ORIGINAL ARTICLE

Eco-certification and coffee cultivation enhance tree coverand forest connectivity in the Colombian coffee landscapes

Ximena Rueda • Nancy E. Thomas •

Eric F. Lambin

Received: 29 April 2013 / Accepted: 8 March 2014

� 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 � Impacts � Coffee �Colombia � Agroforestry � 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

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 (Sanchez-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 Suarez 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

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

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).

Reference data were generated using high 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

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

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

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

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 Andres 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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