En el 2014 Ximena Rueda, Nancy E. Thomas y Eric F. Lambin se basaron en imágenes de satélite para examinar la densidad de la cobertura forestal en la región cafetalera de Santander, Colombia en 2009, para luego compararlo con imágenes del 2013. Con esta comparación los investigadores encontraron que las fincas de café certificadas no solo tenían mayor cobertura arbórea y mayor variedad de especies de árboles que las no certificadas, sino también que las cuencas dominadas por fincas cafetaleras certificadas tenían áreas forestales más grandes y una mejor conectividad entre parches de bosque que en aquellas dominadas por fincas no certificadas.

1. ORIGINAL ARTICLE
Eco-certification and coffee cultivation enhance tree cover
and forest connectivity in the Colombian coffee landscapes
Ximena Rueda • Nancy E. Thomas •
Eric F. Lambin
Received: 29 April 2013 / Accepted: 8 March 2014
Ó Springer-Verlag Berlin Heidelberg 2014
Abstract Eco-certification of agricultural commodities
offers an appealing option to promote more sustainable
practices among smallholders, increase agricultural value,
and lift farmers out of poverty through better market
access. This study evaluates whether coffee cultivation is
associated with changes in forest cover and forest frag-
mentation and whether the Rainforest Alliance eco-certi-
fication program has led to enhanced tree cover and greater
landscape connectivity in the Colombian eastern Andes.
Based on satellite imagery, geo-referenced coffee parcels
and a pair–case comparison, we show that coffee-growing
regions have larger areas in forest, larger forest patches,
and better connectivity among patches than non-coffee
areas. These differences, however, do not seem to be
accentuated over time, except for dense forest cover in the
coffee-growing region. The latter has increased since the
introduction of a certification program that requires pro-
tection of forest remnants and riparian vegetation. More-
over, certified farms in the study area have increased the
amount of tree cover on their plots significantly more than
non-certified ones. Our study design, therefore, detects
additionality in the impact of certification on tree cover
increase: in a region with overall increase in tree cover,
certified farms contributed significantly more to that trend
than non-certified farms. This study presents the first
evaluation of the impacts of certification in cultivated
landscapes at the ecosystem level, detectable by Earth
observation satellites.
Keywords Eco-certification Á Impacts Á Coffee Á
Colombia Á Agroforestry Á Remote sensing
Introduction
Cropland continues to expand in tropical regions to satisfy
increasing demands for food, fibers, and fuels. Much of this
land comes from forests and other natural ecosystems,
severely affecting their functioning (Gibbs et al. 2010).
Confronting a looming land scarcity, the debate on how to
promote nature conservation focuses on what are the most
effective policies to advance land use sustainability
(Lambin and Meyfroidt 2011). Policies in tropical areas
dominated by smallholder agriculture include the support
of on-farm practices more benign to natural ecosystems
through low-intensity, biologically diverse farming sys-
tems that maintain semi-natural habitats in a matrix of
farmed landscapes (Michon et al. 2007). Such multifunc-
tional landscapes, which include agro-forestry systems and
secondary successions, maintain conservation value (Hecht
2010; Mendenhall et al. 2011) while also ensuring the
livelihood of rural communities.
In parallel, businesses and consumers increasingly
embrace corporate sustainable sourcing strategies of
Editor: Wolfgang Cramer.
X. Rueda (&)
School of Management, Universidad de los Andes, Bogota´,
Colombia
e-mail: x.rueda@uniandes.edu.co
N. E. Thomas
Spatial Analysis Center, School of Earth Sciences, Stanford
University, Stanford, USA
E. F. Lambin
School of Earth Sciences and Woods Institute for the
Environment, Stanford University, Stanford, USA
E. F. Lambin
University of Louvain, Louvain, Belgium
123
Reg Environ Change
DOI 10.1007/s10113-014-0607-y

2. agricultural and forest products. Market-based mechanisms
are often cited as effective and efficient interventions to
address conservation dilemmas in human-dominated land-
scapes (Wunder et al. 2008). They vary from direct
investments in projects for farming communities, to par-
ticipation in multistakeholder commodity roundtables to
improve production practices, to strict eco-certification
programs in which suppliers are encouraged to adopt a
code of conduct that ensures sustainable practices. In eco-
certification, a third-party audits compliance; farmers
sometimes receive a price premium and access to market
shares; and corporations may stamp a seal on their pro-
ducts, identifying them as sustainable. Although demand-
ing in the field, eco-certification is attractive because: (1) it
uses existing markets to steer consumers’ choices by cre-
ating additional value to consumers who are willing to pay
more for products they already demand; (2) it engages
farmers in improving their production practices to incor-
porate sustainability considerations that may result in more
efficient farm operations; and (3) if given and properly
transferred to producers, price premiums can motivate
farmers to transform land use and may contribute to alle-
viate poverty through a better integration in the global
market. Certification thus promises to achieve a triple-win
solution for business growth, rural development, and
environmental conservation.
Farm surveys have attempted to evaluate the socio-
economic and plot-level biodiversity impacts of eco-certi-
fication (STAP 2010; van Kuijk et al. 2009; Mas and Di-
etsch 2004; Perfecto et al. 2003; Beuchelt and Zeller 2011),
but much uncertainty remains on the potential to scale up
local benefits at the ecosystem level. Broad-scale impacts
are difficult to detect: farms are small, in patchy and
fragmented landscapes, and certified farms are frequently
spatially disconnected. Attempts at studying forest level
impacts of certification have focused on wild coffee in
Ethiopia, the center of origin of the species (Takahashi and
Todo 2014). Studies on patchy, cultivated landscapes are
still lacking. In spite of the challenges, demonstrating
regional-scale impacts of certification is critical, if certifi-
cation is to become a catalytic force for the adoption of
sustainable land use practices. The ability of ecosystems to
deliver valuable services depends on the synchronized
actions of many individuals: cross-pollination, water
cycling, and climate regulation cannot be protected at the
farm level and require a regional effort. Remote sensing
offers a convenient data source to conduct such impact
evaluation over large areas as it allows monitoring proxy
variables such as tree cover and landscape pattern metrics.
The objective of this study was to evaluate: (1) the
association between coffee cultivation and tree cover
changes and fragmentation and (2) the effectiveness of eco-
certification programs to promote agricultural practices that
lead to enhanced tree cover and greater landscape connec-
tivity in agro-forestry systems. We use the case of coffee
production in Colombia, the largest producer of mild
washed Arabica coffee. The study focuses on the Rainforest
Alliance certification program, which is the most prevalent
in the region, and with the sternest requirements for biodi-
versity conservation and forest protection. To the best of our
knowledge, this study presents the first ecosystem-level
evaluation of the impacts of certification that relies on
satellite imagery and geo-referenced plot information.
Methods and materials
Study site
This research takes the case of the Colombian coffee
landscape in the heart of the tropical Andes. Great varia-
tions in Andean climate and topography gave rise to a
diverse mosaic of ecosystems and plant communities,
making it a big component of the country’s mega-diversity
(Herzog et al. 2011). The Colombian coffee landscape
occupies 3.3 million hectares in three Andean mountain
ranges and in the Sierra Nevada de Santa Marta, on the
Caribbean coast. In Colombia, coffee cultivation expanded
in the nineteenth century. Today, coffee is the main source
of rural employment; more than 500,000 families grow the
crop.
Recent work has shown significant recovery of woody
vegetation in the country. Mountain forest eco-regions
(where coffee is grown) are responsible for two thirds of
that recovery (Sa´nchez-Cuervo et al. 2012). Coffee
expansion has occurred in tandem with this forest recovery,
at least for those parts of the country with adequate con-
ditions to supply to differentiated markets (Rueda and
Lambin 2013a, b; Guhl 2009). The study is located on the
Santander province, one of the oldest coffee-growing
regions in the country, representing about 5 % of Colom-
bia’s coffee production. Specifically, the study focuses on
the upper Sua´rez River watershed, a 172-km-long water-
way in the eastern Andes covering the municipalities of
Socorro, San Gil, and Pinchote (Fig. 1). The study covers
an area of 33,000 hectares, 6,500 of them in coffee. Coffee
is grown from 1,400 to 2,000 MASL. About 39 % of coffee
farms in Colombia cultivate their coffee under shade (FNC
2010), particularly in the Eastern and Northern regions
where biophysical and cultural conditions interact to favor
this type of cultivation. Santander, on the Eastern Andes,
has a 9-month dry season which determines that most of
the coffee here is cultivated under shade. Shade cultivation
facilitated the adoption of certification programs that pro-
mote the use of organic fertilizers and biodiversity culti-
vation. Organic litter from shading trees reduces the
X. Rueda et al.
123

3. dependence on external inputs for fertilization, while shade
trees are favored by some certification, such as bird
friendly and Rainforest Alliance (RFA) for its potential to
support greater biodiversity than sun exposed plantations.
Because of this condition, Santander was the first region to
adopt eco-certification programs, around 2002, as an effort
to differentiate its coffee in the global market. In the study
site, there are more than 2,000 coffee growers, 23 % of
them certified. Ninety percent of the certification in Sant-
ander occurs under the RFA certification program (FNC
2010), which is considered by technicians in the field, as
the most demanding and strict system (Henry Parra, pers.
com.). The other 10 % corresponds to a group of FLO-
Organic certified farmers who are not in the study area.
Once compliant with this program, the local committee of
the Colombian Coffee Growers Federation (FNC for its
acronym in Spanish) has included farmers in other certifi-
cation programs (such as UTZ certified, organic, and FLO),
that are less stringent—in their environmental criteria—
than the RFA certification program (see Rueda and Lambin
2013a for a discussion of all certification programs and
corresponding criteria active in Colombia).
The study focuses on the impacts of Rainforest Alliance
certification program. Rainforest Alliance (RFA) is an
environmental NGO that uses market-based mechanisms to
preserve biodiversity while enhancing people’s livelihood,
linking business to environmentally minded consumers
through the RFA-certifiedTM
seal. The seal was first used
for timber in the late 1980s and later expanded to bananas
and coffee. In 2004, coffee certification went mainstream
with Procter and Gamble and Kraft foods launching RFA-
certified products for particular markets (RFA 2012).
Today, more than 300,000 metric tons of RFA-certified
coffee are produced annually around the world; about half
of them are traded internationally (RFA 2011), represent-
ing close to 2 % of the global market (USDA 2011). The
RFA certification is granted to individual farms or groups
of farms (which can be formally organized in cooperatives
or loosely associated just for the purpose of certification)
that comply with a comprehensive standard including
environmental, social, and economic criteria. The guiding
principles of the standard are as follows: the implementa-
tion of a management system for the farm, ecosystem
conservation (e.g., through the protection of forest rem-
nants and riparian vegetation), wildlife protection, water
conservation, acceptable working conditions, occupational
health, community relations, integrated crop management,
soil conservation, and integrated waste management. Cof-
fee bearing the RFA-certified seal has historically com-
manded the largest premiums among certification
programs, without compromising the farms’ productivity
(Rueda and Lambin 2013a).
Data and methods
We tested whether coffee certification had an additional
impact on tree cover increase compared with the trend in
tree cover increase for the entire study region, and in
particular in the area under coffee production. Detecting
land use change in mountainous, fragmented agricultural
Fig. 1 Map of Colombia and
study area
Eco-certification and coffee cultivation
123

4. landscapes poses numerous challenges given spectrally
complex thematic classes. Our approach extracted fine-
scale tree canopy information from 30-m resolution
Landsat imagery and ancillary data by using a suite of
spectral, topographic, and texture variables. We selected
the best available Landsat TM and ETM? images for the
date before certification started (January 2003) and the one
closest to the most recent socio-economic data (December
2009). These images were calibrated and converted to
surface reflectance using the Landsat Ecosystem Distur-
bance Adaptive Processing System (LEDAPS) tool (Masek
et al. 2006). A set of variables designed to predict percent
tree canopy cover was calculated for each image: surface
reflectance values for each spectral band, vegetation indi-
ces—normalized difference vegetation index (NDVI),
normalized difference moisture index (NDMI), and forest
index (FI) (Huang et al. 2010)—, topographic variables
from the Shuttle Radar Topography Mission (SRTM), and
pixel-based and object-based textures. Pixel-level image-
texture measures were calculated on the reflectance values
and vegetation indices through ENVI’s grey-level co-
occurrence matrix (GLCM) (Haralick and Shanmugam
1973). Object-level GLCM textures were calculated in
eCognition (Trimble 2011).
Referencedata weregeneratedusinghigh resolution (1 m)
aerial imagery collected in 2010. An initial land cover map
was created for the 2009 image using unsupervised classifi-
cation. Spectral clusters were labeled into major land cover
classes (water, agriculture, urban, pasture, forest, shrub, bare
ground, cloud/cloud shadow). Reference data points were
generated as a stratified random sample of this land cover
map, and the selected point locations were intersected with a
fishnet of grid cells corresponding to the pixel boundaries of
the 2009 image. These 30-by-30 m cells were overlaid on the
digital aerial photography to visually estimate and record
percent tree canopy cover for each of the selected cells.
We applied random forests, which is an ensemble
regression tree analysis, to map percent tree canopy cover
(Breiman 2001). The RF classifier generates a large num-
ber (500 in this case) of decision trees and predictions are
made by a voting process among the model runs. RF is
nonparametric, can efficiently handle large variable data-
sets, includes an out-of-bag (OOB) error estimate and is
robust against co-linearity in the predictor variables and
against over fitting (Breiman 2001). We applied RF
through the R statistical package ModelMap (Freeman and
Frescino 2009) with percent forest cover at the 30-m cell
level as the dependent variable. We ran RF multiple times
while varying input predictors to identify the strongest
ones. Percent tree canopy cover was estimated with a R2
of
0.69 at both dates. The best models for both time periods
were used to create percent tree canopy cover maps for
2009 and 2003 using MapMaker.
We then assessed the change in tree canopy cover over
time by subtracting the 2003 map from the 2009 map to
create a difference in tree canopy cover map. We deter-
mined a change threshold to minimize confusion due to
noise or residual error, particularly in low forest cover
landscapes. Finally, we analyzed changes in landscape
patterns to assess how coffee expansion impacted forest
fragmentation and habitat connectivity in the region.
Thresholds of 40 and 70 % tree cover were applied to the
percent tree canopy cover maps from 2003 and 2009. The
RFA standard requires that coffee plots have a tree cover of
40–70 %, so coffee certification was expected to lead to an
increase in areas with [40 % tree cover. Coffee cannot
grow under a tree cover larger than 70 % so that threshold
isolates dense, closed-canopy forests with a high biodi-
versity value from shade-grown coffee plantations. Canopy
cover below 40 % represents sparse tree cover or individ-
ual trees in mono-crops, recently planted coffee areas or
pastures. Commonly used landscape pattern indices were
generated using Fragstats version 4.1. We calculated mean
forest patch size, which is inversely related to forest cover
fragmentation, and the simple Euclidean nearest neighbor
distance from a forest cover patch to the nearest neigh-
boring forest patch, a measure of patch isolation (McGa-
rigal et al. 2012).
Plot-level data came from FNC. FNC has a geographic
information database of all coffee farms in the country that
includes farm size, coffee plot area, and exact location of
each coffee field. Extension agents update the database
periodically. We used the data from December 31, 2010.
For each farm in our sample, we defined the coffee field as
a circle around the GPS point provided by FNC (the cen-
troid of the coffee field) such that the area of the circle is
equal to the area of the field (which also comes from FNC
data). Individual fields from each farm were then joined
into one zone including contiguous and non-contiguous
fields and overlaid on the map representing change in tree
canopy cover (Fig. 2). This method includes some land-
scape elements bordering fields in the analysis. A zonal
summary function was applied to determine the area of
pixels within each farm falling within the categories of no
tree cover change, tree cover increase, tree cover decrease,
and no data (cloud cover).
The study area was divided into coffee-growing and
non-coffee-growing regions based on a kernel density
surface derived from FNC’s database of coffee farm
locations. This kernel calculated the density of coffee
farms at a 30-m resolution. We then identified a density
threshold through visual analysis to map the coffee-grow-
ing region. All statistics on tree cover, landscape patterns,
and changes in these variables were computed for the entire
study area, and for the coffee- and non-coffee-growing
regions.
X. Rueda et al.
123

5. At the farm level, we used a pair matched case–control
method to compare pairs of like farms whose only obser-
vable difference was whether or not they participated in
certification schemes. This method produces a robust
counterfactual to evaluate the impacts of certification
(Blackman and Naranjo 2010).
All certified farms for which at least 50 % of the coffee
plots were cloud free on the Landsat images were chosen.
This rendered a total of 237 certified farms, 46 % of all
certified farms in the study area. We then selected the
closest non-certified farm of similar size to each certified
one to ensure that pairs of farms shared similar biophysical
and accessibility attributes. The certified farms all joined
certification either individually or as part of informal
farmers associations formed only for certification. The
distribution of farms by size was highly skewed toward
smallholders: 95 % of the farms in the study area had
3 ha in coffee (FNC 2010). We therefore used the Wil-
coxon signed-rank test to evaluate whether the amount of
tree cover change was significantly different between cer-
tified and non-certified farms. This test compares pair
samples to evaluate whether the corresponding distribu-
tions of attributes are identical, while relaxing the nor-
mality assumption. Results from our previous study in the
same area (Rueda and Lambin 2013b), interviewing a sub
set of 86 paired-households showed us that location and
farm size did control effectively for covariates. In that
study, which did not include an ecosystem-level evaluation
of tree cover changes based on remote sensing data, we
analyzed other household characteristics, such as age of the
household head, years of education of the household head,
type of tenure, and access to markets. We found that there
was no statistically significant difference between certified
and non-certified farmers on these variables, which makes
us confident that by controlling for farm size and distance,
we are able to produce an unbiased control group to
compare certified and non-certified farmers. The pair
matching thus successfully avoided self-selection effects
whereby producers with certain attributes that make them
more likely to be certified would have selected preferen-
tially into the certification program.
Results
Over the entire study region with cloud-free data, tree
canopy cover has experienced a net increase of 1,294 ha
(6 % of the area) (Table 1). Most of this increase (850 ha)
Fig. 2 Results of the remote sensing analysis for a sub region. The
buffered field polygon layer representing our sample of farms is
shown in yellow and overlaid on Landsat imagery from 2003 (a) and
2009 (b), and the 2010 aerial photographs (c). The forest change map
(d) show areas of increase and decrease in tree cover. Landsat
imagery is shown in a band 4,5,3 combination (color figure online)
Eco-certification and coffee cultivation
123

6. took place in the coffee-growing region. The forest cover
classes at the two thresholds—forest ([40 %) and dense
forest ([70 %)—show clear differences between the cof-
fee-growing and the non-coffee-growing regions. For
[40 % forest cover, both regions showed increased forest
cover and patch size, but decreased connectivity in
2003–2009. For dense forest, only the coffee-growing
region showed an increase in percentage of dense forest in
2003–2009 (1.82 percent points), while the other indices
declined for the two regions, more acutely for the non-
coffee-growing region than for the coffee-growing one
(Table 1). Cloud cover is a confounding factor in this
landscape pattern analysis as clouds are much more pre-
valent in the higher altitude coffee-growing region com-
pared with the non-coffee-growing region. As a result,
many more of the forested patches in the coffee-growing
region are artificially split by cloud cover, leading to an
overestimation of landscape fragmentation and an under-
estimation of landscape connectivity in the coffee-growing
region.
Within the coffee-growing region, certified farms
gained significantly more tree cover compared with non-
certified ones (Table 2). Tree cover loss in coffee plots
was not significantly different between the two groups,
and yet the net gain in tree cover was significantly
greater for the certified compared with non-certified
farmers. The pairing effectively controlled for differences
in the size of the coffee farms, as the difference in the
total area planted in coffee for both groups was not
significant (Table 2).
Discussion
While previous research on eco-certification has focused
on assessing its impacts on farming practices, local biodi-
versity, and livelihoods, its ecosystem-level impacts had
received much less attention. Based on satellite imagery
and a pair–case comparison, our study showed that coffee-
growing regions have larger areas in forest, larger forest
patches, and better connectivity among patches than non-
coffee areas. This association does not imply one-way
causation as coffee cultivation occurs at a certain elevation
range, which may be associated with specific vegetation
characteristics. These differences in tree cover do not seem
to be accentuated over time except for dense forest area in
Table 1 Summary statistics of tree cover loss, gain, and net change for the coffee-growing and non-coffee-growing regions
Change in land cover Change in landscape-level
metrics for forest (tree cover
[40 %) 2003–2009
Change in landscape-level
metrics for dense forest (tree
cover [70 %) 2003–2009
Unit Total
area
No
data:
cloud
cover
Cloud-
free
data
Tree
loss
Tree
gain
Net
tree
gain
% of
forest
cover
Mean
patch
size
Mean
distance
to nearest
neighbor
% of
forest
cover
Mean
patch
size
Mean
distance
to nearest
neighbor
Study site ha 28,111 6,456 21,654 1,888 3,182 1,294
% 100.0 % 23.0 % 77.0 % 8.7 % 14.7 % 6.0 %
Coffee-
growing
region
ha 16,532 5,848 10,685 1,070 1,919 850 6.04 3.66 2.59 1.82 -0.32 1.04
% 58.8 % 35.4 % 64.6 % 10.0 % 18.0 % 8.0 % 10.3 % 33.6 % 2.9 % 4.7 % -6.3 % 1.2 %
Non-
coffee-
growing
region
ha 11,578 609 10,969 819 1,262 444 5.24 2.28 2.26 -1.39 -0.43 4.00
% 41.2 % 5.3 % 94.7 % 7.5 % 11.5 % 4.0 % 12.2 % 36.2 % 2.2 % -13.2 % -29.7 % 3.6 %
Table 2 Summary statistics of
tree cover loss, gain, and net
change for certified and non-
certified farms
* Significant at the 95 % level
Total
Area (ha)
Number of
coffee farms
Median size of
coffee farm (ha)
Tree
loss (ha)
Tree
gain (ha)
Net tree
gain (ha)
Paired-sample
Certified farms 9,560 237 1.72 1.12 9.30 5.95
Non-certified
farms
7,824 237 1.72 1.19 3.92 0.00
Difference
between
medians
0.00 0.07 -5.38 -5.95
p value 0.73 0.27 0.01* 0.01*
X. Rueda et al.
123

7. the coffee-growing region. This suggests that coffee cul-
tivation leads to an increase in areas with a high tree
canopy cover. The dense forest area has increased since the
introduction of a certification program that requires pro-
tection of forest remnants and riparian vegetation. More-
over, certified farms in the study area have increased the
amount of tree cover on their plots significantly more than
non-certified ones. Even though the whole region is char-
acterized by shade-grown coffee, farms that are certified
plant more and more diverse types of trees that non-certi-
fied ones. Most of the certification occurred toward the end
of the period under analysis: in 2003, only 15 farms,
covering 257 ha., had been certified. Most of the farms that
would eventually become certified (77 % of them) joined
certification after 2006. Therefore, the impact of certifica-
tion on tree cover and forest connectivity is likely under-
estimated in our analysis, and subsequent measurements of
tree cover should provide a stronger signal of the impacts
of certification, as planted trees grow.
Our study design detects additionality in the impact of
certification on tree cover increase: in a region with overall
increase in tree cover, certified farms contributed signifi-
cantly more to that trend than non-certified farms. Attri-
bution of these changes to certification is highly plausible
as our previous study on a sub set of the farms showed that
the two observable variables we controlled for—farm size
and location—adequately control for characteristics of
farms that could otherwise confound the effects of certifi-
cation (Rueda and Lambin 2013b). Self-selection into the
certification program of farms that had a greater initial tree
cover is unlikely to explain our results as tree cover
increased during the study period, in parallel with a more
widespread adoption of certification. Our study is therefore
likely to have captured the effects of coffee certification on
tree cover in a region already dominated by shade-grown
coffee cultivation. However, self-selection of farms based
on some unmeasured characteristic cannot be completely
ruled out. Therefore, one should remain cautious when
inferring strong causation from the correlations observed
between eco-certification and tree cover increase. Tree
cover loss was not significantly different between the two
groups, probably reflecting the normal cycle of coffee
replacement of aging trees that typically occurs in 10 % of
the farms each year.
Field observations suggest that certified farmers have
expanded their shade-grown coffee plantations over plots
that were previously under pastures or annual crops, while
non-certified farmers have kept more of their pastures and
open fields. Moreover, field surveys in a subset of the
sampled farms showed that certified farms were holding a
significantly greater diversity of tree species than non-
certified farms and were more actively preserving riparian
vegetation and secondary forests in their farms (Rueda and
Lambin 2013b). Certification is thus associated with sig-
nificant benefits for ecosystems as, in these landscapes, a
greater tree cover with a higher floristic diversity can
generally be interpreted as more healthy ecosystems
(Philpott et al. 2008).
Protected areas and other forms of land use zoning have
long been the main instrument to conserve natural ecosys-
tems. Much attention has been paid recently to payments for
ecosystem services as a way to incentivize communities for
the conservation of local ecosystems through a more
restricted access to natural resources under their jurisdiction.
Our results highlight the value of eco-certification to pro-
mote more sustainable agricultural practices in smallholder
farming systems. Previous studies have shown that certifi-
cation offers important benefits for people’s livelihoods
beyond the economic rewards derived from price premiums.
This ecosystem-level assessment further demonstrates the
potential of certification to contribute to a transformation of
land use practices that could affect whole ecosystems. Cer-
tification engages farmers in productive activities for which
global markets exist. Farmers are rewarded for their efforts
to improve the provision of ecosystem services through their
productive activities, while enhancing their livelihoods. It
may well be, however, that the case of coffee certification in
Colombia was a success story thanks to the convergence of
multiple factors, including a strong institutional support.
Replication of this study in other world’s regions should
explore whether benefits of eco-certification are as tangible
elsewhere.
Despite society’s interest in market-based mechanisms
for sustainability, their reach is still limited to a small
market segment. Globally, coffee cultivation occupies more
than 10 million hectares in the hands of more than
25 million growers, most of them smallholders. After more
than two decades of sustainability programs for coffee
farms, 40 % of all coffee production and 10 % of all coffee
consumption were labeled as compliant to sustainably
standards in 2012 (Potts et al. 2014). These figures include
third-party certification and allegiance to industry-led codes
of conduct, with lesser demands on the farmer. Bringing a
larger portion of the world’s smallholders into certification
will meet enormous challenges. Corporations and retailers,
however, are scaling up their commitments to increase their
purchases of sustainably produced agricultural products,
and wealthy consumers seem to follow the movement.
Conclusion
The Suarez watershed in the eastern Andes of Colombia is
a mountain landscape dominated by farms that grow coffee
under shade. Based on satellite imagery, geo-referenced
coffee parcels and a pair–case comparison, this study
Eco-certification and coffee cultivation
123

8. shows that the coffee-growing portion of the watershed has
larger areas in forest, larger forest patches, and better
connectivity among patches than the non-coffee-growing
area. These results not only confirm the findings of previ-
ous research showing the benefits of shade-grown coffee at
the parcel level (Perfecto and Vandermeer 2008; Perfecto
et al. 2007; Tscharntke et al. 2011), but expand such results
to show that clusters of shade-grown coffee actually have
an impact on tree cover and ecosystem connectivity that
are detected at the landscape level.
Furthermore, we have shown that eco-certification,
particularly the RFA certification program that promotes
the conservation of forest remnants and riparian vege-
tation, is associated with a detectable increase in dense
forest cover in the coffee-growing region. Dense forest
cover has increased in the study area as a whole since the
introduction of the RFA certification program. Moreover,
certified farms have increased the amount of tree cover
on their plots significantly more than non-certified ones.
Our study design therefore suggests additionality in the
impact of certification on tree cover increase: in a region
with overall increase in tree cover, certified farms con-
tributed significantly more to that trend than non-certi-
fied farms. Earth observation satellites are a powerful
tool to detect landscape-level changes and to allow the
evaluation of the impacts of specific conservation
programs, eco-certification, in this case, on cultivated
landscapes.
Acknowledgments We thank the Colombian Coffee Growers
Federation (FNC) for its generous support to conduct this research.
We are grateful to Luis Fernando Samper and Andre´s Valencia who
granted us access to the information and Juan Pablo Becerra and
Martha Cordoba who provided imagery and spatial data from SICA.
We also thank the Ishiyama Foundation for the grant that supported
this work.
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