2014). Both male and female farmers were highly worried
regarding their access to credit to be able to pay for labor and to purchase inputs for crop
production (rank 3). Experts, on the other hand, did not perceive that farmers need to be
worried about having access to credit (rank 6). In contrast, experts believed that farmers
were worried about market opportunities—a perception shared more often by women
than by men (see Fig. 3a).
Fig. 3 Differences in experts’(solid thick line) and farmers’(dashed thick line) rankings of farmers’aworries, b
risks, cbarriers to adaptation, and denablers for adaptation. Rankings of male farmers (dashed narrow line) and
female farmers (dashed-dotted narrow line)
Climatic Change (2018) 151:507–524 515
The main differences in the rankings between experts and farmers were related to risks
(Fig. 3b). For farmers, the highest perceived risks were a failure in crop production and social
vulnerability (lack of access to health and educational services). Experts, in contrast, perceived
insecurity (theft of products from plots or during transportation) and the unreliable weather to
be the highest risks for farmers. From a gender perspective, results showed that women and
men disagreed in rankings with experts for few themes. Whereas women agreed with experts
that insufficient planning is a major risk (even ranking it higher than experts), men agreed with
experts that insecurity is a high risk (for women, this was among the lowest risks). The risk
rankings showed clearly that farmers see the symptoms of social inequality (first rank of social
vulnerability), agricultural production, and market risks such as unstable prices or production
failure. Farmers ranked insufficient planning lower and unreliable weather very low compared
to experts. These results showed that experts rather ranked risks from climate higher than
farmers did. Experts would rather expect a higher planning activity of the farmers for
adaptation to climate risks. Contrastingly, farmers believed that they were doing already as
much as they could.
Experts and farmers also ranked barriers to adaptation differently (Fig. 3c). Experts
perceived both external factors such as the national policy and internal factors such as the
adaptive capacity of farmers to be the main barriers for deciding to take action and to adapt to
change. Farmers, in contrast, ranked the lack of information about weather and climate,
Fig. 4 Differences in experts’(solid thick line) and farmers’(dashed thick line) rankings of farmers’aworries, b
risks, cbarriers to adaptation, and denablers for adaptation. Rankings of farmers with age below 50 (dotted
narrow line) and farmers with age above 50 (dashed-doubled-dotted narrow line)
516 Climatic Change (2018) 151:507–524
especially seasonal weather forecasts, as their main barrier to act by adapting to change.
Farmers with age above 50 ranked not acting collectively the highest among the barriers for
adaptation. The ranking of barriers showed that especially younger farmers felt financially
unable (they ranked adaptation is too expensive high) to adapt to production risks from climate
change (Fig. 4c). The fact that they ranked adaptive capacity low as barrier showed that they
felt prepared to adapt to change but missed access to reliable weather information for planning
(ranked high as a barrier). The experts rather saw the necessity for more activity in adaptation
(high ranking of adaptive capacity as a barrier) and the rigid national policies impeding
farmers’adaptation. Experts did not share farmers’perception about the relevance of improved
The agreement between experts and farmers was mostly on farmers’motivations (enablers
to adaptation), which were family interests, increased quality of life, and traditional attachment
to land (Fig. 3d). Regarding the motivations, one expert mentioned during the interview that:
“Farmers in Cauca do have a strong connection to their roots. Territories and family unity are
very important”(expert’s interview, translated from Spanish, Colombia, August 2014). Within
these motivations, however, men and women placed different emphases. While women ranked
food security and traditional attachment to land higher than men, men ranked economic
interests and improved quality of life higher than women.
3.3 Farmer typologies of risk perception
The cluster analysis of farmers’first ranked answer to each question yielded four typologies of
farmers based on the farmers’perception of concerns, risks, barriers to adaptation, and enablers
i) Cluster 1—Higher-educated women–dominated farmers that are attributing risks to exter-
nal factors: farmers belonging to this group are worried about ending up in poverty and
fear that they will not besupported by the government. They consider insufficient planning
of their farming activities as well as a lack of access to social services (social vulnerability)
as key risks for their future. In the view of this group, farmers are dependent on weather
forecasts which they consider necessary to adapt to risks in agricultural production; they
perceive that not cooperating as a community is a barrier for taking action. Their adaptive
capacity could potentially be triggered ifthey perceived that the quality oflife for them and
their families would increase from implementing adaptation measures. The group of
farmers in cluster 1 consists of 62.5% women and 37.5% men with an average age of
44 years; 50% of the farmers reached the primary education level only, and 38% have
obtained a legal land title (50% have started a legal process). The average farm size is 4 ha.
ii) Cluster 2—Lower-educated production–focused farmers with the land title: farmers
belonging to this group are worried about a lack of access to credit or money to adapt
agricultural production to change, and they are concerned about the government policies
for rural development. These farmers perceive production failure due to uncontrollable
factors (pest and diseases, climate events) and volatile selling prices for their products as
the highest risks. The main barrier to adapt to change is a combination of low adaptive
capacity and missing support from institutions. Similar to the first group, production-
focused farmers are motivated to adapt to changes if their own and their families’quality
of life would increase. The group offarmers in cluster 2 consists of 43% women and 57%
men with an average age of 44 years; 64% of farmers reached the primary education level
Climatic Change (2018) 151:507–524 517
only, and 57% have obtained a legal land title (29% have started a legal process). The
average farm size is 2 ha.
iii) Cluster 3—Vulnerable, less-educated farmers with lower access to land: farmers belong-
ing to this group are worried about unstable markets for selling their products and the
associated poverty risk. Compared to the others, their perceived risk is based not only on
production but also on insecurity issues on their farms and during the transport of their
products to the market. The main barriers for this group of farmers are high costs for
implementing adaptation measures to cope with risks and the missing support from
institutions. Members of this group share motivation for adapting to change due to being
traditionally attached to their land and region. They want to improve the quality of life for
themselves and their families. The group of farmers in cluster 3 consists of 47% women
and 53% men with an average age of 46 years; 67% of farmers reached the primary
education level only, and 27% have obtained a legal land title (47% have started a legal
process). The average farm size is 2 ha.
iv) Cluster 4—Risk-aware male–dominated elderly farmers with the land title: farmers of
this group are worried about the government, risks from climate change, and the overall
security in their region. The risks perceived as the highest by these farmers are social
vulnerability such as the lacking access to social services and the risks associated with
regional insecurity. The main barriers to adaptation lack weather forecasts and a low
adaptive capacity on their farms. Like cluster 3 farmers, they feel traditionally attached to
their land and also believe that their land is highly suitable for agricultural activities. The
group of farmers in cluster 4 consists of 38% women and 62% men with an average age
of 57 years; 69% of farmers reached the primary education level only, and 62% have
obtained a legal land title (38% have started a legal process). The average farm size is
Detailed results of all comparisons, gender differences, and the hierarchical clustering of
farmers’rankings are presented in Online Resource 4.
This paper presented an integrative approach to understanding how climate risks are integrated
into the context of other risks in the farmers’decision-making process. We compared the
experts’with the farmers’view and differentiated between concerns, risks, and barriers for
adaptation, and enablers to adaptation. Two explanations in the literature stress why this type
of integrated analysis of farmers’risk is more suitable than an isolated analysis of climate
change risks: (i) farming systems of smallholders in the developing world are complex systems
of location-specific characteristics integrating agricultural and nonagricultural livelihood strat-
egies, which are vulnerable to a range of climate-related and other stressors (Morton 2007;
Feola et al. 2015), and (ii) farmers’long-term memory of climate events tends to decrease
significantly after a few years; therefore, the importance of climate risks in farmers’percep-
tions may equally decline very quickly after disturbing climate events (Brondizio and Moran
In the case of Cauca, the interviews were conducted in 2014, a year with ENSO neutral
conditions, the same as the two previous years. Farmers ranked climate risks low among their
perceived risks in agricultural production, a perception that might change if the interviews
518 Climatic Change (2018) 151:507–524
would have taken place in a year affected by ENSO conditions (e.g., with a prolonged drought
and high temperatures).
4.1 Reasons for potential maladaptation
Our findings showed that in Cauca, differences in experts’and farmers’perception and related
farmers’concerns, risks, and barriers and enablers for adaptation existed and could lead to
miscommunication and, consequently, to a maladaptation to climate change. This was partly
explained by the finding that experts agreed with farmers about main concerns for farmers but
disagreed about risks and barriers for adaptation, thus suggesting that the same view on a
problem might not necessarily lead to similar action propositions. Our study contributes to a
growing literature on how perception influences farmers’decision-making for adaptation and
adaptation behavior. We especially analyze how climate risks relate to and interact with other
risks and concerns in the farmers’decision-making process. This is important because
smallholder farmers in countries like Colombia are subject to multiple interdependent stressors
and deeply rooted social vulnerability. This interdependency requires a systemic perspective in
farmers’risks. Some other studies simply compare meteorological data with people’smemo-
ries of historical climate events (Boissiere et al. 2013); they attempt to link farmers’percep-
tions about climate change and related risks to adaptive behavior (Jacobi et al. 2013;Quiroga
et al. 2014; Barrucand et al. 2016). Our integrated view on farmers’perceptions and decision-
making might better capture the multitude of stressors for farmers and showed a lower
perceived relevance of climate risks than other studies focusing on farmers’perception of
climate risks. Especially for countries like Colombia, where multiple stressors and rooted
causes of social vulnerability act simultaneously on farmers’decision-making, the adaptive
capacity to climate risks is constraint (Reid and Vogel 2006; Feola et al. 2015). Our findings
show that farmers see the symptoms of social inequality but not their low adaptive capacity to
cope with risks from climate change. The farmers’low ranking of insufficient planning and
unpredictable weather as risk equally underlined their lack of perception of climate risks,
which was not perceived in the same way by experts. Contrastingly, the experts rather looked
first at climate risks and insecurity for transport, but instead did not perceive production failure,
unstable prices, or roots of social vulnerability as high risks.
4.2 What can we learn about climate risk communication?
While experts focus on communicating climate change risks, in cases such as we found in
Cauca, farmers do not see such information as practical since their highest perceived risk is the
poverty trap (social vulnerability) and the sum of risks related to the agricultural production of
which climate risks are merely a part. In their article, Reidand Vogel (2006) pointed to this fact
by stating that farmer’s associate crop losses sometimes with climate events which are,
however, not always seen as extraordinary and farmers are accustomed to coping with them.
This is also supported by our findings. Farmers in Cauca do not rank climate risks high among
their perceived risks, but they rank the lack of weather forecast and weak institutional services
as the most important barriers for adaptation to agricultural production risks. Differences
between experts’and farmers’views related to the weather forecast, seasonal forecast, and
climate change projections of long-term changes and inter-annual climate variability are
relevant issues in climate risk communication (Weber 2010). In the case of Cauca, experts
do not perceive that there is a lack of climate information for farmers. Thus, we recommend
Climatic Change (2018) 151:507–524 519
that experts should provide context-based–climate-related information in such a way that it
becomes tangible and usable for farmers in their everyday and long-term decision-making, for
example daily and seasonal weather information associated with agro-advisory services on
varieties, planting dates, and water management.
4.3 A need for a more holistic perspective on adaptation
Our findings show that farmers in Colombia do not perceive climate risks separately; they are
embedded in their mental models of agricultural livelihood risks. Other scholars have shown
that in Colombia, climate change, trade liberalization, and violent conflicts act simultaneously
on farmers’livelihoods, but policies address them separately (Feola et al. 2015). If the
implementation of policy actions is not coordinated, they might hinder each other or lead to
failure. Understanding differences between experts’and farmers’mental models about risks is
the first step to better design adequate policy actions for adaptation. Additionally, our results
show that farmers in Cauca hardly trust national policies as mentioned by some experts as well
as by farmers during the interviews. Farmers in Cauca are overall concerned about national
policies. Llorente (2015) asserts that this is a result of the violent conflict which, in rural areas
like Cauca, has led to profound mistrust in the state. Feola et al. (2015) argue that the
institutional integration between different levels of government has been historically difficult
in Colombia. Agricultural policies are often not based on the realities of smallholders.
However, before designing adaptation strategies for farmers, the deeply rooted social vulner-
ability and inequality must be addressed and brought to the focus of experts. Ideally, this
should be done together with farmers as a social learning process.
“Adaptation is a dynamic social process”(Adger 2003, p. 387), including many different actors.
We agree with Vogel and Henstra (2015) to involve local actors in the development process of
adaptation plans instead of operationalizing top-down adaptation measures. We suggest starting
this process by developing a Local Adaptation Plan of Action (LAPA) in Cauca, aiming at initiating
a bottom-up process of adaptation planning, which takes into account the community and
individual levels (Jones and Boyd 2011; Regmi et al. 2014). The uptake of adaptation strategies
depends on barriers and the adaptive capacity of both the community and the individual farmer.
Effective adaptation at the community level would require a mix of top-down structural
measures, often provided by institutions, including national adaptation plans, financial ser-
vices, economic incentives, and nonstructural measures developed by the community itself as
a collective action (Girard et al. 2015).
Finally, transformative adaptation instead of targeting climate change by individual tech-
nological solutions would be a better approach for Colombian smallholders because it focuses
on the root of vulnerability rather than on the adaptation of production systems only (Feola
2013). Such an approach would bring a more central role to farmers in developing adaptation
options together with experts and would stimulate a social learning process in which science
engages with lay knowledge and contributes with its transformative role to society (Feola
2013;Mauseretal.2013). Climate change in the context of Latin America is characterized by
complex lay and expert knowledge systems, social coping mechanisms, and ancient resilience
mechanisms to adapt to perturbations (Sietz and Feola 2016). Several scholars support the
need for an integrated approach to address critical dynamics of vulnerabilities and constraints
for adaptation around climate change more integrated into cultural and socioeconomic realities
(De los Ríos Cardona and Almeida 2011;Ulloa2011). Other authors call for identification of
causes of vulnerability and transformative solutions to cope with risks from climate change
520 Climatic Change (2018) 151:507–524
(Ribot 2014). Anyway, the state and its institutions are also important to provide a policy
framework for adaptation, to intervene when resources are required, and to enable needed
policies (Ramirez-Villegas and Khoury 2013). Finally, cooperatives could play a crucial role
and become vehicles for rural development, opposite to previous top-down approaches that
have failed in Colombia (Gutiérrez 2014).
For further research, we recommend to study the dynamics in the farmers’complex
livelihood system, to analyze the actor’s network of farmers, and to identify adaptation
pathways for farmers to cope with climate change in Cauca, Colombia.
Since the 2015 Paris Agreement (COP 21), the political commitment to take action on climate
change increased. Even in developing countries, policymakers have started working more
specifically towards policies for achieving climate resilience, especially in the agricultural
sector. Agriculture, both contributing to climate change and being affected by climate change,
needs a transformation to become more sustainable and climate resilient by improving farmers’
livelihood system and farm productivity while reducing emissions from agriculture. Especially,
transforming smallholders’agriculture in developing countries such as Colombia requires
greater attention to human livelihoods and related concerns, risks, barriers to decision-making,
and the adoption of adaptation strategies.
This study applied a mental model approach to understand better climate risks in the context
of farmers’decision-making process. It showed that climate risks need to be seen in the overall
context of farmers’livelihood risks. Climate change adaptation strategies and policies can be
more successful if they (i) address specific climate risks, (ii) simultaneously address other risks
of major importance for farmers, and (iii) target more climate risk–sensitive groups of farmers.
Our research demonstrates thatunderstanding differences in experts’and farmers’perception of
farmers’livelihood risks could avoid maladaptation and improve climate risk communication
from experts to farmers. Therefore, we recommend to study the dynamics in the farmers’
complex livelihood system, to analyze the actor’s network of farmers, and to identify adaptation
pathways for farmers to cope with climate change in Cauca, Colombia.
Acknowledgements This work was implemented as part of the CGIAR Research Program on Climate Change,
Agriculture and Food Security (CCAFS), which is carried out with support from the CGIAR Fund Donors and
through bilateral funding agreements. For details, please visit https://ccafs.cgiar.org/donors. The views expressed
in this document cannot be taken to reflect the official opinions of these organizations. We thank the International
Center for Tropical Agriculture (CIAT) and Fundacion Ecohabitats for supporting the fieldwork.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International
License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and repro-
duction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a
link to the Creative Commons license, and indicate if changes were made.
Adger WN (2003) Social capital, collective action, and adaptation to climate change. Econ Geogr 79:387–404
Alley RB, Marotzke J, Nordhaus WD et al (2003) Abrupt climate change. Science 299:2005–2010. https://doi.
Climatic Change (2018) 151:507–524 521
Atman CJ, Bostrom A, Fischhoff B et al (1994) Designing risk communications: completing and correcting
mental models of hazardous processes, part I. Risk Anal 14:779–788
Baca M, Läderach P, Haggar J et al (2014) An integrated framework for assessing vulnerability to climate change
and developing adaptation strategies for coffee growing families in Mesoamerica. PLoS ONE 9:11.
Barnes AP, Toma L (2011) A typology of dairy farmer perceptions towards climate change. Clim Chang 112:
Barrucand MG, Giraldo Vieira C, Canziani PO (2016) Climate change and its impacts: perception and adaptation
in rural areas of Manizales, Colombia. Clim Dev 5529:1–13. https://doi.org/10.1080
Binder CR, Schöll R (2010) Structured mental model approach for analyzing perception of risks to rural
livelihood in developing countries. Sustainability 2:1–29. https://doi.org/10.3390/su2010001
Binder CR, Schoell R, Popp M (2015) The structured mental model approach. In: Ruth M (ed) Handbook of
research methods and applications in environmental studies, 1st edn. Edward Elgar Publishing Ltd,
Cheltenham, pp 122–147
Bizikova L, Crawford E, Nijnik M, Swart R (2012) Climate change adaptation planning in agriculture: processes,
experiences and lessons learned from early adapters. Mitig Adapt Strateg Glob Chang 19:411–430.
Boissiere M, Locatelli B, Sheil D, et al (2013) Local perceptions of climate variability and change in tropical
forests of Papua, Indonesia. Ecol Soc 18. doi: https://doi.org/10.5751/ES-05822-180413
Brondizio ES, Moran EF (2008) Human dimensions of climate change: the vulnerability of small farmers in the
Amazon. Philos Trans R Soc Lond Ser B Biol Sci 363:1803–1809. https://doi.org/10.1098/rstb.2007.0025
Carlton JS, Mase AS, Knutson CL et al (2016) The effects of extreme drought on climate change beliefs, risk
perceptions, and adaptation attitudes. Clim Chang:211–226. https://doi.org/10.1007/s10584-015-1561-5
DANE (2014) Censo Nacional Agropecuario 2014. http://www.dane.gov.co/index.php/estadisticas-por-
tema/agropecuario/censo-nacional-agropecuario-2014. Accessed 1 Jan 2016
De los Ríos Cardona JC, Almeida J (2011) Percepciones y formas de adaptación a riesgos socioambientales:
análisis en contextos locales en la región del páramo de Sonsón, Antioquia, Colombia. In: Ulloa A (ed)
Perspectivas culturales del clima, Centro Edi. Universidad Nacional de Colombia, Bogotá, pp 451–473
Ding D, Maibach EW, Zhao X et al (2011) Support for climate policy and societal action are linked to perceptions
about scientific agreement. Nat Clim Chang 1:462–466. https://doi.org/10.1038/nclimate1295
Eakin H, Lemos MC (2006) Adaptation and the state: Latin America and the challenge of capacity-building
under globalization. Glob Environ Chang 16:7–18. https://doi.org/10.1016/j.gloenvcha.2005.10.004
Eakin H, Lemos MC (2010) Institutions and change: the challenge of building adaptive capacity in Latin
America. Glob Environ Chang 20:1–3. https://doi.org/10.1016/j.gloenvcha.2009.08.002
Eitzinger A, Läderach P, Bunn C et al (2014) Implications of a changing climate on food security and
smallholders’livelihoods in Bogotá, Colombia. Mitig Adapt Strateg Glob Chang 19:161–176. https://doi.
Feola G (2013) What (science for)adaptation to climate change in Colombian agriculture? A commentary on “A
way forward on adaptation to climate change in Colombian agriculture: perspectives towards 2050”by J.
Ramirez-Villegas, M. Salazar, A. Jarvis, C. E. Navarro-Valc. Clim Chang 119:565–574. https://doi.
Feola G, Agudelo Vanegas LA, Contesse Bamón BP (2015) Colombian agriculture under multiple exposures: a
review and research agenda. Clim Dev 7:37–41. https://doi.org/10.1080/17565529.2014.934776
Finnis J, Sarkar A, Stoddart MCJ (2015) Bridging science and community knowledge? The complicating role of
natural variability in perceptions of climate change. Glob Environ Chang 32:1–10. https://doi.org/10.1016/j.
Garlick C (2016) CCAFS household baseline study, Latin America and South East Asia (2014-2015)
Girard C, Pulido-Velazquez M, Rinaudo JD et al (2015) Integrating top-down and bottom-up approaches to
design global change adaptation at the river basin scale. Glob Environ Chang 34:132–146. https://doi.
Gutiérrez JD (2014) Smallholders’agricultural cooperatives in Colombia: vehicles for rural development?
Desarro Soc:219–271. https://doi.org/10.13043/DYS.73.7
Haden VR, Niles MT, Lubell M et al (2012) Global and local concerns: what attitudes and beliefs motivate
farmers to mitigate andadapt to climate change? PLoS One 7. https://doi.org/10.1371/journal.pone.0052882
Holmes JS, Gutiérres de Piñeres SA, Curtin KM (2006) Drugs, violence, and development in Colombia: a
department-level analysis. Lat Am Polit Soc 48:157–184. https://doi.org/10.1111/j.1548-2456.2006.tb00359.x
Ibáñez AM, Vélez CE (2008) Civil conflict and forced migration: the micro determinants and welfare
losses of displacement in Colombia. World Dev 36:659–676. https://doi.org/10.1016/j.
522 Climatic Change (2018) 151:507–524
IPCC (2014a) 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 [Core Writing Team, R.K. Pachauri
and L.A. Meyer (eds.)]. IPCC, Geneva, pp151
IPCC (2014b) Climate change 2014: 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 [Field, C.B., V.R. Barros, D.J. Dokken, K.J Mach, M.D. Mastrandrea, T.E.
Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S.
MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, pp1132
Jacobi J, Schneider M, Bottazzi P et al (2013) Agroecosystem resilience and farmers’perceptions of climate
change impacts on cocoa farms in Alto Beni. Bolivia Renew Agric Food Syst:1–14. https://doi.org/10.1017
Jones L, Boyd E (2011) Exploring social barriers to adaptation: insights from Western Nepal. Glob Environ
Chang 21:1262–1274. https://doi.org/10.1016/j.gloenvcha.2011.06.002
Jones NA, Ross H, Lynam T etal (2011) Mental model an interdisciplinary synthesis of theory and methods. Ecol
Lee TM, Markowitz EM, Howe PD et al (2015) Predictors of public climate change awareness and risk
perception around the world. Nat Clim Chang. https://doi.org/10.1038/nclimate2728
Llorente MV (2015) From war to peace: security and the stabilization of Colombia. Stab: Int J Sec Dev 4:1–5
Mauser W, Klepper G, Rice M et al (2013) Transdisciplinary global change research: the co-creation of
knowledge for sustainability. Curr Opin Environ Sustain 5:420–431. https://doi.org/10.1016/j.
Morgan MG, Fischhoff B, Bostrom A, Atman CJ (2002) Risk communication. Cambridge University Press, New York
Morton JF (2007) The impact of climate change on smallholder and subsistence agriculture. Proc Natl Acad Sci
Niles MT, Brown M, Dynes R (2015) Farmer’s intended and actual adoption of climate change mitigation and
adaptation strategies. Clim Chang. https://doi.org/10.1007/s10584-015-1558-0
NOAA (2014) El Niño - Southern Oscillation (ENSO): historical episodes. http://origin.cpc.ncep.noaa.
gov/products/precip/CWlink/MJO/enso.shtml. Accessed 31 Aug 2014
Ovalle-Rivera O, Läderach P, Bunn C et al (2015) Projected Shifts in Coffea arabica Suitability among Major
Global Producing Regions Due to Climate Change. PLoS ONE 10(4):e0124155. https://doi.org/10.1371
Patt AG, Schröter D (2008) Perceptions of climate risk in Mozambique: implications for the success of adaptation
strategies. Glob Environ Chang 18:458–467. https://doi.org/10.1016/j.gloenvcha.2008.04.002
Pérez Correa E, Pérez Martínez M (2002) El sectorrural en Colombia y su crisis actual.Cuad Desarro Rural:35–58
Poveda G, Jaramillo A, Gil MM et al (2001) Seasonality in ENSO related precipitation, river discharges, soil
moisture, and vegetation index in Colombia. Water Resour Res 37:2169–2178. https://doi.org/10.1029/2000
Quiroga S, Suárez C, Solís JD (2014) Exploring coffee farmers’awareness about climate change and water
needs: smallholders’perceptions of adaptive capacity. Environ Sci Pol 5. https://doi.org/10.1016/j.
Ramirez-Villegas J, Khoury CK (2013) Reconciling approaches to climate change adaptation for Colombian
agriculture. Clim Chang. https://doi.org/10.1007/s10584-013-0792-6
Ramirez-Villegas J, Salazar M, Jarvis A, Navarro-Racines CE (2012) A way forward on adaptation to climate
change in Colombian agriculture: perspectives towards 2050—supplementary material. Clim Chang 2006.
Regmi BR, Star C, Leal Filho W (2014) Effectiveness of the local adaptation plan of action to support climate
change adaptation in Nepal. Mitig Adapt Strateg Glob Chang:461–478. https://doi.org/10.1007/s11027-014-
Reid P, Vogel C (2006) Living and responding to multiple stressors in South Africa—glimpses from KwaZulu-
Natal. Glob Environ Chang 16:195–206. https://doi.org/10.1016/j.gloenvcha.2006.01.003
Ribot J (2014) Cause and response: vulnerability and climate in the Anthropocene. J Peasant Stud 41:667–705.
Salazar RCA, Cuervo YD, Pardo R (2011) Índice de Pobreza Multidimensional para Colombia. Arch Econ
Schoell R,Binder CR (2009) System perspectives of experts and farmers regarding the role of livelihood assets in
risk perception: results from the structured mental model approach. Risk Anal 29:205–222. https://doi.
Schöll R, Binder CR (2010) Comparison of farmers’mental models of the present and the future: a case study of
pesticide use. Futures 42:593–603. https://doi.org/10.1016/j.futures.2010.04.030
Scoones I (1998) Sustainable rural livelihoods: a framework for analysis: IDS working paper 72. Brighton
Climatic Change (2018) 151:507–524 523
Sietz D, Feola G (2016) Resilience in the rural Andes: critical dynamics, constraints and emerging opportunities.
Reg Environ Chang 16:2163–2169. https://doi.org/10.1007/s10113-016-1053-9
Thomas M, Pidgeon N, Whitmarsh L, Ballinger R (2015) Mental models of sea-level change: a mixed methods
analysis on the Severn Estuary, UK. Glob Environ Chang 33:71–82. https://doi.org/10.1016/j.
Turner BL, Matson PA, McCarthy JJ et al (2003) Illustrating the coupled human-environment system for
vulnerability analysis: three case studies. Proc Natl Acad Sci U S A 100:8080–8085. https://doi.
Ulloa A (2011) Construcciones culturales sobre el clima, Centro Edi. Universidad Nacional de Colombia, Bogotá
Vermeulen SJJ, Aggarwal PKK, Ainslie A et al (2011) Options for support to agriculture and food security under
climate change. Environ Sci Pol 15:1–9. https://doi.org/10.1016/j.envsci.2011.09.003
Vigneau E, Qannari EM (2003) Clustering of variables around latent components. Commun Stat Part B Simul
Comput 32:1131–1150. https://doi.org/10.1081/SAC-120023882
Vogel B, Henstra D (2015) Studying local climate adaptation: a heuristic research framework for comparative
policy analysis. Glob Environ Chang 31:110–120. https://doi.org/10.1016/j.gloenvcha.2015.01.001
Ward JH (1963) Hierarchical grouping to optimize an objective function. J Am Stat Assoc 58:236–244.
Weber EU (2006) Experience-based and description-based perceptions of long-term risk: why global warming
does not scare us (yet). Clim Chang 77:103–120. https://doi.org/10.1007/s10584-006-9060-3
Weber EU (2010) What shapes perceptions of climate change? Wiley Interdiscip Rev Clim Chang 1:332–342.
&Claudia R. Binder
&Markus A. Meyer
International Center for Tropical Agriculture (CIAT), Km 17, Recta Cali–Palmira 6713, Apartado Aéreo,
763537 Cali, Colombia
Department of Geography, University of Munich (LMU), Luisenstraße 37, 80333 Munich, Germany
Laboratory for Human-Environment Relations in Urban Systems, IIE, ENAC, Ecole Polytechnique Fédérale
de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
524 Climatic Change (2018) 151:507–524
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center
GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers
and authorised users (“Users”), for small-scale personal, non-commercial use provided that all
copyright, trade and service marks and other proprietary notices are maintained. By accessing,
sharing, receiving or otherwise using the Springer Nature journal content you agree to these terms of
use (“Terms”). For these purposes, Springer Nature considers academic use (by researchers and
students) to be non-commercial.
These Terms are supplementary and will apply in addition to any applicable website terms and
conditions, a relevant site licence or a personal subscription. These Terms will prevail over any
conflict or ambiguity with regards to the relevant terms, a site licence or a personal subscription (to
the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of
the Creative Commons license used will apply.
We collect and use personal data to provide access to the Springer Nature journal content. We may
also use these personal data internally within ResearchGate and Springer Nature and as agreed share
it, in an anonymised way, for purposes of tracking, analysis and reporting. We will not otherwise
disclose your personal data outside the ResearchGate or the Springer Nature group of companies
While Users may use the Springer Nature journal content for small scale, personal non-commercial
use, it is important to note that Users may not:
use such content for the purpose of providing other users with access on a regular or large scale
basis or as a means to circumvent access control;
use such content where to do so would be considered a criminal or statutory offence in any
jurisdiction, or gives rise to civil liability, or is otherwise unlawful;
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association
unless explicitly agreed to by Springer Nature in writing;
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a
systematic database of Springer Nature journal content.
In line with the restriction against commercial use, Springer Nature does not permit the creation of a
product or service that creates revenue, royalties, rent or income from our content or its inclusion as
part of a paid for service or for other commercial gain. Springer Nature journal content cannot be
used for inter-library loans and librarians may not upload Springer Nature journal content on a large
scale into their, or any other, institutional repository.
obligated to publish any information or content on this website and may remove it or features or
functionality at our sole discretion, at any time with or without notice. Springer Nature may revoke
this licence to you at any time and remove access to any copies of the Springer Nature journal content
which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or
guarantees to Users, either express or implied with respect to the Springer nature journal content and
all parties disclaim and waive any implied warranties or warranties imposed by law, including
merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published
by Springer Nature that may be licensed from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a
regular basis or in any other manner not expressly permitted by these Terms, please contact Springer