Coffee farmers´ awareness about climate change
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Transcript of Coffee farmers´ awareness about climate change
Exploring coffee farmers’ awareness about climatechange and water needs: Smallholders’ perceptionsof adaptive capacity
Sonia Quiroga a,*, Cristina Suarez a, Juan Diego Solıs b
aUniversidad de Alcala, SpainbUniversidad Nacional Autonoma de Nicaragua, Nicaragua
e n v i r o n m e n t a l s c i e n c e & p o l i c y 4 5 ( 2 0 1 5 ) 5 3 – 6 6
a r t i c l e i n f o
Keywords:
Climate change
Crop production risk
Coffee farmers’ adaptive capacity
Nicaragua
Water scarcity
a b s t r a c t
Nicaragua is one of the four countries most affected by climate change, and coffee
production is expected to vastly shrink in some critical areas. This can have considerable
effects on social structure since nearly a third of its working population depend on coffee for
a living. Social perceptions of climate change and water pressures are a key issue in the
public’s acceptance of adaptation measures. Furthermore, the existing risk for crop pro-
duction is not necessarily correlated with the farmers’ awareness of that threat. This paper
focuses on coffee producers’ perception of risk and adaptive capacity for coffee crops in
Nicaragua in response to climate change and water availability. We aim to analyze how
dependent the producers are on water resources, and if this reliance affects their perception
of risk and their expectations with regard to public and private support for dealing with
adaptation. A survey of 212 representative farmers of the national population of farms in the
country’s two most important production areas was conducted for this purpose. We
consider socio-economic and biophysical variables to explain the farmers’ perceptions.
Our findings show that experience and technical capacity are relevant to the adaptive
capacity although smallholders do not always show high concern and their expectations
with regard to external support are very low. The paper can be useful to prioritize the
measures necessary for a greater level of involvement from stakeholders.
# 2014 Published by Elsevier Ltd.
Available online at www.sciencedirect.com
ScienceDirect
journal homepage: www.elsevier.com/locate/envsci
1. Introduction
Climate change is set to modify the geography of coffee crop
suitability in the coming decades (Glenn et al., 2013; Laderach
et al., 2010, 2013; Rahn et al., 2013; Schroth et al., 2009). This is
naturally related to potential damages and benefits that may
arise in some countries, which will affect domestic and
international policies, trading patterns, the use of resources,
regional planning and consequently people’s welfare (Fischer
* Corresponding author. Tel.: +34 918856370.E-mail address: [email protected] (S. Quiroga).
http://dx.doi.org/10.1016/j.envsci.2014.09.0071462-9011/# 2014 Published by Elsevier Ltd.
et al., 2005; Lobell et al., 2005). Coffee cultivation engages over
100 million people in production and processing. Smallholder
coffee farmers account for over 70% of this labour intensive
crop. Deteriorating terms of trade and price volatility have
historically threatened coffee production and these problems
are exacerbated by the effects of changing climatic conditions.
Prolonged droughts, rising temperatures or heavy rains can
affect coffee plants directly, by affecting growing conditions,
and indirectly, by providing favourable conditions for pests
and diseases (Panhuysen and Pierrot, 2014).
e n v i r o n m e n t a l s c i e n c e & p o l i c y 4 5 ( 2 0 1 5 ) 5 3 – 6 654
Changes to crop productivity and production suitability as
a result of global warming have been extensively predicted for
the coming future (Trnka et al., 2011, 2014; Boko et al., 2007;
Anwar et al., 2013; Van Vuuren et al., 2007), with many places
in Latin America set to be hotspots (Flores et al., 2002; Tucker
et al., 2010; FAO, 2011). In the case of Mesoamerica, and
particularly Nicaragua, climate change may reduce coffee crop
suitability by up to 40% (Glenn et al., 2013; Laderach et al., 2010)
(Fig. 1).
Mesoamerican countries are especially concerned with the
potential local and global impacts of climate change over the
coming decades. Nicaragua, in particular, is already one of the
four countries most affected by climate change, according to
the 2014 Global Climate Risk Index (Kreft and Eckstein, 2013),
and these changes will affect domestic and international
policies, trading patterns, competitiveness for water
resources, regional planning and farmers’ welfare. Nearly a
third of its working population, about 750,000 people, depend
on coffee directly or indirectly for a living. Coffee provides 20%
of GDP and represents 20–25% of export revenues in
Nicaragua. These potential losses have been estimated to
represent almost 20% of Nicaragua’s GDP (Flores et al., 2002),
suggesting that the country’s coffee production is expected to
shrink by 82% between 2010 and 2050 (Laderach et al., 2013).
However, some opportunities to adapt do exist. According
to the Intergovernmental Panel on Climate Change (IPCC)
conceptions, adaptive capacity is defined as a system’s ability
to adjust to climate change in order to reduce or mitigate
potential damage. Adaptive capacity is dynamic, and
depends among other factors on natural and artificial assets,
social benefits and networks, human capital and institutions,
governance, national income, health and technology. Adap-
tation to climate change includes adjustments in natural or
human systems in response to actual or expected climatic
risk, which moderate harm or exploit beneficial opportunities
(IPCC, 2007). These adjustments can be planned or
Fig. 1 – Map of change in suitability for coffee production in Me
Source: International Center for Tropical Agriculture, CIAT, A.Ei
autonomous (IPCC, 2001). Potential responses to climate
change effects on agriculture include farm level adaptation,
through capacity building, financial transfer tools, and
migration to more suitable areas (Iglesias et al., 2012; Anwar
et al., 2013; Baca et al., 2014; Boko et al., 2007). In particular,
coffee production is expected to suffer important migrations
towards southern regions (Laderach et al., 2013).
Unfortunately, there are many cultural, technical and
social obstacles to implementing adaptation measures, and
farmers’ perceptions of climate change risks and their
adaptive capacity are essential for eliminating some of these
barriers. Adaptation to climate change requires that farmers
using traditional techniques of agricultural production first
notice that the climate is changing and that this represents a
threat to their production (Maddison, 2007). This paper does
not attempt to review the current evidence of climate change
impacts on coffee production, but rather to address the
conditioning factors affecting farmers’ perception of their own
adaptive capacity. In this context, farmers’ perceptions about
climate change impacts and their expectations with regard to
receiving resources from external agents – such as govern-
ment, cooperatives or NGOs – may be crucial to understanding
whether coffee farmers adaptation is likely to take place
mostly on an individual basis, or whether they will be
confident about some kind of external intervention (public
or private) designed to promote the adaptation process. In this
paper we explore farmers’ expectations about receiving
external support to promote adaptation measures. Here we
analyze expectations with regard to public support from the
government through any of its programmes and the private
support through farmers associations or cooperatives and
NGOs. We do not consider the role of private external firms,
such as banks, or private insurance companies that can play
an important role as facilitators of valuable financial tools.
Among the current public programmes, Nicaragua has a Social
Safety Net programme designed to address both current and
soamerica.
[email protected], 2014.
e n v i r o n m e n t a l s c i e n c e & p o l i c y 4 5 ( 2 0 1 5 ) 5 3 – 6 6 55
future poverty via cash transfers intended for households
living in extreme poverty in rural areas (Maluccio, 2005) and
other programmes such as U.S. Agency for International
Development (USAID) programme or the German govern-
ment’s assistance agency (KDR) (Vakis et al., 2004). Also, NGOs
are working to develop a market situation that is sustainable
for workers and the environment (Linton, 2005).
Although coffee farmers still tend to perceive climate risks
as less urgent than those posed by market volatility, declining
prices and institutional changes (Eakin et al., 2006; Gay et al.,
2006), important direct and indirect impacts are likely to occur
in response to climate change, with drivers that are diverse
and complex. For example, increased temperatures directly
affect crop suitability and productivity, and may also favour
the progress of pests and diseases; erosion may determine
quality, etc. Among other factors, water scarcity emerges as
one of the key stressors for coffee suitability in this area
(Laderach et al., 2013). Over the next decades, Nicaragua will
increasingly be affected by global climate change and most
models coincide in predicting a rainfall decrease of more than
100 mm by 2050, which will have a major impact on
Nicaragua’s coffee exports and life conditions in rural areas
(Baca et al., 2014; Anderson et al., 2008; IPCC, 2007).
Water is important for coffee production, and information
about water is relevant to farmers’ perceptions. Nicaragua is
affluent in terms of overall water resources and water for use,
the latter being in excess of 38,668 mm per capita per year
(FAO-Aquastat, 2013). Although this level is above average for
Mesoamerican countries, contamination has meant that the
quality of water is very low, and thus water security is actually
quite low as well (OECD, 2010), water security being the
reliable availability of an acceptable quantity and quality of
water for health, livelihoods and production, coupled with an
acceptable level of water-related risks (Grey and Sadoff, 2007).
This is in part due to low financial resources for maintaining
an adequate quality of water resources; as a result of
governance problems affecting integral water resource man-
agement, this issue clearly influences the adaptation capacity
(Quiroga et al., 2011). In Nicaragua, the 1999–2001 droughts
further compounded the problem of low coffee prices. The
effects are complex and clearly affect small-scale farmers. For
example, in the tropical dry regions, including the northern
departments that we consider in this study, the farmers did
not harvest their subsistence crops that they mix with coffee,
further increasing vulnerability (Bacon, 2005).
The main objectives of this paper are: (i) to understand if
current crop water needs and water risk concerns have an
effect on farmers’ adaptive capacity perception, (ii) to
determine farmers’ expectations with regard to receiving
some public or private funds for adaptation endorsement,
especially related to government, farmers’ cooperatives and
NGO support, (iii) to analyze the sensitivity of these percep-
tions to certain structural factors, such as the labour force and
technology. The article is organized as follows. The second
section provides general and detailed information on our
methodological steps. The third section describes the results
of the estimated adaptive capacity perceptions for two main
coffee production regions in Nicaragua. This section also
shows the estimates of marginal effects and probabilities
forecast for adaptation support perceptions in terms of labour
force and water needs. The final section presents the
conclusions of the paper.
2. Methods
2.1. Methodological framework
The methodological approach used here integrates different
components affecting individuals’ perceptions about adaptive
capacity in order to identify their main drivers. The method-
ology includes the following three steps: (i) we estimate
ordered probit models to characterize the main drivers
affecting farmers’ perceptions of their own adaptive capacity.
Ordered probit models have proven useful as a public
participation method to evaluate the effects of socio-econom-
ic characteristics on stakeholder insights, and particularly to
understand individual perceptions about environmental
issues and climate change concern (Bosselmann, 2012; Drake
et al., 2013; Garcıa de Jalon et al., 2013; Layton and Brown, 2000;
Maddison, 2007). Here we analyze the drivers for adaptive
capacity perceptions. Our analysis process includes the link
between objective factors – such as labour or capital – and
subjective factors – such as current crop water needs,
expectations about climate change or water scarcity risk –
so as to analyze current farmers’ perceptions. For this purpose,
we estimate the marginal effects of the considered determi-
nants on the estimated probability of farmers’ responses. (ii) In
a second phase, we try to distinguish between the different
sources of expected funding for climate change adaptation. To
do so, we analyze the likely role of the government, farmers’
cooperatives and NGOs. (iii) Finally, we simulate some
potential structural adjustments. We use the estimated
models to simulate the cumulative probability of farmers’
responses in terms of changes to labour force and water needs.
Fig. 2 shows a summary of this analysis structure.
2.2. Data collection
Data collection was done through a survey. For the sample
selection process, we considered the population of 1624 coffee
farmers from the departments of Jinotega and Estelı, who are
registered at the Ministry of Agriculture and Forestry of
Nicaragua (MAGFOR). We chose these departments because
coffee production is centred in the northern part of the central
highlands north and east of Estelı, and also in the hilly volcanic
region around Jinotega. From this population, a stratified
random sampling proportional to farmer type was assigned.
The representative sample size was estimated by considering
a sampling error limit of 6.34% and a 95% confidence level. The
sample size subsequently contained 215 farmers. Complete
information was obtained from 212 farmers. Table 1 shows the
sample distribution in the considered departments.
The main objective of the questionnaire was to obtain
information about the coffee farmers’ perceptions of their
adaptive capacity, as well as the socio-economic variables
affecting this perception. We divided the questionnaire items
into two separate categories: (i) general characteristics of farm
exploitation, including data on the labour force, capital, etc.
and (ii) subjective opinion or perceptions on climate change
Fig. 2 – Description of the study.
e n v i r o n m e n t a l s c i e n c e & p o l i c y 4 5 ( 2 0 1 5 ) 5 3 – 6 656
risks and adaptive capacity. Farmers were asked to rate the
relevance of certain items associated with perceptions on
climate change risks and adaptive capacity using an equidis-
tant four-point Likert scale.
Data collection was conducted in several steps: (i) organi-
zation and management; (ii) focus group and pilot tests; and
(iii) face-to-face field surveys. A detailed analysis of these
steps is given below.
(i) For the organization and management of the field survey
collection, the first step was to present a research proposal
and work plan to authorities at the Universidad Nacional
Autonoma de Nicaragua (UNAN-Leon). The aim was to
achieve the institutional endorsement needed to obtain
technical support from the MAGFOR delegates at the
national and departmental levels.
(ii) We conducted a focus group session in which a number of
experts in the Ministry were informed about the goals of
our analysis. They were asked about the target population
in the selected departments, and asked for comments and
suggestions on a draft questionnaire. The valuable
information they provided (i.e. farmers registration lists
Table 1 – Sample distribution among the departments.
Departments Total numberof registered
farmers
Percentageof total
Estelı 1020 0.63
Jinotega 604 0.37
Total 1624 1.00
by municipalities, important amendments to the ques-
tions, etc.) was considered when improving the data
collection. Pilot testing of the survey instrument was
conducted prior to the main survey. Along with expert
judgement, the results from the pilot study were used to
polish the questions asked in the main survey. The pilot
tests were used to evaluate how a sample of people – five
technicians and five farmers – from the same sector
responded to the questionnaire.
(iii) The survey process was carried out between 5 March and
10 April 2013 in two of the three main coffee production
departments in Nicaragua – Estelı and Jinotega. There are
a total of 1624 farmers in these two departments. From the
total population, a sample of 212 farmers was randomly
selected by considering the current distribution of the
farmer population in terms of size, i.e. the same
proportion of small, medium and large farms. We used
a Likert scale for most of the questions. Interviews were
conducted face-to-face on the farms, and took an average
of 35 min to complete. The first 5 min were used to explain
the purpose of the survey research, i.e. that it was non-
commercial and non-political.
Sampleselectedfarmers
Successfulsurveys
% Completed
135 133 95.5
80 79 98.8
215 212 98.6
e n v i r o n m e n t a l s c i e n c e & p o l i c y 4 5 ( 2 0 1 5 ) 5 3 – 6 6 57
2.3. Description of variables
Table 2 describes the variables included in this study, as
well as the descriptive statistics of the data. We have
included reported data about harvested area, labour force,
labour experience, capital availability, water needs, and
some variables analysing climate risk concern for the
212 individual farmers. Descriptive statistics include
the mean and standard deviation for the quantitative
Table 2 – Rationale, description and descriptive statistics of thquantitative variables and frequency of qualitative variables).
Name Rationale Description
Dependent variables
Y1i Adaptive capacity
perceptions
Do farmers perceive that they
to adapt to cope with the po
climate change? (Annex 1, Q14
Y2i Perceptions of
adaptation
government funds
Do farmers perceive that they
support (funding) from the go
with the potential impacts? (A
Y3i Perceptions
of farm adaptation
association funds
Do farmers perceive that they
support (funding) from the far
cope with the potential impact
(Annex 1, Q18). (*)
Y4i Perceptions of
adaptation NGOs
funds
Do farmers perceive that they
support (funding) from the NGO
potential impacts?
(Annex 1, Q19). (*)
Independent variables
Li Farm size, labour force Total workers hired by the farm
Techi Technology used Total number of machines (An
Lexpi Labour experience Workers with experience equa
years (Annex 1, Q7)
Erosioni Erosion risk
perception
Farmers description of potenti
their farm (Annex 1, Q11).
CCriski Long-term impacts
of climate change
awareness
Do farmers think that climate
will affect their farm in the lon
10 years from now?) (Annex 1,
Wi Current water
needs
Farmers description of current
coffee production
(Annex 1, Q15)
Wriski Water scarcity
expectations due
to climate change
Do farmers think climate chan
that is affecting or is going
availability for coffee productio
(*) Measured with the Likert scale.
variables, and the frequency of the qualitative dummy
variables.
The survey data shown in Table 2 indicates that about
31.31% of the farmers show no adaptive capacity at all, while
30.84% show a high capacity perception to adaptation. About
half of the farmers surveyed do not have any perception of
support in terms of the government, farmers’ cooperatives
and NGOs funding for climate change adaptation. The
majority of farmers do not have perceptions of erosion risk
e variables (mean and standard deviation for the
Unit Mean Stddev.
have the capacity
tential impacts of
) (*)
0 = no capacity 31.31
1 = low capacity 16.82
2 = medium capacity 21.03
3 = high capacity 30.84
will have the
vernment to cope
nnex 1, Q17) (*).
0 = no support 53.02
1 = low support 13.02
2 = medium support 13.02
3 = high support 20.93
will have the
ms cooperatives to
s?
0 = no support 66.98
1 = low support 18.60
2 = medium support 12.56
3 = high support 1.86
will have the
s to cope with the
0 = no support 56.74
1 = low support 20.47
2 = medium support 14.42
3 = high support 8.37
. (Annex 1, Q4) Number 12.22 11.00
nex 1, Q8) Number 6.16 6.40
l to or more than 4 1 = Yes 83.72
0 = No 16.28
al erosion risk on 1 = Yes 19.53
0 = No 80.47
change impacts
g term? (More than
Q13)
1 = Yes 29.76
0 = No 70.24
water needs for High 53.02
Medium 19.08
Low 27.90
ge is something
to affect water
n? (Annex 1, Q16)
1 = Yes 73.02
0 = No 26.98
e n v i r o n m e n t a l s c i e n c e & p o l i c y 4 5 ( 2 0 1 5 ) 5 3 – 6 658
due to climate change or long-term impacts of climate change.
However, almost 53% agreed that the current need for
irrigation is high, and moreover, almost 73% believe that
there will be water scarcity due to climate change.
2.4. Econometric model for farmers’ perception estimates
In order to examine the factors that influence the farmers’
perceptions, this study used an ordered probit model (Greene,
2012) as shown in Eq. (1):
Y�i ¼ b0Xi þ ei (1)
where Y�i is a latent measure of climate change adaptive
capacity perceptions; Xi is a vector of factors that influence
the farmers’ perceptions; b is a vector of parameters to be
estimated; and ei is the error term and is assumed to have
standard normal distribution. Since we cannot observe Y�i , we
can only observe the categories of responses as follows:
Y ¼
1 if Y�i � 02 if 0 < Y�i � m1
3 if m1 < Y�i � m2
4 if m2 � Y�i
8>><>>:
(2)
The maximum likelihood technique that provides consis-
tent and asymptotic estimators can be used to jointly estimate
the vector of parameters b and thresholds m. Thresholds m
indicate an array of normal distribution related to the definite
values of the explanatory variables. Parameters b denote the
influence of variation in response variables on the principal
scale. According to Greene (2012), the positive sign of
parameter b implies greater adaptive capacity as the value
of related variable increases. To complete the process of
variable selection, we tested for collinearity problems.
The probabilities of the ordered probit model estimated in
this study are shown below:
PrðYi ¼ 0jXÞ ¼ Fð�b0XiÞPrðYi ¼ 1jXÞ ¼ Fðm1 � b0XiÞ � Fð�b0XiÞPrðYi ¼ 2jXÞ ¼ Fðm2 � b0XiÞ � Fðm1 � b0XiÞPrðYi ¼ 3jXÞ ¼ 1 � Fðm2 � b0XiÞwhere F is the cumulative density function (CDF) of a standard
normal random variable. For all probabilities to be positive, we
must have: 0 < m1 < m2.
The marginal effects of changes in response variables are
obtained once coefficients of the ordered probit model are
estimated:
@PrðYi ¼ 0 XÞj@X
¼ �fðb0XiÞb
@PrðYi ¼ 1 XÞj@X
¼ ½fð�b0XiÞ � fðm1 � b0XiÞ�b
@PrðYi ¼ 2 XÞj@X
¼ ½fðm1 � b0XiÞ � fðm2 � b0XiÞ�b
@PrðYi ¼ 3 XÞj@X
¼ fðm2 � b0XiÞb
2.5. Water needs scenarios
Under climate change, drought events are likely to increase in
frequency, duration and intensity, thereby affecting crop
production.
In the considered departments (Estelı and Jinotega), water
is mainly superficial and unregulated, and is therefore directly
related to run-off, which varies greatly from year to year
(between 1500 and 6000 mm per year). An aqueduct that would
increase access to an improved water supply in the city of
Estelı in the next 25 years is currently being completed with
the aid of EU funds. However, the main priority of this project
is increasing the urban supply to match expected increases in
population over time, and thus the water needed for irrigation
in rural areas will not be affected (WHO and OPS, 2006).
Providing smallholder coffee farmers with access to water is
increasingly difficult due to environmental degradation and
climate change, in addition to distortions in property rights
and the inappropriate use of water resources. The experience
levels of farms are a key factor in improving water manage-
ment in order to achieve optimal use of water resources. The
reason is the role that water plays in the growth and
development of the coffee plant. The experience can be used
for better water management (Carr, 2001).
As a result of the increased scarcity of water supplies, water
management and water policy becomes even more crucial,
and farmers’ awareness of future water risks represent a
driving factor for water management improvement. We do not
analyze climate change scenarios such as run-off here, but we
do explore policy implications in which water risk perception
is set to increase. Information about the consequences of
changes to water allocation for irrigation and changes on
irrigated land is relevant during the decision-making process.
Here we present methods to deal with these alternatives,
including: (i) an increase in coffee production water needs and
(ii) an increase in the water risk concern due to climate change.
We present the results for the different types of perception of
water limitations, as well as what we think is the first
necessary step to discuss the synergy between potential
adaptation and water policies.
3. Results and discussion
3.1. Adaptive capacity perceptions responding to currentwater needs
Table 3 shows the results from the ordered probit model on the
estimation of farmers’ perception of adaptive capacity drivers.
The magnitude and sign of the estimated coefficients do not
have a direct impact in this probit model, but we can say that
an increase in a variable with a positive coefficient increases
the probability of the dependent variable being in the highest
category (high adaptive capacity), yet decreases the probabili-
ty of it being in the lowest category (no adaptive capacity). The
relationship between farmers’ perceptions of adaptive capac-
ity and current water needs is clearly stated. This further
underlines the idea of water instruments being crucial to deal
with climate change adaptation measures. Some other
variables, such as farm characteristics, are also important,
as is awareness of global warming. Table 4 shows the marginal
effects of the estimated determinants affecting the probability
of the farmers’ responses. The marginal effects are calculated
for each outcome by considering the mean of the continuous
variables and the zero value of the dummy variables. This
Table 3 – Ordered probit regression on farmers’ percep-tions of adaptive capacity drivers.
Dependentvariable: Y1i
Coef Std. err
Log(L) 0.2306** 0.1104
Log(Tech) �0.2035** 0.1013
Lexp �0.1530 0.2606
Erosion 0.4878** 0.2606
CCrisk 0.7410*** 0.1719
WH �1.7030*** 0.3121
WM �1.4453*** 0.2852
Wrisk �0.3072 0.1906
Log likelihood �229.82
LR x2(8) 112.03***
* Significant coefficient at 10%.** Significant coefficient at 5%.*** Significant coefficient at 1%.
e n v i r o n m e n t a l s c i e n c e & p o l i c y 4 5 ( 2 0 1 5 ) 5 3 – 6 6 59
marginal effect provides the continuous variables – a measure
of the relative effect that a unit increase in the explanatory
variables will have on the probability of being in either group;
while for the dummy variables they provide a measure of the
effect that belonging to a category has on the probability of
being in either group.
As expected, the estimated models suggest that size
(measured by the logarithm of the number of workers) and
technology (measured by the logarithm of the amount of
machinery) are important factors in explaining farmers’
perceptions of adaptive capacity. They are both statistically
significant at a 1% level, but each has a different sign. This
suggests that as the number of workers on a farm increases
(not in a linear way), the perception of adaptive capacity also
increases. On the other hand, as farms increase their
technology, the perception of adaptive capacity decreases.
Therefore, concentration processes based on increasing the
labour force are likely to favour adaptive capacity in terms of
farmers’ perceptions. However, the greater the level of
technical development, the lower the perception of being
able to adapt to climate change. Since technology is widely
Table 4 – Estimated marginal effects on adaptive capacity per
Mar
Pr(outcome 0) = 0.029 Pr(outcome 1) = 0.061
Coef Std. err Coef Std. er
Log(L) �0.0151 0.0125 �0.0222* 0.0125
Log(Tech) 0.0133 0.0124 0.0196 0.0123
Lexpa 0.0116 0.0186 0.0158 0.0261
Erosiona �0.0202 0.0206 �0.0359 0.0254
CCriska �0.0245 0.0233 �0.0465* 0.0278
WHa 0.3928*** 0.0767 0.1581*** 0.0527
WMa 0.2956*** 0.0870 0.1555*** 0.0440
Wriska 0.0268 0.0255 0.0337 0.0231
a Calculated for discrete change of dummy variable from 0 to 1.* Significant coefficient at 10%.** Significant coefficient at 5%.*** Significant coefficient at 1%.
considered as a necessary factor for coping with climate
change risks (Quiroga and Iglesias, 2009; Smit et al., 2000;
Vedenev et al., 2007), this can be seen as paradoxical result.
In our view, however, two factors can explain this percep-
tion: (i) if the current level of technology is higher, the cost
of improvement is also greater, and farmers will be aware of
this and aware of their inability to achieve this and (ii) even
though the correlation between technology and climate
change awareness is low, it is positive, so the most
technically equipped farms also have more information
about climate change potential impacts, and are thus more
worried about their adaptive capacity. We have tested
alternative specifications of the model, including the farm
size (in hectares), as factors affecting perceptions, but we
did not find a significant effect. This is probably because
there are not enough differences in farm size between the
farmers in the region.
Erosion risk perception due to climate change and the
perception of the long-term impacts of climate change
increase the perception of adaptive capacity in farmers. For
instance, if a farmer has a perception of the risk of erosion due
to climate change, the probability of observing high adaptive
capacity increases by 0.114 when keeping the other variables
constant; whereas having a perception of the long-term
impacts of climate change increases the probability of
observing a high adaptive capacity by 0.198.
Current water needs is a crucial factor for farmers’
perception of adaptive capacity, as seen in Tables 3 and 4.
As suggested by the Wald test (x2(2) = 31.4), we can reject the
hypothesis that parameters are jointly zero (H0:
bWH¼ bWM
¼ 0), i.e. that having different levels of current
water needs does not affect adaptive capacity perceptions. As
these variables present negative signs and are significant in
the regression, we can conclude that when current water
needs are low, farmers are more conscious of their adaptive
capacity. A similar conclusion can be observed for the effect of
water scarcity expectations due to climate change: the more
worried about climate change farmers are, the lesser their
perceptions of adaptive capacity.
ceptions.
ginal effects
Pr(outcome 2) = 0.213 Pr(outcome 3) = 0.697
r Coef Std. err Coef Std. err
�0.0430* 0.0224 0.0803** 0.0376
0.0379* 0.0197 �0.0708* 0.0370
0.0278 0.0489 �0.0552 0.0917
�0.0885** 0.0432 0.1445* 0.0813
�0.1265*** 0.0352 0.1976** 0.0774
0.0295 0.1012 �0.5803*** 0.1127
0.0702 0.1043 �0.5212*** 0.0997
0.0537 0.0363 �0.1142 0.0722
Table 5 – Ordered probit regression on farmers’ perceptions of adaptation funds by source.
Government funds (Y2i) Farms association (Y3i) NGOs (Y4i)
Coef Std. err Coef Std. err Coef Std. err
Log(L) 0.3176*** 0.1166 �0.1684 0.1196 0.1008 0.1114
Log(Tech) 0.0624 0.1023 �0.0561 0.1066 0.0049 0.0998
Lexp �0.9909*** 0.2251 �0.4606** 0.2291 �0.4014* 0.2174
Erosion �0.1236 0.2503 0.3520 0.2469 0.1204 0.2385
CCrisk 0.5676*** 0.1947 0.2106 0.2021 0.2801 0.1882
WH 1.2191*** 0.2839 1.3760*** 0.2953 1.3315*** 0.2744
WM 1.4880*** 0.2506 1.0613*** 0.2680 1.0627*** 0.2427
Wrisk �0.5103** 0.2001 �0.0523 0.2052 �0.2101 0.1922
Log likelihood �214.13 �178.47 �223.67
LR x2(8) 82.89*** 35.34*** 37.59***
* Significant coefficient at 10%.** Significant coefficient at 5%.*** Significant coefficient at 1%.
Fig. 3 – Cumulative probability distribution of expected
adaptation support from the government, farmers’
cooperatives and NGOs.
e n v i r o n m e n t a l s c i e n c e & p o l i c y 4 5 ( 2 0 1 5 ) 5 3 – 6 660
3.2. Factors affecting adaptation policy expectations interms of funding sources
Table 5 shows the results of the estimated ordered probit
models used to explain the influence of water needs on
adaptation funds expectations when responding to a wide
range of variables. The government, farmers associations and
NGOs’ cooperation with climate change adaptation is ana-
lyzed in terms of farmers’ perceptions, and can largely be
explained by current water needs. Labour experience is also an
important factor determining public support. As outlined in
our methodological approach, we tested the adequacy of the
functions to represent farmers’ perceptions.
Fig. 3 shows the estimated cumulative probability distri-
bution of expected adaptation support from the government,
farmers’ cooperatives and NGOs. We can see that, despite the
differences, farmers are generally more confident about
government funding than about other sources, since there
is a greater probability of expected public support. We can
observe in Fig. 3 that the predicted probabilities for farmers’
cooperatives and NGOs for the low, medium and high support
categories tend to be less than 0.25. NGOs aim to influence
social responsibility and market capitalism (Linton, 2005) and
coffee cooperatives seek to develop exports and coffee
processing services aimed at differentiated markets, but
coffee farmers do not perceive that there will be funds
specifically allocated to support climate change adaptation.
The high expectation for governmental support can be
explained by the government’s promotion of policies aimed
at increasing smallholders’ access to credits in recent years
(2007–2011). The Banco de Fomento – a state institution that
manages farmers’ credits – has increased Rural Development
Credit Funds in the last few years, which constitutes an
important financial support to smallholders (i.e. in 2010, credit
concessions to smallholder coffee farmers grew 1.6 times
more than in earlier periods) (PNDH, 2012). As well, the Social
Safety Net programme was designed to cash transfers aimed
at households living in extreme poverty in rural areas
(Maluccio, 2005).
Table 6 shows the estimated marginal effects of govern-
ment funding expectations. We can see that the bigger the
farm size, the lower the expectation of perceiving government
support. Technology is also negatively affected. Farmers more
concerned about climate change risks and who have high and
medium current water needs are less confident that public
policies or funds will sustain climate change adaptation.
Farm size (measured by the logarithm of number of
workers) is an important factor in adaptation to government
funding perception (but not in the case of the other funding
Table 6 – Estimated marginal effects of adaptation to government funding perception.
Marginal effects
Pr(outcome 0) = 0.516 Pr(outcome 1) = 0.175 Pr(outcome 2) = 0.154 Pr(outcome 3) = 0.155
Coef Std. err Coef Std. err Coef Std. err Coef Std. err
Log(L) �0.1266*** 0.0465 0.0148 0.0173 0.0362** 0.0165 0.0756** 0.0368
Log(Tech) �0.0249 0.0408 0.0029 0.0054 0.0071 0.0116 0.0148 0.0254
Lexp 0.3327*** 0.0920 �0.0922*** 0.0274 �0.1080*** 0.0318 �0.1324** 0.0663
Erosiona 0.0490 0.0991 �0.0069 0.0152 �0.0145 0.0295 �0.0276 0.0564
CCriska �0.2171*** 0.0732 �0.0013 0.0301 0.0463 0.0288 0.1721** 0.0706
WHa �0.3968*** 0.1065 �0.0586 0.0417 0.0298 0.0525 0.4257*** 0.0906
WMa �0.4422*** 0.1056 �0.0877** 0.0435 0.0032 0.0602 0.5267*** 0.0738
Wriska 0.1930** 0.0766 �0.0406* 0.0235 �0.0610** 0.0256 �0.0914* 0.0506
* Significant coefficient at 10%.** Significant coefficient at 5%.*** Significant coefficient at 1%.a Calculated for discrete change of dummy variable from 0 to 1.
e n v i r o n m e n t a l s c i e n c e & p o l i c y 4 5 ( 2 0 1 5 ) 5 3 – 6 6 61
sources). It is positive and statistically significant at a 1% level.
This suggests that as the number of workers on a farm
increases (not in a linear way), their perception of adaptation
to government funding also increases. As a result, when farms
are bigger in terms of labour force, they are more confident
about government support. This is surprising given that the
government’s rural development policy is now primarily
focused on small producers. Therefore, the more the labour
experience (Lexp), the less the perception on counting on
funding for adaptation support. Current water needs are also
important in determining farmers’ perception of adaptation
funds expectation (see Tables 5 and 6). We can reject the
hypothesis that the parameters are jointly zero (H0:
Table 7 – Potential benefits of adaptation measures, by instru
Instruments Adaptation measures Potential fundingsources
Local capacity
building
Shadow crop planting; fight
crop diseases; changes in
harvesting dates and
processing; water storage
Farms
Government
Cooperatives
NGOs
Financial
transfer tools
Crop insurance programmes Government
Cooperatives
Financial
transfer tools
Micro-credit programmes NGOs
Government
Cooperatives
Financial
transfer tools
Crop rotation Farmers
Alternative
coffee markets
Introduction of organic
and Fair
Trade coffees
NGOs
Cooperatives
Farmers
Alternative
coffee markets
Eco-labels Government
Alternative
coffee markets
High quality gourmet
coffee and sustainability
Cooperatives
Farmers (for
example through
Specialty Coffee
Association of
America)
Changes in
agro-climatic
zones
Migration to southern
areas
Farmers
bWH¼ bWM
¼ 0) in the three specifications, as suggested by
Wald tests (x2(2) = 35.5 for the government funds model,
x2(2) = 22.6 for the farm association funds model and
x2(2) = 25.8 for the NGOs funds model). As these variables
present positive signs and are significant in the estimation, we
can conclude that when current water needs are higher,
farmers are more convinced about funds expectations. For
instance, if farmers have high current water needs the
probability that they will perceive more support from the
government increases by 0.426 when keeping the other
variables constant.
Our results suggest that bigger farms with higher current
water needs are those perceiving more potential for public
ment and type of support requirements.
Supportrequirements
Adaptation potentials
Public
Private
Biodiversity conservation, hurricane
protection, productivity increase,
avoid losses produced by infections,
reduce water pressures
Private
Public
Reduction of income vulnerability
Private
Public
To cope with extreme events and
natural disasters in the short term
Autonomous Diversification of incomes to diminish
losses associated with climate risk
and coffee markets’ volatility
Private
Autonomous
Development of markets and reduce
price volatility
Public Reduce biodiversity losses and land erosion
Private
Autonomous
Promote the orientation to high quality
coffee to reduce vulnerability through
a more established demand
Autonomous Avoid the productivity losses by
changing to more suitable areas
e n v i r o n m e n t a l s c i e n c e & p o l i c y 4 5 ( 2 0 1 5 ) 5 3 – 6 662
support. Support from farmers associations or cooperatives
and NGOs is really low in general. Table 7 summarizes the
potential benefits of adaptation for coffee production in the
region and its classification by type of support requirements.
Since the more confident the farmers are about the source of
funding the more acceptable the measures will be, we can
conclude that the measures including public or autonomous
adaptation is more likely to be successful in Nicaragua. Private
support should make an effort to involve stakeholders’ needs.
3.3. Current water needs and water risk perceptionscenarios
In this section, we simulate some potential responses to
structural adjustments in terms of current water needs and
water risk perception scenarios. Farmers’ responses to
changes in labour force (size) and technology were simulated
for the different levels of current water needs and water risk
perception. Figs. 4 and 5 plot the predicted probabilities for
each outcome and the different current water needs against
labour force and technology, respectively, for the two
scenarios of water risk perceptions. The graphs for low
current water needs indicate the maximum probability for
high adaptive capacity perspectives. We can observe how this
probability increases as the labour force increases, but then
decreases with technology. When the current water needs are
medium or high, then the maximum probability is a feeling of
not having any adaptive capacity, which decreases as the
0.000.100.200.300.400.500.600.700.800.901.00
0 10 20 30 40 50 60 70
Prob
abili
ty
Labou r force
0.000.100.200.300.400.500.600.700.800.901.00
0 10 20 30
Prob
abili
ty
Labou r for
0.000.100.200.300.400.500.600.700.800.901.00
0 10 20 30 40 50 60 70
Prob
abili
ty
Labou r force
0.000.100.200.300.400.500.600.700.800.901.00
0 10 20 30
Prob
abili
ty
Labou r for
No
Pr( L
(a) Farmers witho ut water risk percep�on
Low water needs
Low water needs
Med wate
Med wate
(b) Farmers with water risk percep�on
Pr( No capacity) Pr( Medium capacity)
Fig. 4 – Probability sensitivity to labour force variations for differ
(0, 1).
labour force increases but increases with technology. Being
aware of water risks reduces the probability of feeling a high
capacity to adapt in every scenario. It can be noted that for
farmers with current high water needs, sensitivity to size and
technology is low. This means that their low expectations as to
their adaptation capacity vary slightly when the structural
factors alter; they are more concerned about the climate risks.
As we have mentioned, Nicaragua is a country with high
vulnerability to global change: it is susceptible to risks and
dangers relating to people, biodiversity and natural resources,
which have irreversible long-term effects. By aiming to
improve land use distribution in a sustainable way, regional
and local governments have implemented general policies
relating to environmental conservation, linked to rural
development. From 2007, public policies have been more
oriented to the Millennium goals (UN, 2013), and more focused
on smallholders and the poorest families. In the Human
Development Plan 2012–2016, which is still under public
consultation, priority has been given to labour increases and
the reduction of inequality (PNDH, 2012; UNDP, 2013). One of
the strategies mentioned is to assist the exchange of property
rights in rural areas in order to facilitate rural workers’ access
to their own property (in this case, small farms). This policy
goal indicates that future perspectives are concerned more
with the reduction of farm size at the national level. On the
other hand, the Plan mentions that public policies intend to
strengthen productive capacities in terms of education
and technology in the 26 most vulnerable municipalities
40 50 60 70
ce
0.000.100.200.300.400.500.600.700.800.901.00
0 10 20 30 40 50 60 70
Prob
abili
ty
Labou r force
40 50 60 70
ce
ow
0.000.100.200.300.400.500.600.700.800.901.00
0 10 20 30 40 50 60 70
Prob
abili
ty
Labou r force
r needs
r needs
High water needs
High water needs
Pr( Low capacity)Pr( High capacity)
ent water needs (low, med, high) and water risk perception
0.000.100.200.300.400.500.600.700.800.901.00
0 10 20 30 40
Prob
abili
ty
Techno log y
0.000.100.200.300.400.500.600.700.800.901.00
0 10 20 30 40
Prob
abili
ty
Techno log y
Pr( No capacity)Pr( Low
0.000.100.200.300.400.500.600.700.800.901.00
0 10 20 30 40
Prob
abili
ty
Techno log y
0.000.100.200.300.400.500.600.700.800.901.00
0 10 20 30 40
Prob
abili
ty
Techno log y
0.000.100.200.300.400.500.600.700.800.901.00
0 10 20 30 40
Prob
abili
ty
Techno log y
0.000.100.200.300.400.500.600.700.800.901.00
0 10 20 30 40
Prob
abili
ty
Techno log y
(a) Farmers witho ut water risk percep�on
Low water needs
Low
Med water needs
Med water needswater needs
High water needs
High water needs
(b) Farmers with water risk percep�on
Pr( No capacity) Pr( Low capacity)Pr( Medium capacity) Pr( High capacity)
Fig. 5 – Probability sensitivity to technology variations for different water needs (low, med, high) and water risk perception
(0, 1).
e n v i r o n m e n t a l s c i e n c e & p o l i c y 4 5 ( 2 0 1 5 ) 5 3 – 6 6 63
(Estelı and Jinotega among them) within 20 years. As such, the
technological framework is likely to increase. Considering this
context in which we expect farm size to be reduced and the
introduction of technology to be more generalized, our model
predicts that adaptive capacity perceptions among coffee
smallholders are likely to be reduced in the near future.
Since the consequences of climate change are expected to be
very important for Nicaragua, an awareness of adaptive capacity
flaws is essential to develop the National Adaptation Plan
Strategies (MAGFOR, 2013). The national adaptation plan for the
agricultural sector was developed with the collaboration of
coffee, sugar and agro-ecological farmers, with the aim of
protecting soils from erosion and degradation, conserving water
andnatural habitats andreducingemission of greenhouse gases;
it also envisaged different measures for the adaptation and
mitigation of climate change. The plan intends to endorse
technical assistance to help poor smallholder producers to
adapt, strengthen public institutions and policies, and improve
weather information systems (CCAFS, 2014). The current
strategy 2010–2015 is based on the following five pillars: (i) to
recognize the traditional knowledge and good practices from
rural producers, (ii) to consider the ecosystem based approach as
part of the climate change adaptation strategy, (iii) to consider
the synergies among mitigation and adaptation (iv) to promote
the inclusion and participation of stakeholders, strengthening
the existing associations and cooperatives, and (v) to prioritize
coffee production among other agricultural sectors and focus on
the areas with more food insecurity. This plan is based on the
community and the associative model and the efforts are
oriented to food security and environmental values, encouraging
the traditional and ancient cultures oriented to more sustainable
production (MARENA, 2010; OJLG, 2011, 2012; MAGFOR, 2009).
Some of the actions are difficult to implement, so the
involvement of stakeholders is essential. They are aware of
the difficulties in supporting the adaptation actions. As
mentioned in Milan (2010), it is essential for Nicaragua to
increase its population’s awareness of climate change vulnera-
bility so everyone may assist in the decision-making process.
Since the simulations vary according to current water
needs and water risk scenarios (Fig. 5), our analysis might
suggest the desirability of a greater orientation of adaptation
policies towards water instruments such as promoting small-
scale water capture and storage systems that can help farmers
to improve the ability to store and manage water for
agriculture. For example, the Latin American Fund for
Irrigated Rice (FLAR) and the International Centre for Tropical
Agriculture (CIAT) have a successful 14 micro-dam pilot
project in Nicaragua (Gourdji et al., 2014).
4. Conclusions
In this paper, we have focused on an evaluation of adaptive
capacity perceptions and potential sources of funding for this.
e n v i r o n m e n t a l s c i e n c e & p o l i c y 4 5 ( 2 0 1 5 ) 5 3 – 6 664
We link socio-economic factors and risk awareness as the
main drivers of adaptive capacity perceptions. The results
provide information about current perspectives on funding
capacity for coffee producers, which will be relevant to their
current investments and capacity building. The models were
used to calculate the cumulated probability in terms of labour
force and technology by taking into account the current water
needs. Water risk awareness emerges as a key factor for
greater concern on the part of farmers with regard to their
adaptive capacity; as such, an effort to provide more
information on water risks can be a determinant of small-
holders’ acceptance of adaptation measures.
Other structural and policy factors, such as farm size and
technological development, seem to have significant impor-
tance as well. The Nicaraguan context seems to suggest a
trend for reductions in farm size and an increase in
technology. Our model simulations suggest that adaptive
capacity perceptions among coffee smallholders are likely
to be reduced in this scenario, since when farm size is
reduced and technology increases, the probability of having
low adaptive capacity perceptions is increased and the
probability of having high adaptive capacity perceptions is
reduced. In this context, adaptation plans should include
more efforts in educational programmes to instruct the
Appendix A. Questionnaire
Questions in relation to support for adopting climate change
I. GENERAL INFORMATION ABOUT THE FARM
1. Department
2. Municipality
3. Exploitation area (Ha):
4. How many people were working on this farm
during this period (2011–2012)?
5. Can you divide them in terms of gender?
(Indicate the total number of males and females):
6. Can you classify them in terms of type of
contract (fixed-term versus permanent)?
(Indicate the total number of fixed-term contracts
and the total number of permanent contracts).
7. Please indicate the total number of workers in
this farm that have more than four years experience.
8. Indicate the total number of machines that are
available for use on this farm.
9. Does this farm receive funding from: Bank
Farm
NGO
10. The investment in this farm in the last five
years could be defined as:
High
Med
Low
11. How would you describe the erosion risk
on this farm?
Yes,
a sig
No,
have
population about their own adaptation potential. Since
farmers have shown that they are not confident about
receiving external support and on the other hand their
perception of potential autonomous adaptation is going to
be lower, the country could suffer a substantial rural land
abandonment, or massive migration to higher areas instead
of implementing other adaptation measures in their farms
which would require greater confidence in external support
and their own possibilities.
Our analysis might suggest the desirability of stronger
orientation of adaptation policies towards water instruments,
as also suggested in former recent studies on Nicaragua (see
case studies confronting water scarcity in Gourdji et al., 2014).
This is important because water management is a very
important strategy among the adaptation measures proposed
in the national adaptation plan for the agricultural sector
(MAGFOR, 2013).
Acknowledgements
This research work was supported by the AECID (Spanish
Agency for International Development Cooperation) as part of
a local development programme for Leon, Nicaragua.
adaptation measures (N = 212).
Answers (weighting value)
_____________
_____________
Indicates the total number
Indicates the total number
Males
Females
Fixed term contracts
Permanent contracts
Indicates the total number
Indicates the total number
s: Yes
No
s co-ops Yes
No
s Yes
No
ium
I think this farm have
nificant risk of erosion.
I do not think this farm
a significant risk of erosion.
II. PERCEPTIONS ON CLIMATE CHANGE IMPACTS AND ADAPTIVE CAPACITY
12. How worried are you about global warming? Very worried
Somewhat worried
Not very worried
Not at all worried
13. Do you think that climate change impacts will affect this farm
in the future?
In the short term (less than 10 years from now)
In the long term (more than 10 years from now)
14. Assuming climate change is happening, Do you perceive that
you have the capacity to adapt to cope with the potential impacts
of climate change?
No capacity to adapt
Low capacity to adapt
Medium capacity to adapt
High capacity to adapt
15. How would you describe your current water needs for the coffee
production?
High
Medium
Low
16. Do you think climate change is something that is affecting or
is going to affect water availability for coffee production?
Yes, I think water availability is going to be reduced
No, I think water availability is not going to be affected.
17. Assuming climate change is happening, do you think
you will have the support (funding) from the government
to cope with the potential impacts?
No support
Low support
Medium support
High support
18. Assuming climate change is happening, do you think you will
have the support (funding) from farmer cooperatives to cope with
the potential impacts?
No support
Low support
Medium support
High support
19. Assuming climate change is happening, do you think you will
have support (funding) from NGOs (non-governmental
organizations) to cope with the potential impacts?
No support
Low support
Medium support
High support
e n v i r o n m e n t a l s c i e n c e & p o l i c y 4 5 ( 2 0 1 5 ) 5 3 – 6 6 65
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