Assessment of the
Coffee and Farmer
Equity (C.A.F.E.)
Practices Programfor FY08

Final Report

Prepared by
Bambi Semroc, Elizabeth Baer,
Teresa Castillejos
Center for Environmental
Leadership in Business
March 2011

PHOTOS: © CRISTINA MITTERMEIER/iLCPFINAL REPORT - FY08 Results Assessment
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Table of Contents
1.
2.
3.

Introduction and Objectives..................................................................................................... 8
Methodology.......................................................................................................................... 10
Applications ........................................................................................................................... 11
3.1

Growth in Participation ............................................................................................................... 13

4.1.1

4.

3.2

Application Approval Status ....................................................................................................... 16

3.3

Supplier Performance.................................................................................................................. 21

3.4

Conclusions from Application-Level Analysis ........................................................................... 22

Farms ..................................................................................................................................... 23
4.1

Overview of Farms Participating in C.A.F.E. Practices in FY08 ............................................... 23

4.2

Social Responsibility .................................................................................................................. 25

4.2.1

Compliance with Minimum labor standards ....................................................................... 25

4.2.2

Worker compensation ......................................................................................................... 28

4.2.3

Worker access to benefits ................................................................................................... 31

4.2.4

Worker living conditions .................................................................................................... 33

4.2.5

Access to education............................................................................................................. 34

4.2.6

Access to medical care ........................................................................................................ 36

4.2.7

Social Responsibility Conclusions and Recommendations ................................................ 37

4.3

5.

6.

Amount of coffee purchased through C.A.F.E. Practices ................................................... 14

Coffee Growing .......................................................................................................................... 39

4.3.1

Water and soil resource Conservation................................................................................. 39

4.3.2

Appropriate chemical use on farms..................................................................................... 45

4.3.3

Biodiversity conservation on farms and within the broader landscape ............................... 49

4.3.4

Environmental management planning on coffee farms....................................................... 55

4.3.5

Coffee Growing Conclusions and Recommendations ........................................................ 57

Producer Support Organizations ............................................................................................ 60
5.1

Methodology ............................................................................................................................... 60

5.2Management and Tracking Systems ........................................................................................... 61

5.3

Soil Erosion and Productivity ..................................................................................................... 62

5.4

Shade Canopy and Wildlife Protection ....................................................................................... 63

5.5

Ecological Pest and Disease Control .......................................................................................... 64

5.6

Environmental Management and Monitoring ............................................................................. 66

5.7

PSO Conclusions and Recommendations ................................................................................... 68

Wet and Dry Mills - Coffee Processing................................................................................. 69
6.1

Methodology ............................................................................................................................... 70

6.2

General Participation Data .......................................................................................................... 70

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7.
8.
9.

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Social Responsibility (stand-alone mills) ................................................................................... 72

6.3.1

Minimum labor standards ................................................................................................... 72

6.3.2

Improved compensation ...................................................................................................... 74

6.3.3

Worker benefits ................................................................................................................... 76

6.3.4

Worker living conditions .................................................................................................... 80

6.3.5

Access to education............................................................................................................. 81

6.3.6

Access to medical care ........................................................................................................ 82

6.3.7

Coffee Processing - Social Responsibility Conclusions and Recommendations ................ 84

6.4.1

Wet Coffee Processing ........................................................................................................ 85

6.4.2

Dry Coffee Processing ........................................................................................................ 89

6.4.3

Coffee Processing – Environmental Responsibility Conclusions and Recommendations.. 91

Conclusions & Recommendations......................................................................................... 92
References ............................................................................................................................. 95
Appendices ............................................................................................................................ 96
Appendix B: Producer Support Organization Compliance Rates by Indicator ..................................... 112
Appendix C: Mill Compliance Rates for C.A.F.E. Practices Indicators .............................................. 117
Appendix D: Farm Reporting Rates for C.A.F.E. Practices Indicators ................................................ 130
Appendix E: Mill Reporting Rates for C.A.F.E. Practices Indicators .................................................. 137

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List of Tables and Figures
Figure 1: Conceptual Model of Starbucks Investment in the Coffee Supply Chain ....................... 9
Table 1: Regional participation and number of applications verified FY06-FY08 ...................... 13
Table 2: Participation in C.A.F.E. Practices by Country from FY06 to FY08 ............................ 13
Figure 2: Overlap between Biodiversity Hotspots and Coffee-Growing Regions ....................... 14
Figure 3: FY08 Volume of Coffee Purchases by Farm Size ........................................................ 15
Figure 4: FY08 Volume of Coffee Purchases According to Farm Size ....................................... 16
Figure 5: Application Approval Status FY06-FY08 ..................................................................... 17
Figure 6: FY08 Approval Status by Region ................................................................................. 18
Figure 7: FY08 Approval Status by Country ................................................................................ 19
Table 2: Number of farms by size included in applications receiving various approval statuslevels ............................................................................................................................................. 19
Figure 8: Percentage of farms by size included in applications receiving various approval statuslevels ............................................................................................................................................. 20
Table 3: Coffee deductions from the C.A.F.E. Practices program .............................................. 20
Table 4: Average Application Scores FY06-FY08 ...................................................................... 21
Table 5: Number of Applications Verified and Average Total Score by Region, FY06-FY08 ... 22
Table 6: Relative Participation and Area of Small, Medium and Large Farms in FY08 ............. 24
Table 7: Number and Type of Workers Employed by Participating Farms in FY08 ................... 24
Figure 9: Geographic Distribution of Farms by Size Categories in FY08 ................................... 25
Table 8: Extrapolated compliance rates for zero tolerance indicators in FY08 ............................ 26
Table 9: Incidents of non-compliance on sampled farms by indicator in FY08 ........................... 27
Table 10: Number of farms and applications with zero tolerance incidents by region ................ 28
Table 11: Percentage of farms paying above the minimum wage in FY08 .................................. 29
Table 12: Number of farm workers earning wages above the legal minimum ............................ 29
Figure 10: Percent of farms paying greater than minimum wage to workers in FY08 by worker
type ................................................................................................................................................ 30
Figure 11: Percent of farms paying full-time workers above minimum wage or living wage ..... 31
Figure 12: Percentage of farms providing legally-mandated benefits to workers in FY08 bycountry .......................................................................................................................................... 32
Figure 13: Percent of medium and large farms with paid sick leave program for workers in FY08....................................................................................................................................................... 33
Table 13: Percentage of farms providing potable water to workers ............................................. 34
Figure 14: Percentage of medium and large farms providing primary and secondary education inareas with insufficient access ........................................................................................................ 35
Figure 15: Percentage of medium and large farms offsetting health care for workers in FY08... 36
Table 14: Watercourse buffer indicator results for FY08 ............................................................. 40
Figure 16: Percentage of farms with 2 and 5 meter watercourse buffer zones by farm size ........ 41
Table 15: Slope management results for FY08............................................................................. 43
Figure 17: Percentage of farms protecting steep slopes by degree ............................................... 44
Table 16: Organic nutrient management results for FY08 ........................................................... 45
Table 17: Use of WHO Type 1A or 1B chemicals ....................................................................... 46
Table 18: Agrochemical use in FY08 ........................................................................................... 46
Figure 18: Percentage of farms using agrochemicals only as a last resort by country and farm size
(CG-EM1.18) ................................................................................................................................ 47
Figure 19: Percentage of farms limiting herbicide use by country and farm size (CG-SR1.13) .. 48
Figure 20: Percentage of farms not using chemical fertilizers by country and farm size (CGWR2.4) .......................................................................................................................................... 48
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Figure 22: Percentage conservation hectares by farm size in FY08 ............................................. 50
Figure 23: Adoption rates of nitrogen-fixing shade on farms by farm size and percentage of
productive area .............................................................................................................................. 50
Figure 24: Adoption rates of nitrogen-fixing shade on farms by country ................................... 51
Figure 25: Average compliance rates for 10 and 40 percent shade density*................................ 52
Figure 26: Compliance rates for no forest conversion indicator by farm size and country (CGCB3.1) ........................................................................................................................................... 53
Table 19: Indicators used to assess management planning ........................................................... 55
Table 20: Number of participating PSOs by country.................................................................... 61
Figure 27: Percentage of PSOs facilitating distribution of soil erosion controls (PS-SR1.3, 1.4
and 1.5) ......................................................................................................................................... 63
Figure 28: Percentage of PSOs facilitating distribution of non-chemical controls for coffee berry
borer (PS-EM1.6, 1.7 and 1.8) ...................................................................................................... 65
Figure 29: Percentage of PSOs monitoring farm performance across 5, 10 and 15 percent of
their networks (PS-EM2.1, 2.2 and 2.3) ....................................................................................... 66
Figure 30: Percentage of PSOs implementing the management plan with more than 5, 15 and 25
percent of producers in their network (PS-EM2.6, 2.7 and 2.8) ................................................... 67
Figure 31: Percentage of PSOs training more than 5, 15 and 25 percent of producers in their
network (PS-EM2.11, 2.12 and 2.13) ........................................................................................... 68
Table 21: Number of mills participating in the program in FY08 ................................................ 71
Table 22: Number of stand-alone mills participating in C.A.F.E. Practices in FY08 by country 71
Table 23: Number of workers employed by stand-alone mills participating in C.A.F.E. Practices
in FY08 ......................................................................................................................................... 72
Table 24: Incidents of non-compliance on participating mills by indicator in FY08 ................... 73
Table 25: FY08 Mill incidence of non-compliance with zero tolerance indicators ..................... 73
Table 26: Mill compliance rates for zero tolerance indicators in FY08 ....................................... 74
Table 27: Percentage of mills paying above the minimum wage in FY08 ................................... 75
Table 28: Number of mill workers earning wages above the legal minimum .............................. 75
Figure 32: Percentage of mills paying workers more than minimum wage, by country in FY08 76
Table 29: Percentage of mills providing legally-mandated benefits to workers .......................... 77
Table 30: Number of mill workers receiving legally-mandated benefits in FY08 ....................... 77
Table 31: Percentage of mills paying legally-mandated benefits to workers ............................... 78
Table 32: Percentage of mills providing pension plans exceeding legal requirements to full-timeworkers .......................................................................................................................................... 79
Figure 33: Percentage of mills lacking convenient access to medical care by country ............... 83
Table 33: Number of wet mill facilities by country and type ....................................................... 86
Table 34: Participating dry mills by country ................................................................................ 90

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Executive Summary
In March 2008 Starbucks Coffee Company and Conservation International agreed to undertake
an assessment of the Coffee and Farmer Equity (C.A.F.E.) Practices program. The main
objective of this initiative was to assess progress against the stated objective of improving theuptake of best practices by participating coffee producers over time. The results of the analysis
could then be used as the basis for improving the program over time and to develop
communications messages that would build awareness and support for the program.
Most of the publicly reported results to date on C.A.F.E. Practices (e.g. percentage of purchases,
average criteria scores) have relied on analysis of application data which represents a particularsupply chain. This assessment marks the first step toward diving more deeply into theverification reports to examine farm and mill performance for a given year and establishes aframework for analyzing results on an annual basis. Verifications that took place between
October 1, 2006 and September 30, 2007 form the basis for Starbucks purchasing in FY08.
Review of these reports enables an assessment of performance among farms and mills
participating in C.A.F.E. Practices for that year and establishes a baseline that subsequent
analyses can use to establish trends. While there are 215 indicators that could be analyzed basedon the generic guidelines, this assessment has identified key indicators of social andenvironmental performance that can serve as the basis for subsequent assessments. The
methodology used to carry out this assessment can also be incorporated into the VRS toautomate the calculations and extrapolation process and thus expedite the analysis in futureyears.
This first assessment of C.A.F.E. Practices found:
Small farms of less than 12 hectares make up the vast majority (e.g. 99 percent) of thenearly 141,000 farms participating in the program.
The geographic reach of the program is vast for the three years analyzed included coffee
producers in 20 countries across four continents. These countries significantly overlap
with 8 of the world’s most biologically rich but most threatened regions.
Coffee suppliers achieved high levels of performance across the majority of the socialand environmental indicators selected to assess performance among mills and farms.
Starbucks buys significant volumes from small farms –this amounted to at least half ofthe company’s coffee purchases in FY08.
Coffee farms are making valuable contributions to the conservation of habitat in these
globally important areas for conservation. Participating farms have designated 102,281
hectares as conservation areas and 99 percent have not converted any natural forest areas
to coffee production during the since 2004.
Coffee production is contributing to social and economic development in these regions.
Participating farms employed 1.1 million workers and the majority of large and medium
sized farms made efforts to extend health care and education benefits to workers and theirfamilies.
While this assessment has resulted in the ability to quantify results of the program, thisinformation can also be used to guide improvements to the program Specific recommendations
from this report include:
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Strengthen some of the environmental requirements in the program without significantly
affecting the number of applications achieving an approved status. In particular,
Starbucks should consider making the indicator asking whether farmers had cleared
natural habitat since 2004 a zero tolerance indicator. Other indicators to consider in thisvein are those requiring any wood harvested for coffee drying to be sourced fromsustainable sources.
Review reporting rates for farms assessed on sets of indicators to improve consistency inreporting and enable further analysis of the data.
Repeat the analysis on an annual basis to monitor and evaluate performance over time.
The information gleaned from this analysis can be used to inform the verification process
and identify training needs for suppliers while also providing important information forreporting and communications about the program.

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1. Introduction and Objectives
In 2004 Starbucks launched the Coffee and Farmer Equity (C.A.F.E.) Practices sourcing
guidelines which established a set of best practices for environmental and social performance
among coffee producers. For the past five years Starbucks coffee purchases from producers
implementing the C.A.F.E. Practices guidelines have expanded significantly and in 2008
accounted for the majority of the company’s coffee purchases (77 % of coffee purchases in2008), as has the participation of coffee producers in the program. The program has grown to
include an increasing number of growers in 20 countries across 4 continents, 16 of which overlap
with 8 of the biodiversity hotspots – the world’s most biologically rich regions facing significant
threat (Mittermeier et al, 2007). In addition, all of the countries supplying coffee via the
C.A.F.E. Practices program are considered developing countries according to the World Bank,
with over 20% falling in the low income category (The World Bank, 2009).
Starbucks developed C.A.F.E. Practices to encourage the adoption of best practices for social
responsibility and environmental leadership in coffee growing and processing within the coffee
value chain. In 2008 Starbucks set the following goal:To purchase 100 percent responsibly grown and ethically traded coffee by 2015.
When we cite “responsibly grown and ethically traded coffee,” we’re referring tocoffee that is third-party verified or certified, either through C.A.F.E. Practices, Fair
Trade, or another externally audited system. (Starbucks, 2009)
Starbucks worked in partnership with Scientific Certification Systems (SCS), to develop a third
party inspection process that would verify changes in adoption levels of best practices related tohiring practices and working and living conditions for workers as well as conservation of waterand soil resources, local wildlife and introduction of environmental management programs onfarms.
In 2007, Starbucks and CI forged a renewed partnership focused on developing strategies to help
understand and mitigate the impacts of climate change on coffee producers and hence ensure thelong-term stability of the farms and the coffee. However, before taking this significant step and
exploring opportunities to integrate climate considerations within C.A.F.E. Practices,
understanding of the effectiveness of the program in its current form is necessary. As such, amajor thrust of our current work together is to conduct an assessment of the program todetermine the relative effectiveness of the program toward meeting its goals and identify
opportunities for continued improvements.
The working hypothesis behind C.A.F.E. Practices and most certification programs is that theimplementation of the program will result in the increased uptake of best practices within coffee
production landscapes and that the further implementation of best practices will reduce pressureson these landscapes and also reduce poverty in these communities. The alleviation of thesepressures would then result in improvements in both the environment and the socio-economic
livelihoods of farmers and workers and be demonstrated by improvements in species and habitat
conservation, the maintenance of ecosystem services, and improved livelihoods for local
communities. (See Figure 1.) However, until this point these hypotheses have remained
relatively untested aside from some site-level studies conducted. This was the case of C.A.F.E.
Practices over the past 5 years. The overall objective of the assessment is to identify the8FINAL REPORT - FY08 Results Assessment
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effectiveness of C.A.F.E. Practices in driving adoption of socio-economic and environmental
best practices within the Starbucks coffee supply chain, and, where possible, to identify how
adoption of these practices has influenced changes in grower income, health and education. In
addition, the study is assessing how adoption of conservation best practices has influenced theenvironmental landscape within the coffee growing region by looking at the relationships
between farmer participation, best practice implementation, changes in natural habitat andmaintenance of ecosystem services in at least two key coffee sourcing regions.

Figure 1: Conceptual Model of Starbucks Investment in the Coffee Supply Chain

This report is the first in a series of three which will be developed over the course of the project.
This first report discusses the results of an analysis of verification report data generated by third
party verifiers and reported through a web based system for the FY08 applications. This was thefirst year Starbucks used the Verification Reporting System (VRS) to facilitate the reporting
process via an on-line tool and it is for this reason that the first of the three reports focuses on
FY08, or year 3 of the program. A subsequent report will be developed looking at FY09 andFY10 data and analyzing the overall trends in participation and compliance within the program.
These first two reports will demonstrate changes in the adoption levels of best practices overtime. A third report will capture the findings of the field surveys implemented among coffee
farmers in key productive landscapes in Guatemala. CI and Starbucks will also conduct a fieldsurvey in one additional sourcing country and compile these findings in a fourth report. These
field surveys are designed to better understand the contribution of C.A.F.E. Practices to driving
changes in coffee production practices over time and the effects of these changes on theenvironment and local communities.
This report has three key objectives. First is to capture the findings of the assessment, draw
conclusions and make recommendations for improvement of the C.A.F.E. Practices program toStarbucks. Second, it provides the basis for public reporting and communications about the
C.A.F.E. Practices program as part of the Shared Planet platform. Finally, the report will serveas the methodological basis for subsequent analyses of verification report data that Starbucks canuse to monitor the program and report on progress.

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2. Methodology
Assessing the effectiveness of C.A.F.E. Practices in generating positive contributions to social
conditions of coffee producer families and their workers and environmental sustainability within
the coffee production landscape requires analysis at multiple scales over a given period of time.
The assessment applies a holistic approach and also includes analysis of verification report data
to identify changes in compliance rates over time, GIS analysis to identify important coffee
production landscapes within the broader conservation context, and a survey of coffee farmers in
at least two key sourcing countries. As part of the partnership with Starbucks, CI developed amonitoring framework that includes these various scales and allows for comparison of resultsover time. The stated goals and indicators within the C.A.F.E. Practices Guidelines and Generic
Scorecard formed the basis for this framework. For each statement of intent within the Generic
Guidelines CI and Starbucks drafted a number of targets followed by questions that theassessment should answer. We then identified at least one indicator to monitor over time foreach question. Many of these indicators aligned with the data collected in the verification reports
generated by third party verifiers conducting the audits at the farms and mills. The team
identified some additional contextual indicators that would provide information on participation
rates and trends in overall scores and approval status.
This report provides an analysis of verification reports for FY08 applications completed by third
party auditors from October 1, 2006 to September 30, 2007 using Version 2.0 of the C.A.F.E.
Practices Guidelines. FY08 represents the third year of C.A.F.E. Practices implementation, andwhere possible (e.g. the application analysis section) we include results from the two previous
years (FY06 and FY07) to demonstrate the progression of the program over time. Data from
FY06 and FY07, however, is based on Version 1.0 of the C.A.F.E. Practices guidelines whose
requirements differ somewhat from the revised version of the scorecard used to verify
performance in FY08.
For applications verified during this period, verifiers compiled information for each farm andmill verified and entered it into the Verification Reporting System (VRS). Starbucks staff
worked with the VRS service provider to generate an export of application, farm, mill andproducer support organization (PSO) data for applications verified between October 1, 2006 and
September 30, 2007. In addition, Starbucks staff provided approval status reports for FY06 andFY07 to enable analysis of trends in overall performance and geographical scope. Starbucks also
provided audited purchasing information for FY08 to enable links to be drawn between theapplications verified and actual volumes entering the Starbucks supply chain. CI worked with
Starbucks staff to review the data files provided and where necessary make minor corrections to
the data.
The results reported in this document under-represent the actual number of active participants inthe program for three reasons. First, Starbucks did not require applications receiving three-year
validity during FY06 and FY07 to undergo re-verification in FY08 and these applications were
not included in the analysis.1 These suppliers do, however, represent a portion of Starbucks
purchases for in FY08. Secondly, 16 applications underwent verification using Version 1.0 of
1

We based this analysis on the year the application underwent verification, recognizing that this does not aligncompletely with fiscal year purchasing data. To correct for this, we would have to include applications verified
during a previous year and still valid in FY08 as though the application had undergone verification during each year.

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the C.A.F.E. Practices Guidelines prior to the launch of VRS. These 16 applications consisted of
3649 farms and 25 mills which are not included in the analysis. Finally, one application from
Ethiopia completed the verification process after Starbucks transferred the data file to CI andthus is also not included in the analysis of farm, mill, or PSO results.
For each indicator verifiers assign a comply, non-comply or not-applicable rating to the farm,mill or PSO. Because verifiers can grant a not-applicable rating to any indicator, the number offarms reporting on any particular indicator can vary greatly and does. This is even the case withindicators in which some consistency would be expected. For this reason, results given inpercentage compliance represent only those farms for which the indicator was applicable(compliance + non-compliance). Thus, it is difficult to compare compliance rates acrossindicators, and where this has been done the reporting rate has been noted. The percentage
compliance rates are designed to show the relative performance of the various sizes of farms ortypes of mills for each individual indicator.
We used the application country as the basis for all country analyses as it represented the mostcomplete set of country data within the VRS. Some corrections were made to data wherecountries were misspelled or regions or cities were recorded instead of countries. Using theapplication country assumed that all farms within a single application are located in the same
country, when some applications included farms from two countries. We did not correct for thiserror, although we did correct for issues in which the trading company was based in a country
that does not grow coffee.
We used Access and Excel software to extrapolate verification report data2 to the population offarms participating in the program, given that small and medium farms are verified according toa stratified random sampling method based on established international standards for group
certification (ISEAL Alliance, 2009). We then used the extrapolated data to determine
compliance rates for a select group of agreed indicators included in the C.A.F.E. Practices
Generic and Smallholder scorecards. Where possible we correlated the compliance rates to thehectares and worker data collected as part of the verification process to determine the amount ofland or the number of people, respectively, affected by the practice. This report presents thefindings at the global and country level for each subject area of the guidelines.

3. Applications
Within the C.A.F.E. Practices program verifiers visit farms, mills, and producer support
organizations (PSO) within a given supply chain and verify performance against a set ofpractices. The verifier then submits the reports for an application to Starbucks and the reporting
system consolidates the scores into an overall score which leads to the assignment of an approval
status for each application received. Analysis of the verification report data can focus at theapplication level can also dive more deeply into farm, mill and PSO performance. An
application may represent a single farm or a group of thousands of farms along with dry and wet
mills and a PSO, whose membership can change over time. The application serves as the basis
2

For more information on the methodologies used to extrapolate farm performance to the population, please see
Appendices B (coffee growing) and E (coffee processing).

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for determining sample sizes for small and medium farms that undergo group verification andrepresents the entity receiving an approval status (e.g. verified, preferred, strategic, or noncompliant). Thus, the application forms the basis for managing farm, mill, and PSO verification
reports and hence any analysis conducted. However, the application information often
aggregates results across farm sizes and mill types and requires disaggregation to answer key
questions about adoption of best practices by farms or mills. For this reason, this section of thereport focuses on farm, mill and PSO results at the global, regional and country levels. In
addition, since the majority of the indicators apply to farms, the bulk of the analysis focuses on
the farm level.
An application represents a specific grouping of farms, mills and in the case of small farms PSOs
forming a single supply chain (from coffee cherry to green coffee) and represents the mostaggregated level of data reported within the program. The type and number of farms in any
given application varies greatly. In some cases an application consists of a single farm while inothers it represents tens of thousands of farms. For instance, one application received from
Colombia in 2007 consisted of over 24,400 small farms and was the largest application received
during that year.
The C.A.F.E. Practices guidelines categorize farms according to size, with small farms having
less than 12 hectares, medium farms having between 12 and 49 hectares, and large farms
consisting of 50 or more hectares. Applications may consist of a group of farms representing asingle size category (e.g. the Colombia application with over 24,400 small farms), or be a
combination of the various farm sizes. An application can be quite fluid in terms of the farms
and mills included in it from year to year. Farms and mills can move from one application toanother and export companies and cooperatives can decide to segment or combine applications
from year to year to achieve an improved approval status within the program or realize otherefficiencies within the system. Suppliers may also decide to organize applications according towhere the coffee is processed.
Starbucks has identified the following three objectives that can be assessed using applicationlevel data:
Achieve a high level of supplier performance on social responsibility and environmental
best practices in coffee growing and processing.
Purchase 100 percent of its coffee as responsibly grown and ethically traded by 2015,
whether through C.A.F.E. Practices or another externally audited system;
Over time increase the percentage of coffee purchases from applications receiving
preferred and strategic approval status, all other things equal.
Starbucks assigns approval status (e.g. verified, preferred, strategic, non compliant), andcalculates a total score, and subject area scores at the application level, which can provide somemeaningful data and demonstrate some high-level trends. Based on this, we identified sevenindicators to monitor progress against these stated objectives. They look at growth inparticipation across geographies, growth in the percentage of applications receiving verified,preferred or strategic status, growth in the amount of coffee purchases made from C.A.F.E.
Practices suppliers, and change in the average application scores of suppliers. This section
reports performance against these objectives and indicators and draw some conclusions at how
the program has grown and performed as a whole during the 2005-2007 period.
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3.1 Growth in Participation
Number of applications verified
Number of countries with applications verified
In FY06, the first year of the program, 554 applications underwent C.A.F.E. Practices
verification. This figure increased to 682 in FY07 and to 8043 in FY08. (See Table 1.)
RegionAfricaAsia
Latin America
Grand Total

Number of Applications Verified
FY06
FY07
FY08
0
38
38
0
25
36
554
619
730
554
682
804

Table 1: Regional participation and number of applications verified FY06-FY08

Between FY06 and FY08 the number of applications undergoing verification increased by 45
percent. In FY06 these applications spanned 10 countries, all of which were in Latin America.
The geographic scope of the program nearly doubled in FY07 as applications in 19 countries inAfrica, Asia and Latin America completed the verification process. (See Table 2.)
Country

Participation by Year

FY06
FY07
FY08
x
X
Brazilx
X
Bolivia
X
Burundixx
X
Colombiax
x
X
Costa Ricax
East Timorxx
X
El SalvadorxX
Ethiopiax
x
X
Guatemalaxx
X
Hondurasx
X
Indonesia
x
X
Kenya
x
x
X
Mexicoxx
X
NicaraguaxxXPanamax
X
Papua New Guinea
x
x
X
Peru
x
X
Rwandax
X
Tanzaniax
X
ZambiaTable 2: Participation in C.A.F.E. Practices by Country from FY06 to FY08

3

Although 804 applications underwent verification during FY08, the more detailed analysis for the year only
includes 787 of these applications. One application completed verification after the data transfer was complete and
16 applications were verified against Version 1.0 of the C.A.F.E. Practices Guidelines and not included in the VRS.

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In FY08 the number of countries participating in the program remained the same, although thelist varied somewhat from that of the previous year. For example, in FY08 no applications from
Brazil or East Timor underwent verification, but Burundi began participating for the first time in
the program.
While the number of applications shows an increase over time, this does not necessarily meanthat the number of farms or mills is increasing as some applications split and/or combine overtime and the applications in a given year may represent different farm groups.
Analysis of the applications allows us to conclude that to-date coffee producers in 20 countries
have participated in C.A.F.E. Practices by undergoing verification. The 20 countries participatingin the program overlap with 8 of the Biodiversity Hotspots – the world’s most biologically richregions that are under significant threat. (See Figure 2.) The environmental indicators within the
C.A.F.E. Practices guidelines provide important incentives for protecting the remaining habitat
and wildlife in these regions and also ensuring critical ecosystem services such as freshwater
quality and carbon storage are maintained.

Figure 2: Overlap between Biodiversity Hotspots and Coffee-Growing Regions

4.1.1 Amount of coffee purchased through C.A.F.E. Practices
Pounds of coffee purchased from C.A.F.E. Practices approved suppliers
Percentage of coffee purchases from large, medium and small farms
In FY08, Starbucks purchased nearly 295 million pounds of coffee through the C.A.F.E.
Practices program. This represented 77 percent of total coffee purchases for that year. Starbucks
purchased C.A.F.E. Practices verified coffee from 19 countries spanning 3 continents during thisyear. Only one country applied to the program and failed to comply with the guidelines such that
Starbucks did not purchase C.A.F.E. Practices coffee from that country during the year analyzed.
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year. In each of these cases Starbucks purchased C.A.F.E. Practices coffee from applications
that had undergone verification during a previous year and received a 2 or 3 year validity.
In FY08, Starbucks purchased C.A.F.E. Practices coffee from 645 different applications. The
vast majority (e.g. 86 percent) of these applications consisted of farms from a single size
category.4 The other 13 percent of applications selling C.A.F.E. Practices coffee to Starbucks
were a mix of small, medium and/or large farms. Knowing this allows us to draw someconclusions regarding the proportion of C.A.F.E. Practices coffee purchased from small farms
versus that produced on medium or large farms. At least 49 percent of FY08 C.A.F.E. Practices
coffee purchases were from small farms of less than 12 hectares, and 18 percent were from large
farms of 50 hectares or more. (See Figure 3.) For 29 percent of purchases a correlation withfarm size could not be made due to lack of data.5 Thus, these results under-represent the actual
percentages purchased from each size category.

29%
49%
18%

Small
MediumLarge
Unknown

4%
Figure 3: FY08 Volume of Coffee Purchases by Farm Size

Small farms dominated overall purchases in FY08, particularly from countries such as Bolivia,
Colombia, Indonesia, Rwanda and Tanzania. (See Figure 4.) The supply of C.A.F.E. Practices
from Kenya, El Salvador, Nicaragua, Panama and Papua New Guinea, however, came primarily
from large farms. Costa Rica, El Salvador, Guatemala, Mexico, Nicaragua and Papua New
Guinea had the most representative supply of coffee across the 3 farm size categories. Medium
farms were the least represented group in terms of coffee purchases for FY08, with only El
Salvador producing a large proportion of C.A.F.E. Practices coffee on farms of this size range.
This may be due to the inclusion of medium farms in applications consisting of farms
representing a range of sizes, which precludes this level of analysis.

4

Starbucks has defined farm size according to the following parameters: small farms are any farms of less than 12
hectares; Medium farms are any farms with 12 to 49.9 hectares, and: Large farms are any farms with 50 hectares ormore. (See Starbucks, 2008)
5
Either the application received a 2 or 3 year validity based on verification from a previous year or the application
represented a heterogeneous mix of farm sizes.

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% lbs Purchased

100%

80%

Unknown

60%

Large
Medium

40%

Small
20%

0%

Country

Figure 4: FY08 Volume of Coffee Purchases According to Farm Size

3.2 Application Approval Status
Percentage of applications achieving various approval status categories
Percentage of small, medium and large farms in applications achieving various approval
status categories
Pounds of coffee deducted from the program due to non-compliance
Starbucks assigns an approval status for each application that has undergone verification basedon the verification report entered by the verifier in the VRS. Applications receive scores foreach of the following subject areas:
Economic Accountability (pre-requisite)
Social Responsibility
Environmental Leadership - Coffee Growing
Environmental Leadership - Coffee Processing
Strategic suppliers are those achieving the highest level of performance (over 80 percent) in eachof the social responsibility and environmental leadership subject areas, followed by preferred(60-80 percent) and verified (less than 60 percent). To receive an approved status, all large
farms and mills must perform at the given level in each subject area, and for small and mediumsized farms, the aggregate scores of the sampled farms must meet the agreed threshold for eachsubject area.
Starbucks takes into consideration the type of supply chain included in an application whenassigning a non-compliant status. Applications consisting of a single medium or large farm thatfail to comply with required zero tolerance indicators receive a non-compliant status. For
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applications undergoing group verification6, at least 50 percent of verified farms have to meet thezero tolerance indicators to achieve an approved status in the program. In some cases there areapplications assigned an approved status, but that contain a small number of sampled farms that
did not comply with the zero tolerance indicators. In these cases, Starbucks deducts thepercentage of volume the sampled farm represents from the total quantity of coffee available forpurchase through the program and assigns a verified status to the application regardless ofwhether the overall performance of the application is above this threshold.
An increase in the percentage of applications achieving an improved status over time can serveas an important indicator of the success of the program. Ideally more applications would achievepreferred and strategic status over time and the number of non-compliant applications would
decline. This is the trend among applications verified during the first 3 years of implementation.
During this time when the number of applications achieving a preferred or strategic status rosefrom 30 percent in FY06 to 50 percent in FY08. (See Figure 5.) The percentage of applications
receiving a non-compliant status declined precipitously over the same period. In FY08, 26
applications were non-compliant with the zero tolerance requirements and thus unable to sellcoffee to Starbucks as C.A.F.E. Practices coffee. This was down from 112 applications in FY06
and thus represents a decline of 74 percent. In FY08 non-compliant applications represented
over 15,000 mostly small farms and spanned 8 countries.
Because the number of applications received varies from year to year, as does the make-up offarms and mills in any given application, a change in the number of applications achieving thevarious approval status categories does not directly correlate to increased adoption of bestpractices by farms and/or mills over time. This indicator can, however, serve as a proxy foroverall performance and demonstrates important improvements in the performance ofparticipants as a whole.
100%% of Applications

80%
60%

Strategic
Preferred

40%

Verified
Non-Compliant

20%
0%
FY06

FY07

FY08

Figure 5: Application Approval Status FY06-FY08

6

Applications consisting of medium and/or small farms undergo group verification in which a sample of the total
number of farms undergoes verification.

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A further look at the approval status data by region in FY08 shows an expected trend in whichcountries participating the longest in the program demonstrated a higher proportion of
applications achieving Strategic and Preferred approval status and lower levels of those failing tocomply with minimum requirements of the program. (See Figure 6.) Latin America has thehighest percentage of Strategic and Preferred applications. Within Latin America, Guatemala
had the highest number of applications receiving Strategic status, with Costa Rica, andNicaragua rounding out the top three countries. These same 3 countries along with El Salvador
also had the highest number of applications achieving Preferred status.
100%% of Applications

80%
60%

Strategic
Preferred

40%

Verified
Non-Compliant

20%0%Latin America

AfricaAsia

Figure 6: FY08 Approval Status by Region

Country level analysis of applications achieving the various approval status levels can bemisleading due to the variation in the number of applications received from suppliers in various
countries. (See Figure 7.) For instance, in FY08 two countries had only a single application
apply to the program whereas five countries had more than 100 applications undergoingverification. Those countries showing 100 percent of applications receiving a single approval
status had only a few applications (e.g. 1-4) verified. Guatemala, Costa Rica, El Salvador andNicaragua had the most applicants verified in FY08 and there is a high degree of variation in
performance among these countries.
Guatemala, Costa Rica, Peru, Bolivia and Tanzania had the largest percentage of applications
achieving strategic status. Eight countries, had applications receiving non-compliant status in
FY08. Burundi had no applications achieving an approved status. Honduras had the second
highest rate of non-compliance at over 20 percent.

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100%
80%
60%
Strategic
40%

Preferred
Verified20%

non_compliant

0%

Figure 7: FY08 Approval Status by Country

An increase in the number of farms included in applications achieving the Strategic andPreferred status can also serve as an indicator of improved performance over time. For thisparticular indicator, however, FY08 would serve as the baseline year. As mentioned above, anapplication may represent a single farm or a group of farms verified against the guidelines.
For FY08, the vast majority (67 percent) of farms in each size category were in applications
receiving a verified status. Another 22 percent achieved either a Strategic or Preferred status.
The remaining 11 percent of farms were included in applications found non-compliant.
Strategic

Preferred

Verified

Non-Compliant

Grand Total

Small
16,20914,754
93,203
15,347
139,513
Medium
114
216
695
23
1,048
Large
82
106
217
7
412
Total
16,405
15,076
94,115
15,377140,973
Table 2: Number of farms by size included in applications receiving various approval status levels

The approval status data when analyzed with farm size data permits an analysis of the percentage
of small, medium and large farms achieving the various approval status levels. This provides
some insight into whether any particular farm size is more likely to be included in applications
achieving strategic or preferred status. The results show little difference between the percentage
of small and medium farms achieving a verified or strategic status. Large farms were more
likely to be in applications achieving strategic status and small farms were less likely to be
included in applications receiving a preferred status. Small farms were also more likely to be inapplications receiving a non-compliant status. These results suggest that the C.A.F.E. Practices
program has been quite successful in enabling applications that include small farms to achieve
high levels of performance. While more of the non-compliant applications included small farms,
this could be due to the large number of small farms applying to the program. Applications withlarge farms appeared more likely to achieve a strategic status than those including medium
and/or small farms. This may be due to the ability of large farms to participate in small groups
or as individual applications within the program. Small farms undergo group certification which
can make it more difficult for applications to achieve a high status as the entire group of farms
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sampled have to perform at a high level to reach the required performance threshold. (SeeFigure 8.)100%80%Strategic

60%

Preferred
40%

Verified
Non-Compliant

20%
0%
Small
Figure 8:levelsMediumLarge

Percentage of farms by size included in applications receiving various approval status

Another indicator of improved performance among suppliers participating in the program is a
decline in the total volume of coffee failing to qualify for purchase within the C.A.F.E. Practices
program. These volumes represent coffee produced by farms in applications that received a noncompliant status or in the case of group certification, the proportion of farms found to be noncompliant with minimum requirements (e.g. green coffee deductions). Data from FY06-FY08
shows a dramatic decline in the amount of coffee not meeting the requirements for purchasethrough C.A.F.E. Practices. (See Table 3.) In FY06 the volume of coffee represented by noncompliant applications accounted for over 14 percent of the coffee verified, but this dropped toless than 2 percent in FY08. Between FY06 and FY07 an increase took place in both categories
which may be explained by the overall growth of the program to include a larger number offarms and an expanded geographic scope to include applications from Africa and Asia. Other
explanations for the change could be the release of a new version of the C.A.F.E. Practices
guidelines in FY08 which introduced some changes to the way in which verifiers evaluatedfarms and the introduction of the on-line data reporting system.
FY06
Green coffee
deductions (lbs)
Noncompliant
applications(lbs)
Total (lbs)

FY07

FY08

20,162,158

10,178,430

47,704,038

57,176,897

9,878,873

48,677,601

79,392,022

20,057,303

973,563

Table 3: Coffee deductions from the C.A.F.E. Practices program

One of the economic incentives built into the C.A.F.E. Practices program is the ability for an
application to receive validity over multiple years if certain performance conditions are met.
This results in cost savings as the supply chain does not need to undergo an annual audit and thus
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pay the requisite fees for re-verification for another 2 to 3 years. During the three-year period
between FY06 and FY08 applications could receive a 3 year validity if they achieved strategic or
preferred status and starting in FY08 a policy was implemented that required the audits to be
conducted during the harvest period to be eligible for this benefit. In FY06, 24 percent ofapplications received a 3-year validity, followed by a short drop in FY07 to 20 percent ofapplicants. Again, this decline may be due to the expansion of the program to new geographies
and the large increase in participation the program experienced during that year. In FY08, 34
percent of applications verified received a multi-year validity – a figure that exceeds even the
FY06 level.

3.3 Supplier Performance
Average application score
In addition to looking at change in the percentage of applications achieving the various approval
status categories over time, application scores can help determine trends in overall performanceof program participants. Each application receives an overall score as well as subject area scoresfor social responsibility, coffee growing and coffee processing.7 Since not every application
undergoes verification each year and the make-up of farms and mills in any application canchange from year to year as well, changes in these scores over time do not necessarily represent
the adoption of better practices over time by participating farms, mills and PSOs. Rather, it
provides a sense of the overall performance of the potential Starbucks coffee supply chain foreach year. These scores do not represent the average score for coffee purchases in a given year,
but can be used to assess general performance of supplier networks applying to the program in agiven year.
Although Starbucks does not use average total scores to assign supplier status they can serve as
an indicator of performance levels at both the global and country levels. Between FY06 andFY08 average total scores increased by 23 percentage points. (See Table 4.) From FY07 to
FY08 social responsibility scores did not change significantly, although coffee growing scores
did increase by 10 percentage points.
Avg.
Avg. CG
Avg. CP
Avg. CPSR
Score
Score (wet)Score (dry)
Score
FY06
52%
NA
NA
NA
NA
FY07
73%
76%
62%
NA
NA
FY08*
77%
78%
72%
68%
87%
Table 4: Average Application Scores FY06-FY08*FY08 average total score data includes all 804 applications verified that year. The subject area averagescores, however, do not include the 16 applications verified under Version 1.0 or the 1 application from
Ethiopia completed after the data transfer
Year

Avg. Total
Score

In looking at the country level average total scores an upward trend occurs for the majority of thecountries participating in the program. (See Table 5.) Exceptions are in 4 Latin Americancountries where scores fell between 2 and 9 percent. Statistical analysis of FY08 data shows nosignificant correlation (.18) between the number of applications verified in a given year and the7

Subject area scores were only available starting in FY07, as these were not recorded in the Starbucks databaseprior to this year.

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average total score. In FY08, 5 countries average total scores of 80 percent or greater. This is asignificant change from FY06 when no countries achieved an average total score of more than 78
percent.
RegionAfricaAsia
North & Central
AmericaSouth America
Total

FY06
FY07
FY08
No. of Apps Avg. Score No. of Apps Avg. Score No. of Apps Avg. Score
0
0
38
67%
38
70%
0
0
25
65%
36
73%
519
35
554

50%
73%
52%

557
62
682

74%
77%
73%

658
72
804

74%
87%
77%

Table 5: Number of Applications Verified and Average Total Score by Region, FY06-FY08

3.4 Conclusions from Application-Level Analysis
The analysis of application indicators of C.A.F.E. Practices growth, purchases, and performance
showed the following positive trends:
The number of applications verified grew from 554 in the first year of the program to 804
in FY08 and the number of countries represented nearly doubled during the same period.
Starbucks purchased nearly 294 million pounds of coffee from 645 applications in FY08.
This represented 77 percent of coffee purchased by the company and at least 49 percent
came from smallholder producers.
The number of applications achieving a preferred or strategic status increased over the 3
years analyzed, and the number of non-compliant applications fell by nearly 75 percent
during this same period.
Average total scores for applications improved by 25 percentage points since the start ofthe program.
Performance trends on average total score and approval status need to acknowledge the lack ofconsistency in the applications verified during any given year as some receive multi-year
validity. Thus, the upward trend is based on a different population in each year,
The application level analysis allows Starbucks to target certain countries for additional technicalassistance and training, especially for the 8 countries where some applications failed to achieve
an approved status. Of these Burundi and Honduras may warrant special attention as well as
closer scrutiny in the future based on past failure to comply with the zero tolerance indicators.
The average total score analysis also points to a need in Brazil and Papua New Guinea as thesetwo countries had the lowest averages in both FY07 and FY08.
The analysis also identified some potential advantages to larger farms as these tended to achieve
higher approval statuses and have fewer instances of non-compliance when compared to small
farms, even though the large farms are audited against a greater number of indicators. Starbucks
should continue to monitor these results over the next few years to determine the types oftechnical assistance necessary to increase performance among smaller farms.

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4. Farms
Farms form the basis of the coffee supply chain and thus serve as a primary focus of the C.A.F.E.
Practices program. The Generic C.A.F.E. Practices Evaluation Scorecard contains 179 indicators
used to verify performance of medium and large farms. The smallholder scorecard is made up of
74 indicators, which are a subset of those included in the generic guidelines with one additional
indicator to assess school attendance among children living on the farm. An additional 38
indicators are used to audit Producer Support Organizations that provide assistance to small farm
applications.8
The guidelines assess social responsibility and coffee growing aspects of farm performance andat a minimum the guidelines are designed to ensure that farms do not employ underage, forced orbonded labor and that they pay workers the legal minimum wage. Yet the broader objective is toencourage the adoption of best practices over time to improve the working conditions and wellbeing of workers and to conserve natural resources and biodiversity. Using verification reports
prepared by verifiers, we can determine the percentage of farms adopting the various bestpractices. These adoption rates can then be used to assess change over time using FY08 data as
the baseline.9
Verifiers mark each indicator as comply, not comply, or not-applicable for each farm visited andprovide written evidence in the report. We calculated the percentage of farms compliant with agiven indicator made based on the number of farms for which that particular indicator wasapplicable (C/C+NC). Applications including small and medium farms undergo group
certification in which verifiers visit a stratified sample of farms and prepare verification reports.
The total number of farms included in this sample equaled 5141 (581 medium farms and 4560
small farms).10 We extrapolated these results to the total population in the respective FY08
application for both medium and small farms. (See Appendix B for extrapolation methodology.)
We analyzed the farm data at the global, regional and country levels. The application country
field formed the basis for country classification as many applications lacked data for the country
field in the farm record as this was the field for which we had data for all farms that underwent
the verification process.
While it is possible to analyze compliance rates for each of the 179 social responsibility andcoffee growing indicators, this analysis focuses on a subset of these identified as the most
relevant for ensuring social well-being of farmers and workers and conserving natural resources
and the broader environment.11 This section presents the findings of the analysis and presents
some conclusions on farm level performance as well as recommendations for how Starbucks
might improve the program moving forward.

4.1 Overview of Farms Participating in C.A.F.E. Practices in FY08
Number of farms
8

See Section 4.3 for analysis of PSO performance.
FY06 and FY07 farm level data was not collected in a way that permits farm level performance to be tracked overtime, thus making FY08 the start date for trend analysis regarding farm-level performance.
10
This is in addition to the 411 large farms verified, for a total of 5552 farms with verification reports in FY08.
11
Some indicators were not analyzed due to time constraints, but could be analyzed in the future as the extrapolation
process is automated in the VRS.
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Number of workers
Number of coffee hectares
Geographic distribution of farms by country and farm size
In FY08, 140,973 farms participated in the C.A.F.E. Practices program, 99 percent of whichwere small farms of less than 12 hectares. (See Table 6.) In total these farms represented
479,309 hectares, over two-thirds of which were located on small farms.
Hectares
Number
Percentage
Number
Percentage
Small
139,51398.96%
297,787
62.13%
Medium1048
0.74%
26,179
5.46%
Large
412*
0.29%
155,344
32.41%
All
140,973
100.00%479,309100.00%
Table 6: Relative Participation and Area of Small, Medium and Large Farms in FY08
*The large farm count shows 412 farms, but only 411 were verified in FY08.
FY08

Farms

These farms provided over 1 million workers with full-time, part-time, or temporary
employment. (See Table 7.) The majority of employment was based on the need for temporary
workers on small farms, presumably during the harvest period. Farms also employed over 30,000
full-time, permanent workers, the vast majority of which worked on small farms.

FY08
Permanent
Workers
Part-Time
Workers
Temporary
Workers
Total(by size)

Small Farms

Medium Farms

Large Farms

Total(by type)

18,815

2,491

9,057

30,36226,01534,626

36,61597,256775,795

44,611

88,598909,004

820,626

81,727

134,270

1,036,622

Table 7: Number and Type of Workers Employed by Participating Farms in FY08

Of the farms sampled in FY08, 27 percent reported not employing any workers. The vast
majority (99 percent) of these were small farms of less than 12 hectares that may have usedfamily labor. However, 3 large and 15 medium farms also reported employing no workers,
which may warrant further investigation to ensure the verifiers are accurately recording thisinformation.
Small farms in three countries, Colombia, Indonesia and Ethiopia, made up over 50 percent of
the small farm population participating in C.A.F.E. Practices in FY08. Guatemala, Nicaraguaand El Salvador made up 71 percent of participating large farms during the same year. (SeeFigure 9.) Latin American countries represented nearly 80 percent of the medium and large
farms applying to the program, whereas the countries with the largest number of small farms
participating in the program were more representative across the three continents. Interesting tonote is that the 4 applications from Burundi found to be non-compliant represented 11 percent ofsmall farms applying to the program in FY08.
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March 2011

MediumLarge

Figure 9: Geographic Distribution of Farms by Size Categories in FY08

4.2 Social Responsibility
Within C.A.F.E. Practices farms are verified against a set of practices for working conditions andhiring practices in the hope that the livelihoods of workers and farmers will improve over time.
Most indicators in this section assess how farms treat their workers. By improving worker wages
and benefits and providing improved access to education, medical care and other benefits overtime, farmers can demonstrate a commitment to improving the livelihoods of workers. In
addition, farms can make investments improving worker wages, living conditions and the qualityof the education and medical care received. The Social Responsibility indicators are designed toprovide assurance that minimum performance standards are met, that the most egregious
practices are not part of the Starbucks coffee supply chain, and to encourage the further adoptionof better practices over time.
Although there are 71 indicators in the Generic Evaluation Guidelines that focus on social
responsibility, only 28 of these are also used to monitor conditions on small farms. Compliance
rates for all 71 indicators can be tracked over time, but this section focuses on a select set
identified as having the closest link to improved livelihoods for farmers, farm workers and theirfamilies.12 This analysis provides a baseline from which Starbucks can monitor any changes in
the adoption of best practices over time. To demonstrate actual impacts in these areas, further
information is needed on disease incidence, school attendance and student performance as wellas information on how workers on farms participating in C.A.F.E. Practices compare to regional
averages.
4.2.1 Compliance with Minimum labor standards
Number of farms with instances of child labor
Number of farms with instances of forced or bonded labor
Number of farms failing to pay the minimum wage to workers
Farm level analysis provides further insight into instances of non-compliance with the minimumlabor standards, or zero tolerance indicators, within the C.A.F.E. Practices guidelines. Instancesof non-compliance with the zero tolerance indicators, although not desired, demonstrate the12

See Appendix B for compliance rates for the majority of the Social Responsibility indicators.

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capacity of the program to identify cases of non-compliance. We can identify the number offarms where issues arose, and extrapolate these results to the broader population of farms
participating in the program.
When extrapolating the results to the entire population, we find that for each of the 7 zerotolerance indicators between 82 and 100 percent of farms complied with these practices in FY08.
(See Table 8.)
Large

Farm Size
MediumSmall

SR-HP1.1

All full-time workers are paid thenationally or regionallyestablished minimum wage

97%

95%

80%

82%

SR-HP1.2

All part-time workers are paid
the nationally or regionallyestablished minimum wage

98%

99%

100%

100%

99%

98%

95%

95%

100%

100%

100%

100%

99%

97%

98%

98%

99%

100%

NA

100%

100%

100%

100%

100%

Indicator

SR-HP1.3

SR-HP4.1

SR-HP4.2

SR-HP4.3

SR-HP4.4

All temporary/seasonal workers
are paid the nationally or
regionally established minimum
wage
Employer does not directly
contract any persons under theage of 14
Employment of authorized minors
older than 14 does not conflictwith their access to education
Management has an enforced
policy prohibiting discrimination on
the basis of gender, race,
ethnicity, age or religion as per
ILO Convention 111
Employer prohibits the use offorced, bonded, indentured or
involuntary convict laborAll

Table 8: Extrapolated compliance rates for zero tolerance indicators in FY08

The lowest level of compliance occurred among small farms failing to pay the minimum wage tofull-time workers.
Of the 5552 farms sampled in FY08, 174 failed to comply with at least one of the zero tolerance
indicators. This resulted in 3 percent of verified farms ineligible to sell coffee to Starbucks
through the C.A.F.E. Practices program. Of these 174 farms, 12 employed child labor (0.2
percent of farms sampled), and 1 was reported to employ forced or bonded labor. (See Table 9.)Upon closer examination of the verification report for the farm where forced labor was reported,the evidence entered by the verifier did not support the not comply evaluation found in thereport, suggesting that this incident may be a data entry error.

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Large

Medium

Small

Total

SR-HP1.1

All full-time workers are paid thenationally or regionally establishedminimum wage

10

16

25

51

SR-HP1.2

All part-time workers are paid thenationally or regionally establishedminimum wage

3

1

2

6

SR-HP1.3

All temporary/seasonal workers arepaid the nationally or regionallyestablished minimum wage

4

17

90

111

SR-HP4.1

Employer does not directly contract
any persons under the age of 14

1

3

8

12

1

2

17

20

3

3

NA

6

0

1*

0

1

Employment of authorized minors
older than 14 does not conflict with
their access to education
Management has an enforced policy
prohibiting discrimination on the
SR-HP4.3
basis of gender, race, ethnicity, age
or religion as per ILO Convention
111
Employer prohibits the use of forced,
SR-HP4.4
bonded, indentured or involuntaryconvict labor* May represent a reporting error
SR-HP4.2

Table 9: Incidents of non-compliance on sampled farms by indicator in FY08

The majority of farms (97 percent) found non-compliant with a zero tolerance indicator failed to
pay the legal minimum wage as set by the local government to their workers, especially totemporary or seasonal workers. The zero tolerance indicator that had the highest number of noncompliance issues was the payment of the minimum wage to temporary/seasonal workers, whichis to be expected given that many farms only employed temporary/seasonal labor during theharvest. Thus, this particular indicator applied to a much larger number of farms.
There was no particular pattern of countries or regions with higher incidents of non-compliancewith this set of indicators. (See Table 10.) Ten countries had farms failing to comply with atleast one of these indicators. Rwanda and Burundi had the highest instances of non-complianceon 36 and 33 farms, respectively, although these were confined to a small number ofapplications. Guatemala and Costa Rica had the highest levels of non-compliance with a zerotolerance indicator as 15 and 12, respectively. Burundi and Rwanda, however, had highnumbers of farms with incidents of non-compliance with these indicators that affected a very
high percentage of applications. In most cases farms failed to comply with only a 1 of the 7 zerotolerance indicators. However, farms in 8 countries failed to comply with 2 of the zero tolerance
indicators and Nicaragua had 2 large farms failing to comply with 3 of these indicators.

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RegionAfricaAsia
Latin
America
Grand Total

March 20117
5

# of Farms with:
1 ZT
2 ZT
3 ZT
18%
58
11
14%
13
5

56
68

8%
8%

# of Apps
with ZTs

% of
Apps

72
286

13
58

2
4

Total # of
Farms
69
18
87
174

Table 10: Number of farms and applications with zero tolerance incidents by region

Further analysis found that only a small proportion of farms with zero tolerance instances failed
to comply with more than one of these indicators. None of the farms sampled failed to comply
with more than 3 zero tolerance indicators. Two large farms failed to comply with 3 zerotolerance indicators and 29 farms failed to comply with 2. If a farm failed to pay full-time
employees the minimum wage, they were much more likely to also pay temporary and seasonal
workers less than the legal minimum.
Farm size does not appear to influence the likelihood of farms to have non-compliance issues
with this set of indicators. Small farms sampled had the lowest rate of failure (e.g. 2.7 percent)and medium farms had the highest rate at 5.5 percent. Large farms had a failure rate of 3.6
percent.

4.2.2 Worker compensation
Number of workers receiving the minimum wage
Number of workers paid above minimum wage
Number of workers paid a living wage
Percentage of farms paying overtime at or above the legal minimum requirement
The C.A.F.E. Practices program requires farms to pay workers the legal minimum wage andencourages them to exceed it by including additional wage indicators that ask whether workers
receive more than the minimum, and whether they receive a living wage. Verifiers report onthese indicators across 3 categories of workers: full-time, part-time, and seasonal. Verifiers also
record the number of workers by category employed on each farm sampled. By linking theworker numbers to compliance rates for the various wage-related indicators and extrapolating theresults, we can determine the number of workers receiving wages meeting the various strata.
Most farms participating in the program went beyond the legal requirements and paid wages thatexceeded the minimum across all 3 categories of workers. (See Table 11.) Medium and large
farms were more likely to comply with these indicators than small farms. Farms were alsosignificantly more likely to pay wages exceeding the legal minimum to part-time workers. Thismay be due to the relatively small number of farms that employed this category of worker.
Interestingly, full-time workers on small farms did not fare as well as part-time and seasonal
employees in receiving wages that exceeded the legal minimum. For instance only 48 percent ofsmall farms paid their full-time workers wages that exceeded the legal minimum requirements,
but 92 percent paid such wages to part-time workers and 73 percent paid them to temporary
workers.
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Large

Compliance
MediumSmall

All

SR-HP1.11

All full-time workers are paid morethan the nationally or regionallyestablished minimum wage

74%

76%

48%

52%

SR-HP1.12

All part-time workers are paid morethan the nationally or regionallyestablished minimum wage

76%

72%

92%

91%

SR-HP1.13

All temporary/seasonal workers arepaid more than the nationally or
regionally established minimum wage

74%

76%

73%

73%

SR-HP1.14

Full-time workers are paid at least aliving wage

86%

87%

55%

58%

Table 11: Percentage of farms paying above the minimum wage in FY08

The FY08 verification reports show that over 996,500 workers received the minimum wage set
by national or local governments. The majority of these workers were temporary workers
employed on small farms. Over 785,000 farm workers were paid more than the minimum wage.
When overtime was required, 91 percent of farms paid their workers the legal overtime wage forthis time and 51 percent exceeded this minimum.
In addition 58 percent of farms paid full-time workers a living wage sufficient to meet the basic
living needs of an average-sized family in the particular economy.13 This benefitted over 18,000
workers. (See Table 12.)
Farm Size

Indicator
Full-TimePart-Time Temporary
Greater than minimum wage
6,241
22,407
601,844
Small
Living wage
5,880
NA
NA
Greater than minimum wage
2,759
2,420
66,787
MediumLiving wage
3,795
NA
NA
Greater than minimum wage
5,267
4,298
73,810
Large
Living wage
8,363
NA
NA
Greater than minimum wage
14,26629,125
742,441
Total
Living wage
18,038NA
NA
Table 12: Number of farm workers earning wages above the legal minimum

Total Workers
630,492
5,880
71,966
3,795
83,3758,363
785,832
18,038The number of farms reporting on these indicators is quite inconsistent. For instance, thenumber of farms reporting that they used full-time workers ranged from 5525 (SR-HP1.11) to8859 farms (SR-HP1.14). This could explain the counter-intuitive trend that emerges among
medium and large farms in which a larger number of full-time workers received a living wage
than were paid wages exceeding the legal minimum. Another explanation might be that the
government-established minimum wage for some countries or regions was considered sufficient
to cover the basic needs of workers, but even in such cases it would seem that the percentages
should be more aligned. In either case, this may warrant further exploration to determine
whether verifiers require further training on these indicators.

13

International Labor Organization. www.ilo.org

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In most countries some proportion of farm workers received wages that exceeded the minimum
legal requirements. (See Figure 10.) Analysis of these indicators at the country level iscomplicated by the fact that not all countries had farms employing full-time or part-time workers
due to the type of farms applying to the program. For instance Indonesia, Rwanda and Bolivia
did not have any farms employing full-time workers, while in Bolivia, Ethiopia, Papua New
Guinea and Tanzanian participating farms did not employ part-time workers. Farms in only 2
countries -- Kenya and Mexico --employed all three categories of workers and paid all of them
above the minimum wage. Costa Rican and Guatemalan farms also employed full-time, parttime and temporary workers and in both cases were more likely to pay temporary workers above
the minimum wage than full-time or part-time workers.

100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%

Full-TimePart-time

Figure 10: Percent of farms paying greater than minimum wage to workers in FY08 by worker typeIndonesia, Panama, Costa Rica and Honduras had the highest compliance rates for farms paying
full-time workers a living wage. (See Figure 11.) While Panama had only a single farm apply tothe program in FY08, Indonesia demonstrated an ability for all small farms employing full-timeworkers (n=15) to comply with this indicator. Costa Rica had one of the larger reporting rates
for this particular indicator (n=1654) and still achieved a compliance rate of 97 percent. In
Burundi and Kenya (1038 small and 8 large farms, respectively) no farms paid a living wage toworkers, although some did pay beyond the minimum wage. Six countries had higher rates ofpaying living wages to full-time employees than paying wages exceeding the legal minimum(e.g. Costa Rica, El Salvador, Ethiopia, Honduras, Panama and Peru). In Honduras no farms
paid wages exceeding the legal minimum, but nearly all the farms paid a living wage. This could
be due to a high minimum wage requirement, but may warrant further examination and trainingof verifiers.

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100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%

March 2011

Full-Time
Living Wage

Figure 11: Percent of farms paying full-time workers above minimum wage or living wage

4.2.3 Worker access to benefits
Number of workers receiving legally mandated benefits
Number of full-time farm workers receiving pension plans that exceed the national standard
Number of workers receiving paid sick leave
Number of workers receiving paid vacation
The C.A.F.E. Practices guidelines include indicators on the provision of benefits to workers.
These include those that government regulations require of employers as well as those that may
exceed those requirements, such as paid sick leave, paid vacation and pension plans. Verifiers
collect information on the number of workers employed on the sampled farms which allows for
extrapolation of results to report on the number of workers receiving these types of benefits.
Legally mandated benefits: In countries where legislation requires the provision of somebenefits to workers, the majority of farms complied with this legislation in FY08. Extrapolated
data from the verification reports shows that 82 percent of farms paid full-time employees thesebenefits, whereas 99.5 and 94 percent of farms provided these benefits to their part-time andtemporary employees, respectively. Thus, farms were more likely to pay benefits meeting thelegal requirements to part-time and temporary workers. Papua New Guinea, Burundi and
Rwanda had the lowest levels of farm compliance for full-time employees, and in each case thesefigures were significantly lower than the compliance rates achieved by the same country for parttime and/or temporary workers. (See Figure 12.) These trends may be due to some countries
having stricter legal requirements to guide the employment of full-time workers, whereas there
may be fewer benefits mandated for the other categories, thus making it less costly to achievecompliance. The reason for this trend may warrant further exploration in the future.

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100%
80%
60%
40%

Full-timePart-time

20%

Temporary0%

Figure 12: Percentage of farms providing legally-mandated benefits to workers in FY08 by country

Pension: This indicator asks whether medium and large farms provide a pension plan thatexceeds national legal requirements for full-time workers. It is not verified on small farms, asmany small farms do not employ full-time labor. As might be expected, the compliance rate forthis indicator was rather low at only a little over 22 percent of farms. Farms providing thisbenefit employed 3700 full-time workers in FY08. Three countries – Ethiopia, Panama andZambia – had no farms comply with this particular indicator. Tanzania had the highest level ofcompliance at 67 percent (n=3). Costa Rica, Guatemala and Colombia each had much higher
numbers of farms verified against this indicator and compliance rates above 30 percent.
Paid sick leave program: The C.A.F.E. Practices guidelines include 2 indicators on paid sickleave benefits to distinguish between paying these to full-time versus all workers. As with thepension indicator, the sick leave indicators are verified only on medium and large farms. In
FY08, 83 percent of the medium and large farms participating in the program provided paid sickleave to their full-time employees. These farms employed 12,708 full-time workers. Whenlooking at the percentage of farms extending these programs to all of their workers thecompliance rate falls slightly to 71 percent, but the number of workers benefitting rises
significantly to 221,041 workers due to the inclusion of part-time and full-time workers in theequation. During the FY08 verification year, medium and large farms applied to the programfrom 14 countries and of these 10 extended paid sick leave to at least a portion of their part-timeand temporary workers. (See Figure 13.) Farms in Mexico and Peru had the lowest compliancelevels for both of these indicators, whereas the majority (10) of the countries had at least 80
percent of their farms providing sick leave to full-time workers. Farms in El Salvador and Costa
Rican were the most likely to extend these benefits to all workers. Panama also shows a highcompliance rate, but this is based on just a single large farm applying to the program in FY08.

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100%
80%
60%

Full-Time Workers
All Workers

40%
20%
0%

Figure 13: Percent of medium and large farms with paid sick leave program for workers in FY08

Annual Leave (Vacation): Medium and large farms providing annual leave of at least 10 days
per year employed 10,792 full-time workers in FY08.14 On average 75 percent of medium andlarge farms provided this benefit to workers according to the legal requirements. Due to the way
in which this indicator is worded the degree to which these benefits extended to part-time
employees is unknown and would depend on whether this was a legal requirement of therespective national government. Each of the farms participating from Kenya, Zambia andPanama provided this benefit to their workers (e.g. compliance rate of 100 percent). Farms in
Colombia and Costa Rica had compliance rates over 90 percent as well.
Farms in Mexico and Nicaragua were the least likely to provide this benefit to workers, but stillachieved compliance rates of 64 percent.
4.2.4 Worker living conditions
Number of farms that provided habitable dwellings to workers
Number of workers with access to potable water
Habitable Dwellings: Over 14,400 farms applying to C.A.F.E. Practices in FY08 provided
housing for some portion of their work force.15 The majority of farms that provided housing forsome portion of their work force were large and medium sized farms, as 66 percent of medium
farms and 91 percent of large farms provided housing for workers while only 10 percent of small
farms did the same.

14

This indicator does not apply to small farms.
This figure is based on extrapolation of reporting rates for SR-WC1.1based on a weighted average according tofarm size.
15

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Verifiers characterized the vast majority (e.g. 90 percent) of the housing provided as habitable.
Compliance rates varied by only 4 percentage points among the 3 farm size categories. In FY08,
418,756 workers were employed by farms that provided habitable dwellings to those workers
living on the site. The actual number of these workers who received housing as part of theiremployment is unknown, however, as verifiers do not collect this information. Of the 18
countries applying to the program in FY08 17 provided housing to at least some of their workers.
Indonesia, Kenya, Zambia and Panama all achieved 100 percent compliance with this indicatorand all but one country had compliance rates above 85 percent. Burundi was the only outlier
with only 39 percent compliance for this indicator..
On 69 percent of medium and large farms worker housing was a safe distance from theproductive area and agrochemical storage facilities to prevent exposure to pesticides.
Compliance levels ranged from 100 percent in Ethiopia, Panama and Zambia to 58 percent in
Costa Rica and El Salvador.
Potable Water: In FY08, 905,780 workers were employed by farms that provided access topotable water. This represents a 94 percent compliance rate for this particular indicator. (SeeTable 13.) Interestingly small farms had the highest level of compliance outperforming medium
and large farms by 15 and 4 percent, respectively. Eight countries had compliance rates of 100
percent for this indicator. Honduras had the lowest compliance level at 34 percent.
Farm Size% Compliance# of Workers

Small

94%

649,371

Medium79%

92,811

Large

89%

163,598All

94%905,780

Table 13: Percentage of farms providing potable water to workers

There are some instances of verifiers stating this indicator is not applicable (NA) for a farm.
This should only occur on small, family-run farms that do not employ workers, but someverifiers marked this on medium and large farms. Thus this is one indicator for which verifiers
may require further training and guidance.

4.2.5 Access to education
Percentage of small farms where children of legal school age attend school where available
and do not work during school hours
Percentage of farms with insufficient access to education providing primary or secondary
education to children of workers living on site
Percentage of farms with convenient access to education supporting schools through in-kindor financial support
Percentage of farms providing direct incentives for education

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March 2011Children living on small farms: The C.A.F.E. Practices program recognizes the distinctive
conditions on small farms when compared to medium and large farms and has identified a
unique indicator for monitoring access to education on small farms. Small farms are verified
against only one education indicator that looks at whether school-age children attend school incommunities where a school is available to the children. The same indicator also assures thatchildren are not working on coffee farms during school hours. In FY08,74 percent of small
farms participating in the program had children living on the farm. Of these farms, 99 compliedwith this indicator and made sure children attended school. The actual number of children thisrepresents is unknown as this information is not collected during the verification process. Eight
countries achieved 100 percent compliance for this indicator. Rwanda at 98 percent compliance
had the lowest level of performance for this indicator. Thus, the majority of small farms had
children living on the farm and the vast majority of these children attended school.
Children living on medium and large farms: A couple of the indicators are applicable only on
farms in communities where verifiers have noted an insufficient access to education. By
analyzing reporting rates for these we can identify the percentage of medium and large farms
located in remote areas where public primary or secondary schools are not available to children.
In FY08, 40 percent of medium and large farms were in areas that lacked sufficient access toprimary schools and 32 percent to secondary schools. An overwhelming majority (e.g. 97
percent) of medium and large farms in areas lacking access to primary education provided
educational instruction, facilities and materials that met national requirements. Of the 12
countries where these conditions exist, 11 achieved compliance rates above 95 percent. Of thefarms located in regions without sufficient access to secondary schools, 84 percent provided
secondary education to children living on the farm. In most countries medium and large farms
were more likely to provide supplementary primary than secondary education. (See Figure 14.)
Costa Rica and Colombia, however, were unique in that they were more likely to provide
supplementary secondary education. Results for Panama and Ethiopia are based on a single farm
reporting, and in Ethiopia the farm did not provide supplemental primary or secondary education
for children of workers.

% of Farms

100.00%80.00%
Primary Education

60.00%

Secondary Education

40.00%
20.00%
0.00%

Country
Figure 14: Percentage of medium and large farms providing primary and secondary education inareas with insufficient access

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In addition to filling this critical need, nearly half (e.g. 49 percent) of medium and large farms
are providing scholarships and other incentives for education to children. Finally, in regions
where public schools are available, 58 percent of medium and large farms are providing either
in-kind or financial support to these facilities.
4.2.6 Access to medical care
Percentage of farms with convenient access to public medical care
Percentage of farms supporting local medical facilities through in-kind or financial support
Percentage of farms offsetting the cost of health services for workers
The smallholder scorecard does not include an indicator to evaluate access to medical care, so
this section focuses solely on medium and large farms applying to the program in FY08. Duringthat year, 84 percent of medium and large farms were located in sites with convenient access to
medical care. Of these farms, 34 percent provided in-kind or financial support to these facilities.
Countries with the highest number of farms reporting on the indicator (e.g. Costa Rica, El
Salvador, Guatemala, Kenya, and Mexico) tended to have compliance rates between 20 and 48
percent. Those with fewer farms reporting (e.g. Ethiopia, Kenya and Panama) were more likely
to have all farms complying (e.g. Panama, Colombia and Kenya) or failing to comply (e.g.
Ethiopia).
In addition to supporting local clinics, some farms are offsetting the cost of health services fortheir workers. While 82 percent of medium and large farms offset these costs for full-timeworkers, 71 percent extended these benefits to all workers on the farm. This practice benefitted
221,041 workers. Panama, Tanzania, Costa Rica, Colombia and El Salvador had the highest rates
of compliance when only full-time workers were considered. Panama, Colombia and Peru roseto the top in extending these benefits to all workers. (See Figure 15.)
100%
80%
60%
40%

Full-Time Workers
All Workers

20%
0%

Figure 15: Percentage of medium and large farms offsetting health care for workers in FY0836

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4.2.7 Social Responsibility Conclusions and Recommendations
Overall, farms provided important employment opportunities in the regions and performed at ahigh level across the majority of indicators analyzed.
Participating farms employed 1.1 million workers and over 14,400 farms provided
housing to at least a portion of their workers.
Most farms participating in the program went beyond the legal requirements and paid
wages that exceeded the minimum to full-time, part-time and temporary workers. 58
percent paid a living wage to full-time workers.
Farms are ensuring children have access to education, even when they are located inremote areas. Nearly all medium and large farms located in areas without access toprimary schools provided supplemental facilities for workers’ children living on the farm,and 99 percent of small farms ensured that children living on the farm attended school.
The majority of large and medium sized farms made efforts to extend health care andeducation benefits to workers and their families.
Over half of medium and large farms have a paid sick leave program for workers andthree-quarters have annual leave programs.
The compliance rates achieved in FY08 clearly demarcate the zero tolerance indicators andcriteria requirements from others included in the guidelines and farmers strove to meet these
performance requirements. For instance, compliance rates for criteria requirements tended torange from 80-90 percent. Compliance rates then tended to fall significantly (e.g. to 30-50
percent for those indicators requiring a larger up-front investment (e.g. in-kind or financial
support of schools, medical facilities) and not designated as criteria requirements in the program.
Of the indicators analyzed for the social responsibility section of the guidelines, 16 were relevant
to all farm sizes. Farms tended to perform well across these 16 indicators, achieving averagecompliance rates above 50 percent for all but one indicator.16 Small farms did struggle to pay
full-time workers wages that exceeded the national or regional minimum rate.17 In this casesmall farms achieved only a 48 percent compliance rate. Small farms in Burundi, Guatemala,Rwanda and Papua New Guinea appeared to have the most difficulty complying with thisindicator.
The majority of the indicators analyzed were applicable only on medium and large farms. Fromamong these, the provision of a pension plan for full-time employees demonstrated the lowest
compliance rate (e.g. 23%) and was consistent across both farm size categories. Low
compliance rates were also consistent across countries, with only Tanzania achieving acompliance rate exceeding 50 percent. Available data also shows that medium farms faced morechallenges than large farms in complying with this particular indicator. Farms were also unlikely
to provide in-kind or financial support to public health clinics, with only 34 percent of farms
complying with this practice. In both of these cases the indicators in question represent a move16

. This one exception concerns the provision of personal protective equipment for employees who apply chemicals(32% compliance) and is based on a low percentage of small farms complying with the indicator. Medium and large
farms both achieved compliance rates of over 65 percent. Small farms in Papua New Guinea, Rwanda, Burundi,
Tanzania, Mexico, Colombia, Nicaragua, Ethiopia and El Salvador are of particular interest for this indicator as eachcountry had compliance rates below 50 percent.
17
SR-HP1.11

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beyond compliance to best practice for a farm and thus help distinguish farms making additional
investments in the safety and welfare of their workers.
When looking at the data on a country-by-country basis across the indicators analyzed sometrends occur that could help inform Starbucks investment in training and farmer outreach. Most
countries (n=10) achieved average compliance rates of at or above 80 percent for the hiring
practices section of the guidelines. Exceptions were Burundi, Ethiopia, Papua New Guinea,
Rwanda, Tanzania and Zambia. Thus, Starbucks may consider undertaking further training onthese practices in East Africa. For the working conditions indicators most countries (n=12)
achieved average compliance rates of 70 percent or more across those indicators included in theanalysis. Burundi, El Salvador, Ethiopia, Mexico, Nicaragua and Papua New Guinea did notachieve this level of performance and may warrant further training on these particular indicators.
Given the lower level of performance for both criteria hiring practices and working conditions in
Papua New Guinea, Ethiopia and Burundi, priority might be given to these countries forinvestments in training.
This analysis also identified some key data gaps, areas for clarification within the guidelines
which can inform future implementation of the program and future analyses of the results.
Some data gaps restrict the development of strong communications messages based on thisanalysis – especially regarding the number of children living on the farms who would benefit
from investments made in improving access to primary and secondary schools. The same gap
occurs when looking at the number of workers living on participating farms. Collecting this typeof supplemental information could result in improved communications about the program toStarbucks partners and customers.
There is also a gap in what we are able to learn about the farm owner, especially in the cases ofsmall farms as the majority of the indicators in this section focus on the treatment of workers.
There may be opportunities to expand the smallholder scorecard to include some further
information on changes to the household over time (e.g. improved housing, potable water, etc.)
even in cases where only family labor is used.
There were also instances where further definition of the indicator could prove helpful to ensure
verification is conducted in a standardized way. For instance, the indicators for access tohousing and potable water could be strengthened by adding definitions for each of these terms.
Finally, when verifiers mark an indicator not applicable they note the rationale for this (e.g. nochildren live on the farm, no workers employed, etc.), but this qualitative information was notavailable to CI during the analysis. Having this information would have assisted in thevalidation of assumptions made regarding the use of reporting rates to determine the number offarms fitting a given profile (e.g. number of farms with children living on the premises, numberof farms lacking access to primary or secondary education, etc.) and help explain unexpected
trends. Starbucks may wish to undertake further analysis to better understand the reasons forthese results and to identify any necessary actions.

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4.3 Coffee Growing
Farms participating in C.A.F.E. Practices are verified against 108 coffee growing indicators,
although 45 are included in the smallholder scorecard used to verify small farms of less than 12
hectares. For applications including small farms, verifiers apply an additional 38 indicators toverify Producer Support Organizations (PSOs), entities that provide technical assistance services
to participating small farms and facilitate group verification.18 Across the suite of indicators
used to assess coffee growing, applications achieved an average score of 73 percent. Scores
ranged from 100 percent in Bolivia to 59 percent in Panama. While these figures provide ageneral understanding of performance, a more detailed analysis of the indicators can identify
target areas for training, technical assistance and other types of support.
Starbucks could choose to track performance improvements in all of the 108 indicators overtime, but instead we have focused on key indicators identified as having the greatest potential tocontribute to positive conservation outcomes within coffee growing landscapes. The C.A.F.E.
Practices program is designed to provide assurance that minimum performance standards are met
and encourage farmers to adopt better practices over time. None of the coffee growing indicators
has the status of a zero tolerance indicator which would result in coffee ineligible for sale toStarbucks as part of C.A.F.E. Practices. Nine coffee growing indicators serve as criteria
requirements that if failed result in a zero points for that particular criteria.
Key areas identified for assessing results of C.A.F.E. Practices and the role the program has
played in encouraging the adoption of best practices for environmental conservation include soil
conservation, watercourse protection, and the preservation of natural habitat. Another key areaof analysis is the development of environmental management plans by farmers to guideinvestments in improved practices over the long term. Across the suite of focal areas a core setof objectives has been developed which highlights the broad objectives of the program. While
the overall objective of the Coffee Growing portion of the guidelines is to improve
environmental performance among coffee growers, more specific objectives can be set for the
specific criteria. For instance, one specific objective is to conserve biodiversity on farms andwithin the broader landscape and to conserve natural resources (e.g. soil and water) important forsustaining coffee production. For each of stated objectives, we identified a set of indicators to
track performance over time. This section presents the results of an analysis of best practice
adoption rates among farmers according to farm size and country.19
4.3.1 Water and soil resource Conservation
Percentage of farms maintaining buffer zones next to watercourses
Percentage of farms using native woody vegetation to maintain watercourse buffer zones
Number of farms managing for steep slopes to control erosion
Percentage of farms maintaining vegetative cover to reduce erosion
Water resources: The establishment and maintenance of riparian buffer zones along water
bodies is the primary focus of the water resource indicators. This includes maintaining a buffer
18

An analysis of PSO performance is presented in Section 4.3.
To demonstrate actual impacts on biodiversity, natural habitat, and water quality within a given production
landscape, additional studies would be needed that correlate species populations and forest cover, and water quality
trends to the adoption of best practices.
19

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between water bodies and areas of agrochemical application and waste dump sites on the farm.
The set of indicators on riparian buffer zones allows the verifier to acknowledge progress thefarm is making to establish these zones along water bodies over time by establishing a series ofpercentage and width thresholds. In FY08, 62 percent of participating farms with water bodies
on or adjacent to the farm maintained a 2 meter buffer zone along all water bodies. Large farms
had the highest compliance levels of 72 percent. (See Table 14.) In looking at performance bycountry, it becomes clear that countries with very low numbers of farms reporting on thisindicator (e.g. Panama and Zambia with 1 and 2 farms, respectively) achieved the highest
performance for this indicator with all of their farms having a 2 meter buffer along all water
bodies. Countries with higher reporting rates across all 3 farm sizes tended to have somewhat
lower compliance rates. Several countries with a high number of farms reporting achieved highcompliance rates, however. For instance, Honduras (n=305) and El Salvador (n=106) achieved
rates of 89 and 87 percent respectively. Farms in Nicaragua (n=1871) had the lowest compliance
rate at 32 percent.
Indicator
CG-WR1.1

CG-WR1.2
CG-WR1.3

CG-WR1.4

CG-WR1.5

CG-WR1.6

CG-WR1.7

CG-WR1.12
CG-WR1.14
CG-WR1.15

Small
Watercourse buffer zones are maintained
adjacent to at least 25% of water bodies(>2m in width)
Watercourse buffer zones are maintained
adjacent to at least 50% of water bodies(>2m in width)
Watercourse buffer zones are maintained
adjacent to all of water bodies (>2m in width)
Watercourse buffer zones are maintained
adjacent to at least 50% of
seasonal/intermittent watercourses (>2m inwidth)
Watercourse buffer zones are maintained
adjacent to all seasonal/intermittent
watercourses (>2m in width)
Watercourse buffer zones of at least 5m perside are maintained adjacent to at least 50%
of seasonal/intermittent watercourses andwater bodies
Watercourse buffer zones of at least 5m perside are maintained adjacent to allseasonal/intermittent watercourses andwater bodies
There is a plan to restore native vegetation
within the buffers
At least 50% of watercourse buffer zones
are composed of native woody vegetation
All watercourse buffer zones are composedof native woody vegetation

Farm Size
Medium Large

All

NA

92.43%

98.44%

94.28%

79.50%

84.85%

88.82%

79.67%

61.82%

65.99%

72.14%

61.98%

NA

64.65%

72.27%

67.17%

NA

43.85%

46.27%

44.65%

NA

52.51%

60.31%

54.99%

NA

34.95%

40.48%

36.70%

NA

51.84%

59.69%

54.37%

75.52%

80.72%

84.07%

75.70%

59.32%

54.23%

49.42%

59.12%

Table 14: Watercourse buffer indicator results for FY08

Increasing the width of the buffer zone to 5 meters and including both permanent andintermittent water bodies in the indicator results in a dramatic decline in compliance rates. While
this particular indicator is not used to assess small farms, average compliance levels for medium
and large farms fell by 25 percentage points from 62 to 37 percent.
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In this case countries with only 1-2 farms reporting were more likely to be at opposite extremesof performance. Panama had the lowest compliance rate, with its one farm failing to meet thisthreshold and Zambia had 100 percent compliance (n=2). Nicaragua and Papua New Guinea
had the lowest rates of compliance. Colombia (n=29), at 69 percent, had the second highest rateof compliance.
The majority (e.g. 76 percent) of farms with buffer zones used native woody vegetation on atleast half of the buffer zones, but this figure dropped to 59 percent when all buffer zones wereconsidered. (See Figure 16.) This trend held true for many of the individual countries, although
in Ethiopia compliance rates dropped by only 10 percent when all buffer zones were considered.

100%
80%
60%
Small
40%

Medium
Large

20%

All
0%
25% of water
bodies50% of water
bodies100% of water
bodies50% of water
bodies100% of water
bodies

2m

2m

2m

5m

5m

Figure 16: Percentage of farms with 2 and 5 meter watercourse buffer zones by farm size

The majority of farms also took precautions to protect waterways against agrochemical
contamination. In FY08, 77 percent of participating farms did not apply chemicals within 10
meters of water bodies or watercourses, and in this case small farms had the highest compliance
rates. This may be due to a smaller proportion of small farms actually using agrochemicals on
their farms. The vast majority of medium and large farms took additional steps to avoid
applying nematicides within 20 meters of any water body, with compliance rates of 95 and 92
percent, respectively. Countries demonstrated a wide range of performance among farms
applying chemicals. In Burundi (n=142) and Zambia (n=2) all participating farms maintained 10
meters of buffer between areas treated with chemicals and water bodies, but in Panama the one
participating farm did not. Papua New Guinea had the second lowest compliance rate at 30
percent (n=312). Farms achieved much higher compliance rates for the indicator on nematicide
application. Five countries achieved 100 percent compliance, and the lowest level of
performance was in Nicaragua which still had 73 percent of farms in compliance.
For many of these water indicators, there is inconsistency in reporting, as the number of farms
where the indicators were not applicable varies across groups of indicators. For instance 40,108
farms reported on the indicator stating that watercourse buffer zones are maintained adjacent to41

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March 2011at least 50 percent of water bodies, but only 39,011 reported against the indicator asking whether
such buffers were maintained adjacent to all water bodies. This point is particularly noticeable inreviewing Rwanda data for maintaining natural woody vegetation in buffer zones. In this case,an 11 percent higher compliance rate was achieved for farms having all buffer zones underwoody vegetation than those having such vegetation on only half of the area. This inconsistency
is due to the fact that 75 more farms reported on indicator CG-WR1.15 than reported on CGWR1.14. To address this issue, Starbucks should explore opportunities to add functionality to
the VRS which requires verifiers to report on all of the indicators in a given related set based on
the response to the first indicator within the group.
Soil resources: The soil resource indicators included in the C.A.F.E. Practices guidelines focus
primarily on preventing erosion on steep slopes (10 indicators) and using leguminous trees andcover crops in lieu of chemical fertilizers to maintain soil fertility (7 indicators). The majority of
the indicators (19 of 21) included in the analysis applied to all farm sizes, which makes thissection somewhat unique in that results can be reported across the entire population of farms
participating in the program.
The indicators associated with the protection of steep slopes verify the percent of slope underprotection for various grades of slope. Farms performed best in protecting at least 25 percent ofslopes over 10 percent, with 98 percent complying with this practice. (See Table 15.) However,these are the lowest grade slopes and it could be argued that the most important slopes to beprotected are those at greatest risk of landslide (CG-SR1.16) and those over 30 percent in grade(CG-SR1.10-12). Thus, an unexpected trend appears in which farms appear more likely toconserve slopes of between 10 and 20 percent than those at greatest risk of erosion (e.g. over 30
percent). While compliance rates for using shade trees and/or cover vegetation on slopes of over
30 percent are still high, averaging 63 percent if all slopes of this grade are included, this is 8
percentage points lower than that for slopes of over 10 percent. Variation in the number of farms
reporting against these indicators could explain this trend. For instance, while over 103,000
farms reported having slopes over 10 percent on their farms, only 77,000 reported having slopes
over 30 percent.
Compliance levels for conserving slopes of 20-30 percent varied significantly at the country
level. (See Figure 17.) For farms protecting all slopes of at least 10 percent, most countries
achieved compliance rates of 75 percent or higher. Colombia (n=27,683) and Kenya (n=6) were
exceptions with compliance rates of 32 percent and 33 percent, respectively. For farms
protecting all slopes of at least 20 percent compliance rates ranged from 5 percent in Rwanda(n=1036) to 100 percent in Panama (n=1) and Zambia (n=2). A similar range occurred for farms
protecting all slopes of over 30 percent, with only no farms in Papua New Guinea complying(n=117) , but 100 percent compliance in Panama (n=1), Kenya (n=1) and Zambia (n=2). The
latter appears to be a case where countries with low numbers of farms reporting on the indicator
were much more likely to achieve the highest compliance rates. Ethiopia, with 15,079 farms
reporting on this indicator managed to achieve a 99 percent compliance rate, however.

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Indicator
Productive areas on at least 25% of
slopes over 10% slope are covered
CG-SR1.4
by shade trees and/or covercrops/vegetation
Productive areas on at least 50% of
slopes over 10% slope are covered
CG-SR1.5
by shade trees and/or covercrops/vegetation
Productive areas on all of slopes
over 10% slope are covered by
CG-SR1.6
shade trees and/or covercrops/vegetation
Productive areas on at least 25% of
slopes over 20% slope are covered
CG-SR1.7
by shade trees and/or covercrops/vegetation
Productive areas on at least 50% of
slopes over 20% slope are covered
CG-SR1.8
by shade trees and/or covercrops/vegetation
Productive areas on all of slopes
over 20% slope are covered by
CG-SR1.9
shade trees and/or covercrops/vegetation
Productive areas on at least 25% of
slopes over 30% slope are covered
CG-SR1.10
by shade trees and/or covercrops/vegetation
Productive areas on at least 50% of
slopes over 30% slope are covered
CG-SR1.11
by shade trees and/or covercrops/vegetation
Productive areas on all of slopes
over 30% slope are covered by
CG-SR1.12
shade trees and/or covercrops/vegetation
Areas in which the risk of landslides
is extreme are not cultivated, are left
CG-SR1.16
or taken out of production andrestored with native vegetation
where practicable
Table 15: Slope management results for FY08

March 2011

Small

Farm Size
Medium

Large

98%

99%

98%

98%

91%

96%

94%

91%

71%

82%

85%

71%

96%

93%

95%

96%

87%

79%

79%

87%

67%

52%

62%

67%

89%

90%

97%

89%

77%

80%

89%

77%

63%

62%

77%

63%

NA

56%

56%

56%

All

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100%
80%
60%
40%

>10%
>20%

20%

>30%

0%

Figure 17: Percentage of farms protecting steep slopes by degree

This set of indicators also demonstrates some inconsistency in reporting by verifiers. For
instance, there is a different number of farms reporting on CG-SR1.4 (N=103,967) from thatreporting on CG-SR1.6 (N=102,939). The number of farms reporting should be consistentacross CG-SR1.4, CG-SR1.5 and CG-SR1.6 as they are simply gradients of compliance (e.g.
25%, 50% and 100%) against the same indicator (slopes over 10% covered by shade trees and/or
cover crops/vegetation). Reporting on medium and large farms was fairly consistent across these
3 indicators, with the highest degree of variation occurring among small farms. There is also ahigher degree of consistency between CG-SR1.4 and CG-SR1.5 than between these and CGSR1.6. The pattern of inconsistency occurs throughout this suite of indicators. To address thisissue, Starbucks might require verifiers to report on each of these indicators for a farm and notallow verifier to submit the report if reporting rates are not consistent across the related
indicators.
Organic matter and leguminous trees: The other focal area of the soil resources section of thescorecard is the use of natural sources of nutrients such as nitrogen-fixing cover crops andleguminous trees. Like the slope indicators, these are graded by the percentage of the farmcovered by these types of vegetation. Only 36 percent of farms had leguminous trees throughout
the entire productive area of farm, although 73 percent of farms applied this practice on at least
25 percent of the area. Medium and large farms were twice as likely to apply this practice thansmall farms.20 This may be due to the variation in growing conditions across the 18 countries.
As might be expected, East Africa had low rates of compliance with this set of indicators, withcompliance rates in most countries below 7 percent. Ethiopia, however, is an interesting
exception with 77 percent of the farms reporting on this indicator using leguminous trees
20This is the only indicator measuring the extent of shade-grown cultivation on a farm (see shade section) eventhough leguminous trees signify only one type of shade coffee system and does not capture rustic shade systems
which may consist of nitrogen-fixing trees combined with larger native trees.

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throughout the productive area of the farm. Bolivia and El Salvador at 100 percent compliance
had the highest levels of farms maintaining leguminous trees throughout the productive area.
Farms demonstrated higher compliance rates for cover crop use, with 64 percent applying themon the entire productive area, and 98 percent applying them on at least a quarter of the productive
area. (See Table 16.) Farms using cover crops on the entire productive area cover over 304,000
hectares of land. Interestingly, Zambia Panama and Bolivia all had compliance rates of 100
percent for the use of cover crops across the entire productive area of the farm. Zambia andPanama only had 2 and 1 farms reporting, respectively, but Bolivia had 168 small farms all incompliance with this indicator. At the other end of the spectrum, Colombia (n=29,844) andKenya (n=7) had the lowest rates at 27 and 25 percent, respectively.
Indicator

Small

Farm Size
MediumLarge

All

CG-SR2.1

At least 25% of the productive area iscovered by an organic matter layer and/or
nitrogen-fixing cover crops

98%

100%

99%

98%

CG-SR2.2

At least 50% of the productive area iscovered by an organic matter layer and/or
nitrogen-fixing cover crops

87%

97%

93%

87%

CG-SR2.3

All of the productive area is covered by anorganic matter layer and/or nitrogen-fixing
cover crops

64%

74%

74%

64%

73%

97%

96%

73%

55%

90%

89%

56%

35%

71%

71%

36%

CG-SR2.5

CG-SR2.6
CG-SR2.7

At least 25% of the productive area isplanted with nitrogen-fixing, leguminous
trees
At least 50% of the productive area isplanted with nitrogen-fixing, leguminous
trees
All of the productive area is planted withnitrogen-fixing, leguminous trees

Table 16: Organic nutrient management results for FY08

4.3.2 Appropriate chemical use on farms
Percentage of farms not using WHO Type 1A and 1B chemicals
Percentage of farms demonstrating a decrease in toxic load over time
Percentage of farms using pesticides and/or herbicides only as a last resort
Percentage of farms not using synthetic fertilizers (and no. of hectares)
Percentage of farms that are certified organic
WHO Type 1A and 1B Chemicals: In FY08, 98 percent of farms participating in the program
did not use chemicals classified as WHO Type 1A or 1B on their farms, except under certain
conditions explained in the nematode amendment. Small farms at 98 percent had the highest
level of compliance, probably due to the low level of chemical use by smallholders. Largefarms, the most likely to apply chemicals had the lowest compliance rates of 85 percent. (SeeTable 17.) Eight countries achieved 100 percent compliance with this particular indicator, andmost of them were in Asia and Africa. Panama and Zambia had no farms achieving compliance,
due in part to only a small number of large farms participating from each country.

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Indicator

CG-EM1.1

Farms do not use chemicals that arelisted by the World Health Organization as
Type 1A or 1B, except as specified by thenematode amendment

March 2011

Small

Farm Size
Medium

Large

98%

92%

85%

All

98%

Table 17: Use of WHO Type 1A or 1B chemicals

Toxic load: This indicator applies to only large and medium sized farms and would demonstratea decline in the eco and/or human toxicity of chemicals used on the farm over time for those
farms actually monitoring toxicity levels in this way. There are several inconsistencies inreporting that make it difficult to determine compliance rates with these two indicators for FY08.
First, only 1050 farms reported on this particular indicator, a figure which does not equal the
total number of medium and large farms. (See Table 17.) Verifiers may have considered thisparticular indicator not applicable for some farms. The reason could be that only those farms
applying chemicals were verified against this indicator. Yet, when comparing reporting rates on
this indicator with the one asking whether any agrochemicals are applied within 10 meters of awater body, some inconsistency still remains.
Secondly, of these 1050 farms reporting on the indicator asking whether the farm calculated the
toxic load, 201 complied. 875 farms reported on the indicator asking whether farms
demonstrated a decline in toxic load - a figure that is not consistent with the 1050 farms
reporting on the previous indicator. 171 of these 875 farms showed a decline in the toxic load, afigure that is actually less than the 201 farms reporting that they monitored toxic load, so it isfeasible that 171 of 201 farms complied with this indicator (a compliance rate of 85 percent).
These inconsistencies in reporting are quite uniform across all the countries, except for thosewith no farms reporting on this particular indicator (e.g. Bolivia, Burundi, Indonesia and
Rwanda). A case in point is the one large farm in Panama verified that reported non-compliancein calculating the toxic load but then compliant in decreasing this load over time. To addressthese inconsistencies, Starbucks might require verifiers to report comply/not comply for CGEM1.8 unless an NA rating was received for CG-EM1.7 in which case both indicators would
receive an NA rating.
To work around this issue, we identified the medium and large farms complying with CG-EM1.7
and used this data to determine the percentage that showed a decrease in toxic load, or compliedwith CG-EM1.8. The results show that 63 percent of medium farms and 56 percent of large
farms that completed a toxic load calculation saw a decrease in the load over time.
Pesticides: In FY08, 57 percent of farms only used pesticides as a last resort after cultural andphysical controls have failed. Large farms had a 14 percent higher compliance rate for thisparticular indicator than small farms. (See Table 18.) This trend suggests that although larger
farms may be more likely to use pesticides overall, they do so primarily as a last resort.
Indicator
CG-EM1.18

Agrochemicals are only applied as a last resort(after cultural and physical controls have failed)

Small

Farm Size
Medium

Large

57%

62%

71%

All
57%

Table 18: Agrochemical use in FY0846

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Burundi had the lowest level of farmers complying with these indicators (6 percent). Rwanda,
Panama and Nicaragua also had relatively low compliance rates. Ethiopia, Kenya, Panama, Peru
and Zambia each had all of their farms in compliance. (See Figure 18.)
100%
80%
60%
40%

Small

20%

MediumLarge

0%

Figure 18: Percentage of farms using agrochemicals only as a last resort by country and farm size
(CG-EM1.18)

Herbicides: In FY08, 95 percent of participating farms did not use herbicides to control groundcover, and applied them only during aggressive weed outbreaks. This represents over 341,000
hectares managed under this practice. Unlike the trend for pesticides, small farms faroutperformed larger farms in limiting herbicide use with a compliance rate of 96 percent while
medium and large farms achieved only 75 and 61 percent compliance, respectively. Thissuggests that small farms can more easily manage weeds through other means (e.g. cultural
practices) than larger farms. Most countries achieved compliance rates of between 90 and 100
percent for this particular indicator, with Papua New Guinea, Zambia and El Salvador reporting
the lowest rates. (See Figure 19.)

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100%
80%
60%
40%

Small
Medium

20%

Large

0%

Figure 19: Percentage of farms limiting herbicide use by country and farm size (CG-SR1.13)

Chemical Fertilizers: Verifiers report on whether farms apply synthetic fertilizers andStarbucks considers this when scoring applications as one among many indicators. Starbucks
recognizes applications that refrain from using synthetic fertilizers, but does not consider amovement towards organic production as the ultimate goal of the program. Instead the focus ison sustaining production of high-quality coffee. In FY08, 51 percent of farms representing
143,000 hectares did not use synthetic fertilizers. An overwhelming majority of the farms
complying with this indicator were small ones of less than 12 hectares. In comparing small
farms to medium farms the compliance levels fell by half. A small percentage (e.g. 8 percent) oflarge farms were also able to comply with this indicator. Bolivia was the only country achieving
100 percent compliance with this indicator. Those countries such as Panama, Zambia and Kenya,
in which only large farms applied to the program in FY08 tended to fare poorly with no instancesof compliance. Honduras also had lower levels of compliance than expected (e.g. less than 1
percent) due in part to all small farms using some chemical fertilizers during this year. (SeeFigure 20.)
100%
80%
60%
40%
20%
0%

Small
MediumLarge

Figure 20: Percentage of farms not using chemical fertilizers by country and farm size (CG-WR2.4)

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Organic Production: This indicator is only used to verify medium and large farms. In FY08 13
percent of medium farms and 4 percent of large farms were certified organic, representing
25,500 hectares. Of the 128 organic coffee farms participating in the program, 17 were large
farms. The remainder (n=111) consisted of medium-size farms. An overwhelming majority ofthese farms were in Peru, followed by Ethiopia and Mexico. (See Figure 21.)
100%
80%
60%
40%
Medium
20%

Large

0%

Figure 21: Percentage of certified organic farms participating in C.A.F.E. Practices by country andfarm size (CG-SR2.12)

4.3.3 Biodiversity conservation on farms and within the broader landscapeNumber of conservation hectares on participating farms (and percentage by farm size)
Percentage of farms with shade cover on at least part of the farm
Percentage of farms that have not cleared natural habitat since March 2004
Percentage of farms that have established conservation set-asides.
Percentage of farms managing areas of high ecological value
Conservation hectares: Participating farms managed 102,281 hectares as conservation areas.21
These are areas where conservation is the primary objective of land management, although somecoffee cultivation may take place as well. Small farms were responsible for nearly three quarters(e.g. 72 percent) of the conservation areas, but 20 percent were from large farms. (See Figure22.) Colombia and Guatemala each had over 20,000 hectares of conservation hectares managed
by coffee farms.22 The Colombian hectares are spread across a large number of small farms,
whereas for Guatemala the majority are located on large farms.

21

This figure is based on an extrapolation of the conservation hectares data for each verified farm. Set aside figures
are provided in a subsequent section.
22
Compared to 90,758 (29%) coffee hectares in the program for Colombia and 79,355 (26%) hectares in Guatemala.

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Large,
30%
Small,
63%

Medium,7%

Figure 22: Percentage conservation hectares by farm size in FY08

Shade coffee systems: One indicator of the prevalence of shade coffee production systems wasanalyzed in the soil resources section. That indicator found that 36 percent of farms applied
shade throughout the coffee production area (an equivalent of 246,213 hectares) by planting
leguminous trees. Seventy-three percent of farms applied these practices on at least a quarter ofthe productive area (105,860 hectares) and 56 percent applied them on at least half (181,564
hectares). Medium sized farms had the highest compliance rates across all 3 gradients of thisparticular practice, with small farms having the lowest compliance rates. (See Figure 23.)
100%
80%
60%

at least 25%
at least 50%

40%

100%
20%
0%
Small

MediumLarge

Figure 23: Adoption rates of nitrogen-fixing shade on farms by farm size and percentage of
productive area

Countries in Latin America were the most likely to plant nitrogen fixing trees on a least a quarterof their farms. (See Figure 24.) East African growers, on the other hand, were the least likely.
The only exception occurred in Ethiopia where 99 percent of farms had at least a quarter of theircoffee planted under these trees.

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100%
80%
60%
40%
20%

at least 25% All
at least 50% All
100% All

0%

Figure 24: Adoption rates of nitrogen-fixing shade on farms by country

The indicator measuring the extent of canopy cover within the productive area of the farm
provides another method of determining the prevalence of shade production systems. This
indicator (CG-CB1.4) applies to all 3 categories of farm sizes and thus serves as a proxy for
determining the percentage of farms with a 10 percent canopy cover (e.g. density). According
to the canopy cover indicator 87, percent of farms used shade production systems (on 451,000
hectares), which is actually a higher percentage than those using nitrogen fixing trees. Thisdifference could be based on the use of more rustic shade systems consisting of native canopy
trees rather than leguminous species. According to this indicator, farms in each country usedshade systems, although the extent of adoption ranged from 25 percent in Kenya to 100 percent
in Bolivia, Nicaragua and Panama. Of the 18 countries with farms verified in FY08, 13
countries scored above 90 percent compliance for this particular indicator.
Increasing the threshold of canopy cover to 40 percent (CG-CB1.8) leads to an 8 percentage
point drop in compliance rates, although this indicator is not used to assess small farms.(SeeFigure 25.) Ethiopia and Tanzania both achieved 100 percent compliance for this particular
indicator followed by Guatemala and Honduras which had scores above 90 percent. It isinteresting to note that Tanzania and Kenya both had higher compliance rates for the 40 percent
threshold, but this is due to small farms not being assessed against this threshold.

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100%
80%
60%
40%
20%
0%

10%
40%

Figure 25: Average compliance rates for 10 and 40 percent shade density**The 40% canopy cover indicator does not apply to small farms

Other shade indicators verify the quality of the canopy in terms of tree species diversity andnumber of strata. Results show that 63 percent of farms used locally native species for at least
three quarters of the canopy cover (CG-CB1.9). Large farms were 6 percent more likely to havethis level of shade than medium farms and 16 percent more likely than small farms. Bolivia(n=168) and Panama (n=1) each achieved 100 percent compliance with this indicator. Countries
in East Africa did not fare as well, with Burundi, Rwanda, and Tanzania each achieving less than
10 percent compliance and Tanzania below 20 percent.
The number of strata present within a shade production system (CG-CB1.10) is only assessed on
medium and large farms. In FY08, 59 percent of medium and large farms maintained at least 2
strata within the shade canopy. Large farms had the highest compliance rates, outperforming
medium farms by a difference of 6 percentage points. Once again Ethiopia (n=14) and Tanzania(n=1) performed extremely well with all farms in both countries having at least 2 distinguishable
strata within the shade canopy.
Forest Conversion: The overwhelming majority of farms verified in FY08 - 98 percent - hadsome natural habitat on their farm (e.g. verifiers recorded a comply/non-comply rating for CGCB3.1), and 99.6 percent of these farms had not cleared natural habitat over the past 3 years.
This means that participating farms maintained 102,281 hectares of conservation area during thisperiod. Small farms had the highest compliance rates for this indicator and medium farms thelowest at 96 percent. At the country level 8 of 18 countries achieved 100 percent compliance forthis particular indicator, thus conserving all remaining natural habitat on their farms. (See Figure26.) Peru, at 94 percent, had the lowest performance level. Peru also had the lowest compliance
rates across all countries for small and medium farms. Ethiopia had the lowest rate for large
farms (e.g. 88 percent). All of the other countries had at least 98 percent of participating farms
complying with this indicator.

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100%
80%
60%

Small
Medium

40%

Large

20%
0%

Figure 26: Compliance rates for no forest conversion indicator by farm size and country (CGCB3.1)

If medium and large farms did convert natural habitat, most took steps to ensure that the areas
cleared were not areas of high ecological value and established equivalent set-asides. Only 42
farms cleared areas of ecological value during FY08. Ethiopia had the lowest compliance rate
for this indicator at 7 percent, followed by Kenya at 50 percent, which suggests there may besome outreach and training needed in these countries to conserve natural habitat in coffee
production landscapes.
In addition to natural habitat, 79 percent of farms reported having native trees on the farm (CGCB1.1). Of these, 94 percent removed them only when they constituted a human hazard orcompeted significantly with coffee production. Compliance levels varied by only 3 percentage
points across the different farm sizes when taken as an aggregate across all participating farms.
At the country level, Rwanda, Zambia, Kenya, Panama and Bolivia each had no farms clearing
native trees unless these conditions were met. Burundi appeared to struggle the most with thisparticular indicator, with only 51 percent of farms in compliance, a rate that is 35 percentage
points lower than that of the next lowest country (e.g. Ethiopia).
Conservation Set-Asides: The two set aside indicators (CG-CB3.9 and CG-CB3.10) apply only
to medium and large farms participating in the program. To achieve compliance farms must set
aside either 3 or 5 percent of their farm as a conservation emphasis area, and make conservation
the primary objective of management activities. Across both farm size categories 76 percent set
aside 3 percent of their farm (approximately 11,550 hectares), but this rate dropped to 68 percent
when the threshold was raised to 5 percent of the farm (approximately 17,492 hectares).23 Largefarms had higher compliance rates in both instances. Most countries achieved over 80 percent
compliance for setting aside 3 percent of their farm. Papua New Guinea at 13 percent
compliance was the only country with very low compliance for this particular indicator and no23

The hectares calculations are based on an extrapolation of total area data recorded for each verified farm.

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farms in that country complied with the 5 percent set aside indicator. Ethiopia had an interesting
trend in that all of the medium and large farms set aside at least 3 percent of their land, but only
7.4 percent of them had set aside 5 percent.
Areas of High Ecological Value: While the indicator on clearing natural forest applies to all
farm size categories, those assessing areas of high ecological value (CG-CB3.3 through CGCB3.8) are only used to assess medium and large farms. Thus the results reported in this section
are applicable only to the 1460 medium and large farms exceeding 12 hectares in size. Of thesefarms, only 1316 were assessed against the indicator asking whether an assessment of the farm
has been completed to identify areas of high ecological value (CG-CB3.3). This may be due tothe lack of natural habitat remaining on some of the farms participating in the program. Of thefarms reporting on this indicator, 53 percent complied, with large farms 11 percentage points
more likely to have undertaken the assessment.
By comparing the number of farms reporting on the assessment indicator (CG-CB3.3) with thatasking whether the farmer has taken steps to protect these areas (CG-CB3.5), the number offarms with and without areas of high ecological value can be calculated. However, for this entire
suite of indicators there is some inconsistency in verifier reporting. For instance, 240 large farms
completed an assessment, but 252 were taking steps to manage and protect these areas. It is
unclear how a farm can be compliant in protecting areas of high ecological value if noassessment to identify these areas was undertaken. The same type of discrepancy occurred for
medium farms, although to a lesser degree, with 461 reporting that an assessment had been
undertaken and 468 reporting that they were managing and protecting these areas. This is an
instance where the information collected to score farms renders it difficult to identify
consistencies and trends across a series of indicators. Current guidance provided to verifiers is toevaluate all of the indicators as applicable regardless of whether or not an assessment wascompleted, thus potentially increasing the level of reporting and the percentage of farms found to
be non-compliant.
We conducted a more detailed analysis across this set of indicators using only those farms foundcompliant with the assessment indicator (CG-CB3.3). This analysis found that of the 701 farms
that had undertaken an assessment, 671 were taking steps to conserve areas identified as having
high ecological value. However, only 315 of these 671 farms reported on whether they weretaking steps to legally protect these areas (e.g. reported on indicator CG-CB3.8). The reason forthis large drop in reporting levels is unclear. For those farms reporting on this indicator, 107 or
34 percent sought legal protection for these areas.24 This is another indicator of best practices
which explains the relatively low compliance rate. Also, some countries have incentive
structures that discourage farmers and other landowners from setting land aside under legal
protected status.
The number of farms reporting on the legal protection indicator does not align with the number
found to be compliant with the conservation indicator even when corrections were made to only
include farms complying with indicator CG-CB3.6. For this reason a calculation of the numberof assessed farms that actually had areas of high ecological value could not be completed based24

Given that verifiers are instructed to rate farms as non-compliant for this indicator if they were also found to be
non-compliant with CG-CB3.5, we have corrected for this by eliminating from the sample those farms reported noncompliant with CG-CB3.5.

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on CG-CB3.5, as it returned a figure larger than the original 701 farms. However, in looking at
the restoration indicators, it becomes obvious that the majority of farms that undertook an
assessment found that they did have areas of high ecological value on their lands.
Reporting rates on the restoration indicator (CG-CB3.6) can provide a sense of the number offarms that had no areas of ecological value. In FY08, only 81 of the 701 farms that had
conducted an assessment were found to have no areas of high ecological value on their land.25
Of these 81 farms, 38 had habitat restoration plans and 24 were implementing them.
4.3.4 Environmental management planning on coffee farms
Number of farms with written environmental management plans
Number of farms with soil management plans
Number of farms with IPM plans
Number of farms with wildlife management plans
Number of farms with shade management plans
Number of farms with monitoring programs in place
The development and implementation of management plans is a consistent theme throughout the
Coffee Growing section of the C.A.F.E. Practices scorecard, although the indicators on
management plans apply only to medium and large farms. There is some variation on the typesof indicators included in the scorecard for each type of management plan, with some having
separate indicators for development and implementation. Only the environmental management
plan includes an indicator on whether the plan is updated annually. (See Table 19.)
Type of Plan
Developed
Implemented
UpdatedSoil
CG-SR1.2
NA
NA
Shade
CG-CB1.2
CG=CB1.3
NA
IPM
CG-EM1.19
CG-EM1.19
NA
Wildlife
CG-CB2.5
CG-CB2.6
NA
Environmental
CG-EM2.3
CG-EM2.4
CG-EM2.5
Table 19: Indicators used to assess management planning

For each type of management plan included in the scorecard assessment, reporting on thepercentage of farms that have both developed plans and are implementing them is challenging asverifiers mark the implementation indicator as non-compliant in cases where farms have notdeveloped a management plan. Using the standard extrapolation methodology, this deflates thecompliance rates for the implementation indicators as the non-compliant figure includes farms
that never developed plans. For instance, 715 of 1402 farms reported having shade management
plans. Yet, this indicator was used to assess 1215 farms (based on extrapolated figure), a figurethat does not align with the 1402 farms originally assessed for the CG-CB1.2. Thus, for farms
verified in FY08, a deeper analysis that only includes those farms that complied with indicators
asking whether a plan was developed is required for each indicator that asks whether the plan
was implemented or updated. A closer look at verification reports revealed that verifiers rated
some farms as compliant for implementing plans when they were not marked as compliant inhaving developed plans. The VRS could be used to correct for this type of reporting error using
25

This figure is based on the number of farms reporting on CG-CB3.6 and the number found to be compliant with
CG-CB3.3.

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an auto-fill function that does not permit the verifier to report a C or NC for an indicator that isdependent on achieving a compliance rating for another indicator.
Environmental management plans: Of the medium and large farms 58 percent had written
management plans to guide farming practices and decisions. Correlation of this figure with thenumber of coffee hectares on each of these farms shows that participating farms managed over
85,000 hectares according to an environmental management plan. Large farms were 13
percentage points more likely to comply with this indicator than medium farms. The majority(e.g. 75 percent) of farms that had developed management plans were implementing them aswell. Large farms outperformed medium ones in this regard with an 88 percent implementation
rate. Medium sized farms had a rate of only 74 percent. Of those farms having environmental
management plans in place, 72 percent were updating them on an annual basis. Once again large
farms were more likely to keep their management plans current.
When looking at individual countries a trend appears in which countries with only a few large
farms participating in the program (and no medium farms) were the most likely to haveenvironmental management plans for all of their farms (e.g. Kenya, Panama and Zambia).
Ethiopia and Papua New Guinea had the lowest percentage of farms with management plans,with respective compliance rates of 14 and 22 percent.
Soil management plans: In addition to environmental management plans, verifiers look for soil
management plans on large and medium sized farms. A larger proportion of medium and large
farms (i.e. 61 percent vs. 58 percent for environmental plans) had soil management plans tominimize erosion than those having broader environmental management plans. Once again large
farms had a higher level of compliance than medium farms. The range of compliance varied
greatly by country with Kenya, Panama, Tanzania and Zambia all achieving 100 percent
compliance, whereas Papua New Guinea and Ethiopia only achieved 13 and 14 percent
compliance, respectively. These rates appear to correlate with the number of farms reporting on
the indicator, as those with 100 percent compliance had relatively few farms reporting.
Integrated Pest Management (IPM) plans: The proportion of farms having IPM plans was
lower than that for either soil or environmental plans, with only 52 percent of medium and large
farms having a plan that is being properly implemented on the farm. Once again larger farms
had a greater likelihood of having such plans than medium farms, with a difference of 12
percent. This trend was also reflected in the country data, with only Tanzania and Zambia
achieving 100 percent compliance rates. Papua New Guinea and Ethiopia continued to have thelowest compliance rates of less than 10 percent each. Medium-sized farms in Colombia had
extremely high rates of compliance at over 97 percent.
Shade management plans: Shade management plans were even less common on large andmedium sized farms than IPM plans with only a 51 percent compliance rate. This translates toover 123,000 hectares of land managed under a shade plan. The trend of large farms having
higher rates of compliance continues for this indicator, with the difference extending to 17
percentage points. Of the 532 farms that had shade management plans, 492 (92 percent) were
implementing them in accordance with the established timeline. At the country level,compliance rates for having shade management plans ranged from 18 percent for El Salvador to
100 percent for Ethiopia, Indonesia, Kenya, Panama, Tanzania and Zambia. However, not all of
the countries that achieved 100 percent compliance for farms having shade management plans
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had the same level of compliance for the implementation of the plans. For example, in Kenya
only half of the farms with shade management plans were implementing them.
Wildlife management plans: Farms were the least likely to have wildlife management plans
than any other type of plan included in the C.A.F.E. Practices guidelines. Only 24 percent of
medium and large farms had developed plans to ensure the conservation of wildlife on their land.
Large farms outperformed medium farms by 11 percentage points in this case. Of the 326 farms
that had wildlife management plans, 286 (88 percent) were implementing them. At the country
level there was a wide range of performance for the indicator asking if farms had developed
wildlife management plans. For instance, Panama and Papua New Guinea had no farms withwildlife management plans, while in Tanzania the one farm participating had a plan, thus making
the country compliance rate 100 percent. Interesting to note is that 11 percent of Ethiopian farms
had management plans, but none of them were found to be properly implementing these plans.
Monitoring systems: Medium and large farms are also assessed on whether they are
implementing a monitoring program to track activities and improvements over time. Results
show that 59 percent of these farms were implementing such a program. Large farms were more
likely to have such programs, outperforming medium farms by 18 percent. At the country level,
Panama, Tanzania, Peru, Panama and Zambia each had all of their participating farms incompliance with this indicator, whereas Papua New Guinea and Ethiopia continued to struggle
with compliance rates of 23 and 25 percent, respectively.

4.3.5 Coffee Growing Conclusions and Recommendations
Participating farms achieved compliance rates of over 50 percent for the vast majority of the bestpractice indicators analyzed for this study. Only 18 of the 73 indicators analyzed had averagecompliance rates below this level and many of these had low rates of applicability (e.g. farms
applying irrigation or farms without areas of high ecological value). This demonstrates a
relatively high level of performance among participating farms for this section of the scorecard.
Compliance rates for the various indicators did vary according to farm size but for the most part
this variation did not exceed 10 percentage points.
Compliance rates were even higher for indicators identified as “Criteria Requirements” within
the C.A.F.E. Practices scorecard. The Coffee Growing section of the scorecard includes 9
criteria requirement indicators that farms must meet to generate points from the other indicatorsin the respective criterion. We included 7 of these 9 indicators in this analysis.26 Of these 7
indicators, 5 had average compliance rates of over 94 percent. The indicator prohibiting forest
conversion after March 2004 had the highest level of compliance (99.6 percent). Given thisextremely high compliance rate, Starbucks might consider designating this indicator as a zerotolerance requirement as it would only affect a small number of farms and would send an
important message stating the relative importance of conservation within the C.A.F.E. Practices
program. This is an important step in balancing the focus of the program between the social
responsibility and coffee growing sections of the scorecard as there are no zero tolerance
indicators currently within the latter.

26

Two of the criteria requirement indicators for the Ecological Pest and Disease Control section were not analyzed
for this first report (CG-EM1.2 and CG-EM1.3) but will be included in subsequent analyses.

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The requirement that farm managers implement a monitoring program had the lowest
compliance rate (59.2 percent) of all the criteria requirements, yet still more than half the farms
complied with this indicator. The high level of compliance for the criteria requirement indicatorsreflects the relative importance given to them within the C.A.F.E. Practices guidelines anddemonstrates the effectiveness of weighting certain indicators as higher priority. The message to
growers was clear and the vast majority took steps to meet these requirements. Although coffee
is often grown on steep slopes and the guidelines place a strong emphasis on maintaining coffee
productivity and water quality, there is currently no criteria requirement for the soil resources
section of the scorecard. Given the high levels of compliance achieved for many of the soil
resources indicators, Starbucks might consider making one of the indicators (e.g. knowledge ofareas prone to erosion) a criteria requirement.
Compliance rates for the other indicators included in the analysis experienced a higher degree ofvariation. Indicators with the lowest compliance rates were usually those in which a written
document was required for small farms as well as medium and large ones (e.g. CG-SR1.3) andthose which were only applicable to a small number of farms (e.g. irrigation, farms without an
area of high ecological value, etc.). Some sets of indicators were designed to recognize
incremental progress toward implementing a best practice across all of the coffee production areaof a farm. In these cases the higher the threshold, the lower the compliance rate. There were a
few exceptions, however (e.g. conversion of steep slopes) that call into question the level ofverifier and grower understanding of the indicator. There were other cases where the barrier toachieving compliance was quite high (e.g. certified organic) which resulted in lower compliance
rates. An interesting aside on organic certification is that Mexico and Peru had the highest
percentage of participating farms certified organic – two regions where Starbucks andConservation International have worked with farmers to promote the adoption of conservation
coffee best practices and organic certification. Starbucks core coffee line-up also includes
Starbucks shade grown Mexico which is Organic.
This analysis highlighted 2 areas of farm management where additional investments in technicalassistance could prove helpful: chemical and disease control, and; wildlife management. Withinchemical and disease control, particular emphasis should be given to the calculation of toxic
loads and the development of Integrated Pest Management plans for farms. Growers could also
use assistance in identifying wildlife species that may occur on their lands and developing
management plans to conserve critical habitat. There is also a need for some assistance in thedevelopment and implementation of restoration plans for those farms having no remaining
natural habitat of ecological value.
More specific results are provided below.
Shade Coffee: A high proportion of participating farms used some shade production system. 87
percent of farms reporting having at least 10 percent canopy cover and 36 percent had
leguminous shade trees planted throughout the farm. Shade production systems featured more
prominently among medium and large farms in Latin America. The indicators on shade do notprovide sufficient information to determine the actual percentage of farms adopting shade-grownpractices on a portion of the productive area. The soil resources indicators on leguminous trees
provide a rich level of detail on the percentage of farm with these trees as shade, but there is noequivalent for other types of shade systems. To determine the extent of the farm under shade,58

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Starbucks might consider adding some additional indicators asking whether 25, 50 or 100
percent of the coffee is under a shade canopy.
Soil Resources: Farms appeared more likely to implement soil conservation practices on slopesthat were of a lower grade. This may be due to some confusion on the relative importance of thegradation of the indicators in this section and even the order in which the indicators appear on
the scorecard or cost of implementation on steeper slopes. By starting with the 10 percent slope
indicators and working up to 30 percent, it would appear that the 10 percent slope indicators
would be the easiest to comply with as this is the way indicators are presented in the othersections of the scorecard. Yet, in this case areas of 30 percent slope or greater may represent a
relatively small percentage of the land under production and be the easiest for the farmer tocomply with. In addition, it could be argued that it is most important for these lands to be underconservation practices as they are at the greatest risk of erosion. In revisiting the guidelines, it
might be helpful to re-order these indicators so the 30 percent slope indicators appear first in thelist.
Water Resources: The majority of farms (62 percent) maintained riparian buffer zones on all
water bodies and 77 percent did not apply agrochemicals within 10 meters of water bodies. Thereis a high degree of emphasis on buffer zones along water bodies and watercourses on or adjacent
to coffee farms, yet there is no information on the amount of land under this type ofmanagement. Having data on the area under such practices (e.g. by adding a data point onlength) would be beneficial for reporting purposes, as it would allow the calculation of hectares
under these practices. Most farms did not use irrigation, but those that did had low levels ofcompliance with the irrigation indicators. The barriers for complying with these indicators
should be further explored to determine whether they are financial or technical.
Chemical Use: Nearly all of the farms verified in FY08 were not using pesticides classified as
Type 1A or 1B by the World Health Organization. At such a high compliance rate, Starbucks
might consider making this a zero tolerance indicator. 51 percent of farms used no chemicals on
their farms and 13 percent were certified organic. The level of chemical use on small farms washigher than might be expected based on the economics of these production systems. This might
be an area where further technical assistance to small farmers could improve performance levels.
Information on particular disease and pest issues in particular regions could greatly enrich thisanalysis and is an area where further input from agronomists within the Starbucks Farmer
Support Centers could help determine the cause of this trend and develop strategies to improve
performance in the future.
Conserving Biodiversity: Nearly all of the farms verified had not cleared natural forest since
March 2004. In addition, these farms had set aside over 100,000 hectares for conservation.
Most farms are taking steps to conserve biodiversity, primarily through the protection of natural
habitat and the retention of shade cover. The vast majority of medium and large farms had areasof high ecological value that should be managed for conservation. Some practices appeared torequire a high degree of expertise to implement (e.g. identification of endangered species,
development of wildlife management plans) and this was reflected in the lower compliance rates
for wildlife management and restoration. More guidance and technical assistance may benecessary to improve performance in these areas.

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Management Planning and Monitoring: Only medium and large farms reported on these
indicators and they were more likely to have a soil management plan than any of the other typesof plans included in the guidelines. They were least likely to have a wildlife management plan.
There appears to be a high degree of correlation among compliance rates for the various planningand monitoring indicators that occurred throughout this section of the scorecard. Those countries
with few numbers of large and medium farms fared quite well, whereas those with greaternumbers of farms in both categories were more likely to achieve lower compliance rates. In
addition, those countries struggling to comply with one of these indicators tended to struggle
with the others as well. Such was the case for Papua New Guinea and Ethiopia across most of
the indicators in this section.

5. Producer Support Organizations
Producer Support Organizations (PSOs) provide assistance to smallholder coffee growers
participating in C.A.F.E. Practices. These entities may take the form of a cooperative, an export
company, a mill, an NGO or other entity that works with farmers to improve farm management
and provide technical assistance that enables the adoption of better practices over time. They areincluded in the verification process for small farms and evaluated based on the support provided
to these farms. The objective of including PSOs in the C.A.F.E. Practices program is to send amessage to these entities regarding the need to raise awareness among smallholders and provide
them real support. Starbucks launched the PSO section of the smallholder scorecard March 1st,
2007, thus making FY08 the first year they were verified and the first year for which data isavailable.

5.1 Methodology
The smallholder scorecard includes 38 indicators designed to assess PSO performance. The
indicators evaluate performance on issues ranging from management and tracking systems tomaintaining healthy soils and protecting wildlife. In FY08, Starbucks received 355 PSO
verification reports from 15 different countries. (See Table 20.) The actual number of entities
performing the role of a PSO in FY08 is much lower than 355 as some PSOs (n=105) provided
services to multiple applications and thus had multiple verification reports associated with them.
We treated each verification report as a separate PSO and did not eliminate duplicate PSO
identification numbers given that Starbucks treats them as separate records within the program(and they occur within separate applications).
For each application with smallholders there is also a verification report for the PSO whichenables analysis of all participating PSOs without requiring data extrapolation. We analyzed the
PSO data to determine overall performance across all applications including small farms (lessthan 12 hectares) and performance at the individual country level. The results of this analysis arepresented below.

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Country
No. of PSOs
No. of Farms
168
Bolivia1
15,144Burundi529,955
Colombia
34
7,868
Costa Rica
70
253
El Salvador
12
19,513
Ethiopia
14
9,821
Guatemala69
358
Honduras
22
21,627
Indonesia
34
4,735
Mexico21
2,048
Nicaragua
34
370
Papua New Guinea
4
5,441
Peru
21
9,960
Rwanda10
12,251
Tanzania
4
139,512Grand Total
355
Table 20: Number of participating PSOs by country

5.2 Management and Tracking Systems
Percentage of PSOs with tracking systems from purchase to point of export
Number of PSOs with an annually updated list of producers participating in the C.A.F.E.
Practices program.
Number of small farms that receive a receipt for their coffee
Number of small farms that received a written agreement or identification card upon theircommitment to comply with C.A.F.E. Practices
Percentage of PSOs maintaining farm inspection reports, maps and description
Verification of PSOs includes an assessment of its management systems. Starbucks asks PSOs
to keep records of the farms participating in the program, to ensure farmers receive receipts forthe coffee sold and that the farmers voluntarily agree to participate in C.A.F.E. Practices. Each ofthese indicators has zero tolerance status within the program, although since FY08 was the firstyear Starbucks implemented the PSO verification, the company did not assign a non-compliancestatus to an application solely based on PSO performance related to these indicators.
Of the PSOs assessed in FY08, 97 percent had tracking systems in place to monitor the flow of
product from the initial purchase point through the point of export (PS-MT1.1). Only Colombia,
Costa Rica, Guatemala and Mexico had a small number of PSOs failing to comply with thisindicator. The PSOs in the other 11 countries were all in compliance with this requirement.
Mexico had the lowest performance with an 87 percent compliance rate (e.g. 13 of 15 PSOs).
The vast majority (e.g. 98 percent) of PSOs maintained a list of the smallholders participating in
the program (PS-MT1.2). Once again 11 of the 15 countries achieved 100 percent compliance.
Only Colombia, Ethiopia, Guatemala and Peru had some PSOs that did not comply with thispractice, although each of these countries had compliance rates of 90 percent or higher.
Ninety-two percent of PSOs ensured each small farm within its supply chain received a receipt
for their coffee (PS-MT1.3). This is significant in that means that 127,044 small farms received
a receipt for the transfer of their coffee. Some PSOs in Colombia, Guatemala, Indonesia,
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Mexico and Rwanda failed to provide receipts to all of the farms in their supply chain. Indonesia
had a compliance rate significantly lower than any of the other countries with only 35 percent of
the PSOs providing receipts to small farmers in their supply chain. This is of concern and should
warrant further investigation by Starbucks, as all of the other countries had compliance rates of
90 percent or higher.
Within the suite of indicators used to assess management and tracking systems, two had lower
compliance rates than the others. Only 58 percent of PSOs provided participating farmers with awritten agreement and identification card once the farmer committed to complying with C.A.F.E.
Practices requirements, and 52 percent maintained farm inspection reports. There were a wide
range of compliance rates across the 14 countries for these two indicators. Only Bolivia (n=1)
and Honduras (n=22) had all of their PSOs provide written agreements to their farmers, while
compliance rates for other countries ranged from 20 percent in Burundi (n=5) to 73 percent in
Nicaragua (n=34). Burundi also had the lowest performance for PSOs maintaining farminspection reports, with none of the 5 participating PSOs in complying with this practice. In
Bolivia (n=1) and Mexico (n=20) all participating PSOs maintained these records.

5.3 Soil Erosion and Productivity
Percentage of PSOs with soil management plans
Percentage of PSOs that facilitate distribution of erosion controls to 5, 15 and 25 percent of
producers in network
Percentage of PSOs using synthetic fertilizers on small farms
Percentage of PSOs implementing soil and foliar testing strategy
Within C.A.F.E. Practices, PSOs are responsible for assisting small farmers in developing
erosion control strategies and accessing erosion controls. In FY08, 74 percent of PSOs had
developed an explicit soil management plan that included erosion reduction strategies (PSSR1.1). All PSOs in Bolivia and Burundi had these plans in place, and Costa Rica had acompliance rate of 94 percent. None of the Tanzanian PSOs had developed soil management
plans meeting these requirements, and less than a third of participating PSOs in Indonesia andNicaragua had these plans.
PSOs also assist small farms with accessing erosion control materials. The indicators used torate PSO performance on this practice establish a continuum of performance that assesses thepercentage of producers receiving the service (e.g. 5, 15 or 25 percent).27 Across all PSOs, 59
percent facilitated access for 5 percent of producers in their network, but this figure fell to 51
percent when the threshold was raised to 15 percent of producers. Finally, only 47 percent of
PSOs were helping 25 percent of the farmers in their network access erosion controls. (SeeFigure 27.) Across this set of indicators only Bolivia, with its one PSO achieved 100 percent
compliance for all three thresholds. No other country achieved 100 percent for even the 5
percent threshold, although five achieved rates of 70 percent or higher. In Tanzania none of theparticipating PSOs facilitated access to erosion controls for the farmers in their network.
Honduras and Indonesia also had low performance rates (e.g. below 25 percent) across all three
indicators. Rwanda and Honduras had instances of reporting inconsistencies within this set ofindicators, where the percentage of PSOs that complied with the 15 percent threshold, was less27

Since this was the first year of implementation for the PSO verification, the thresholds established are relatively
low.

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than the percentage complying with the 25 percent threshold. This leads to the conclusion thatsome additional training may be required for verifiers working in these countries.
100%
80%
60%
40%
20%
0%

5% of farms
15% of farms
25% of farms

Figure 27: Percentage of PSOs facilitating distribution of soil erosion controls (PS-SR1.3, 1.4 and1.5)

PSOs also assist small farms with synthetic fertilizer management. However, there were 83 PSOs
whose farmers did not use synthetic fertilizers, which represents 23 percent of all PSOs verified
during this year. The remaining 77 percent of PSOs worked with farmers using synthetic
fertilizers and 74 percent of these had soil management plans that included strategies for
conducting soil analyses to identify any nutrient and/or organic matter deficiencies. Papua New
Guinea was the only country where all of the PSOs had soil management plans that included thisstrategy for managing application rates. In Ethiopia and Burundi, on the other hand, none of the
PSOs had developed these strategies. In fact, none of the East African countries participating in
the program were very likely to have such strategies in place, as no country achieved compliance
rates exceeding 25 percent.
Among the 201 PSOs that had strategies for soil analysis included in their soil management
plans, 99 were implementing these strategies according to the established timeline. Somecountries, such as El Salvador, Mexico, Papua New Guinea and Rwanda had all of their PSOs
implementing their soil management plans. Others like Colombia, Honduras and Guatemala had
implementation levels below 30 percent. There were a number of instances where a PSO that
had a soil management plan that included a soil analysis strategy but was not assessed against theindicator asking if the strategy was being implemented. This was particularly the case forIndonesia, Tanzania, Costa Rica, Mexico, and El Salvador. It is not clear why some verifiers
would report performance in this way, and this might be a point to discuss during subsequentverifier trainings in these countries.

5.4 Shade Canopy and Wildlife Protection
Percentage of PSOs with shade management plans
Percentage of PSOs that have identified resources for distribution of shade trees or seedlings
Percentage of PSOs with lists of wildlife species native to the region

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PSOs participating in C.A.F.E. Practices are encouraged to assist small farmers with shade
management by developing a shade management plan across the network of farms that identifies
gaps in shade canopy cover and areas with invasive, non-native species and sets forth steps toreplant these areas. In FY08, 71 percent of PSOs had shade management plans that identified
these areas. In Bolivia and Papua New Guinea all of the participating PSOs had these plans inplace. PSOs in Peru, Costa Rica, Colombia and Ethiopia also performed well, with compliance
rates of over 80 percent. In Tanzania none of the 4 participating PSOs had such plans. PSOs in
El Salvador were also very unlikely to have these plans in place, with only 25 percent of themcomplying with this indicator.
Verifiers also determine whether PSOs are helping farms identify resources for distributing
shade trees or seedlings. In FY08, 68 percent of PSOs had identified these resources for the
network of small farms in their supply chain. Costa Rica and Colombia and Bolivia all had
compliance rates of over 95 percent. PSOs in El Salvador, Honduras, Mexico and Tanzania werethe least likely to assist small farmers in identifying shade tree and seedling suppliers, with onlya third or less complying with this indicator. The reason for low performance in these countries
is not clear, although it may be due to the lack of readily available seedling sources.
The only wildlife protection indicator applied to PSOs looks at whether they have created a listof wildlife species that are native to the region, using the IUCN Red List of Threatened Species.
52 percent of PSOs had these lists of species for the region. Costa Rican PSOs, at 77 percent,were the most likely to have compiled these lists. None of the PSOs in Bolivia, Burundi,
Rwanda or Tanzania had compiled these lists. This may suggest PSOs do not have relationships
with universities or NGOs who could assist with this process. Starbucks might consider
conducting regional workshops with PSOs to assist with this indicator. Another alternative is to
use the Integrated Biodiversity Assessment Tool (IBAT – see www.ibatforbusiness.org) to
identify overlap of coffee farms with critical sites for conservation and generate an initial list ofimportant species.

5.5 Ecological Pest and Disease Control
Percentage of PSO not using Type 1A or 1B chemicals as listed by the World HealthOrganization28
Number of small farms applying agrochemicals only as a last resort
Number of small farms where coffee berry borer infestation is an issue
Percentage of PSOs facilitating distribution of biological control agents or traps for 5, 15 or
25 percent of the producer network
PSOs provide assistance to small farms in managing pest and disease outbreaks. This may takethe form of guidance on types of control interventions to apply, be they biological or chemical.
In FY08, 93 percent of PSOs refrained from using Type 1A and 1B chemicals as classified by
the World Health Organization. PSOs achieved 100 percent compliance with this practice in 8 of
the 15 countries with smallholder farms. An additional 5 had performance levels of 90 percent or
better. Nicaragua, with a 57 percent compliance rate had the highest levels of WHO Type 1A
and 1B chemical use.

28

Except those allowed under the nematode amendment.

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In most cases small farms working with PSOs applied chemicals only as a last resort. The
verification records show that 81 percent of PSOs used chemicals only after cultural and physicalcontrols had failed to produce the necessary results. This translates to 35,874 small farms
applying cultural physical controls to manage pest and disease outbreaks. In many cases,
however, verifiers found this indicator to not apply to the PSO verified and this affected over
101,000 small farms. Many of these farms were in Bolivia, Burundi, and Ethiopia, whereverifiers reported that the indicator was not applicable to any of the PSOs in these countries. Of
those countries where the indicator did apply, 7 achieved 100 percent compliance across all of
their PSOs. The lowest level of performance was in Nicaragua where only 29 percent of PSOs
worked with the small farms in its network to apply alternative, non-chemical controls for pest
and disease outbreaks.
C.A.F.E. Practices recognizes the severity of coffee berry borer infestation among small farms
participating in the program and asks PSOs to facilitate the distribution of biological control
agents or methane/ethanol traps to producers facing this risk. The suite of indicators used toassess PSOs on this practice applied to 271 of the 355 PSOs verified in FY08. Thus it can beconcluded that 61,930 small farms faced the threat of coffee berry borer infestation during thatyear – a number representing nearly half of the small farms participating in the program. The
majority (e.g. 65 percent) of PSOs facilitated access to alternative control mechanisms to at least
5 percent of affected producers. This figure declines to 62 percent when the threshold ofaffected producers is increased to 15 percent and to 52 percent when the threshold is set at 25
percent of affected producers. Bolivia, with its single PSO, was the only country with a 100
percent compliance rate across all 3 indicators. (See Figure 28.) El Salvador, Guatemala andPeru also had high levels of performance at the 25 percent threshold. Four countries – Ethiopia,
Papua New Guinea, Rwanda and Tanzania – had none of their PSOs providing these services tosmall farms. Other countries with low performance include Honduras and Indonesia. Mexico
was the only country in which the trend across the three indicators showed some inconsistency inreporting, as the percentage of PSOs meeting the 25 percent threshold exceeded that meeting the
5 percent threshold.
100%
80%
60%
40%
20%

5% of network
15% of network
25% of network

0%

Figure 28: Percentage of PSOs facilitating distribution of non-chemical controls for coffee berry
borer (PS-EM1.6, 1.7 and 1.8)

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5.6 Environmental Management and Monitoring
Percentage of PSOs with monitoring programs to track farm activities across 5, 10 and 15%of their network
Percentage of PSOs with written management plan
Percentage of PSOs implementing management plan with more than 5, 15 and 25% of their
network
Percentage of PSOs training more than 5, 15 and 25% of their network
The management and monitoring section of the PSO scorecard includes more indicators than any
other section used to assess these organizations. This section looks at whether the PSO has amonitoring program, a written management plan that is implemented, and if the PSO is training
farmers within the network on key issues.
Monitoring Program: In analyzing the percentage of PSOs with monitoring programs, there
was only a 5 percent difference between the percentage of PSOs monitoring 5 percent and 15
percent of their network. This suggests that the 15 percent threshold could be raised to 25
percent to push the PSOs to extend these programs even further. The only exception to this at
the country level was in Ethiopia where 93 percent of PSOs implemented these programs among
5 percent of their networks, but this figure dropped to 64 percent for the 10 and 15 percent
threshold levels. (See Figure 29.) Bolivia was the only country achieving 100 percent
compliance for the 15 percent threshold. Across most countries the trend shows that verifiers
understood the continuum of indicators, but in Guatemala auditors reported a higher percentage
of farms complying with the 15 percent threshold indicator than complied with the 10 percent
one. It appears that this is based on a single occurrence of a verifier reporting an indicator was
not applicable to a PSO when it should have been applicable.
100%
80%
60%
40%
20%

5% of network
10% of network
15% of network

0%

Figure 29: Percentage of PSOs monitoring farm performance across 5, 10 and 15 percent of their
networks (PS-EM2.1, 2.2 and 2.3)

Written Management Plans: C.A.F.E. Practices also evaluates if PSOs have developed written
management plans that include types of farm production systems in the network and information
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on coffee productivity, farmer training, ecological pest and disease management measures, soil
quality improvement strategies, and PSO farmer resource sharing. The analysis shows that 58
percent of PSOs had developed management plans that included this information. Once again
Bolivia (n=1) was the only country achieving 100 percent compliance for this indicator.
Colombia and Peru followed with rates of 90 percent or higher. None of the PSOs in Tanzania
or Burundi had these written management plans in place, however. Honduras at 14 percent
compliance also fared poorly against this indicator.
A subsequent set of indicators looks at the level of implementation for the management planacross 5, 15 and 25 percent of producers within the PSO network. There was little significant
difference between the percentage of PSOs implementing the management plan across 5 and 15
percent of producers, with compliance rates only differing by 4 percent. When the threshold israised from 15 to 25 percent of the network, however, the compliance rate drops from 72 to 59
percent – 13 percentage points. In analyzing the results at the country level, there is a high levelof consistency in the performance of PSOs across the 3 thresholds for a number of countries. For
instance, in Bolivia, Honduras, Peru, Rwanda and Tanzania there was no difference between thepercentages applying the plan to 5 and 25 percent of producers in their networks. (See Figure30.) Bolivia and Rwanda both achieved 100 percent compliance for this indicator; while
Tanzania had no PSOs comply. The latter, however, is due to the fact that none of the Tanzanian
PSOs even developed a written plan in the first place. The only other anomaly occurs with
Ethiopia, where verifiers reported more farms in compliance with the 25 percent thresholdindicator than with the 15 percent one.
100%
80%
60%
40%
20%
0%

>5% of network>15% of network>25% of network

Figure 30: Percentage of PSOs implementing the management plan with more than 5, 15 and 25
percent of producers in their network (PS-EM2.6, 2.7 and 2.8)

Training: Finally, Starbucks asks that PSOs provide training to small farmers within their
network according to the C.A.F.E. Practices guidelines. Among participating PSOs in FY08, 80
percent trained more than 5 percent of its network. When this threshold is raised to 15 and 25
percent of the network, the compliance rate fell to 77 percent and 75 percent, respectively. PSOs
in 7 countries trained more than 5 percent of producers in their networks, but only Bolivia did thesame for 25 percent of the network. (See Figure 31.) PSOs in Guatemala, Peru and Mexico had
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the highest rates of compliance for training more than 25 percent of its network of producers,
while those in Honduras, Rwanda and Tanzania had the lowest. This trend suggests that thelength of time in the program may correlate with performance levels at the highest thresholdlevel. For this set of indicators, El Salvador was the only country where reporting rates appear to
be inconsistent, with more PSOs meeting the 25 percent threshold than those meeting the 15
percent one.
100%
80%
60%
40%
20%
0%

>5% of network>15% of network>25% of network

Figure 31: Percentage of PSOs training more than 5, 15 and 25 percent of producers in their
network (PS-EM2.11, 2.12 and 2.13)

5.7 PSO Conclusions and Recommendations
Producer support organizations play a key role in assisting small farms in accessing technicalsupport, resources and training. Across the suite of indicators used to assess the performance of
the PSOs participating in C.A.F.E. Practices, compliance rates ranged from 98 percent of PSOs
maintaining an annually updated list of participating producers to 42 percent of PSOs
implementing their soil and foliar testing strategies according to the established timeline.
Starbucks has designated three of the management and tracking indicators zero tolerance
requirements for participation in the program. These require PSOs to track the product frompurchase through export, maintain an updated list of producers participating in the program, andensure each farm within the supply chain receives a receipt for their coffee. PSOs performed
well against these indicators, with compliance rates of 97, 98 and 92 percent. More PSOs failed
to ensure receipts for the coffee farmers sold (e.g. 28 PSOs) than for the other 2 indicators.
Based on this analysis it appears that most verifiers understand the indicators and how they relate
to one another. There were only a small number of instances where trends in compliance rates
across a set of related indicators demonstrated errors. Ethiopia had the most instances of thisoccurring and may require additional verifier training.
PSOs performed well across the majority of indicators, with compliance rates exceeding 50
percent for all but 3 indicators. Working with farmers to manage fertilizer application according
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to a management plan appeared to be the greatest challenge for PSOs. Two of the 3 indicatorswith compliance rates below 50 percent were in the Maintaining Soil Productivity section of thescorecard. Starbucks might consider working with PSOs to conduct further training to improvethese rates.
The single PSO in Bolivia performed extremely well across the suite of indicators – 100 percent
compliance for all but one indicator. Costa Rican PSOs also achieved high performance acrossthe entire set of indicators in this section, with a rate above 50 percent for all but one indicator.29
Guatemala also performed well across many of the indicators, especially in the ecological controlof pests and diseases and the environmental management sections. Tanzanian PSOs had thepoorest performance across the set of indicators, with compliance rates of less than 50 percent
for 27 indicators. PSOs in Honduras, Rwanda and Indonesia also struggled with these criteria,
each with less than half of them complying with 18 or more indicators.
For those sets of indicators designed to monitor improvements in reaching a broader portion of
the network of producers within a PSO, Starbucks should consider revisiting the establishedthresholds as there was often no significant change in performance between the 5 and 15 percent
thresholds. For instance, Starbucks could choose to keep an entry-level threshold (perhaps of 10
percent) and then reward PSOs that extend their services to a wider range of smallholders in their
supply chain on a continuous improvement basis. The only exception to this was in the training
indicators, where there appeared to be a significant barrier to extending this service to a larger
extent of smallholders. This is most likely due to the human and capital investments required toprovide training to a large portion of the PSO networks. The way in which these indicators are
written does not enable the recognition of those PSOs providing the specified services tosignificantly more than 25 percent of the producers in their networks. Starbucks might consider
collecting information on the percent of producers receiving these services and then scoring thembased on the indicators established. This would allow the company to revisit the thresholds overtime and determine when the top one needs to be expanded to 35 percent or higher. It would alsoenable Starbucks to recognize those PSOs greatly exceeding the expectations set within theguidelines.
Starbucks might also consider creating a standardized list of type of entity that serve the PSO
function within C.A.F.E. Practices. Within the current framework, a verifier can use any term(in any language) to describe the PSO. The lack of standard categories does not allow us todetermine whether there is any correlation between the type of entity providing this service andthe compliance rates achieved. In the future it would be interesting to see whether cooperatives,
exporters, or mills appear to provide the highest level of service to networks of small farmers.

6. Wet and Dry Mills - Coffee Processing
In addition to verifying social and environmental performance of suppliers for coffee growing,
C.A.F.E. Practices also includes guidelines, criteria and indicators for coffee processing.
Suppliers use a range of processes to convert the coffee cherry into green coffee ready for export
to Starbucks. These include small-scale wet processing on small farms, larger-scale wet
processing, and dry processing. All of the social indicators analyzed in the coffee growing
29

The one indicator where Costa Rica did not achieve 50 percent compliance was PS-MT1.5 – maintaining farminspection reports along with a map and description of the farm.

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March 2011section of this report also apply to mills. In addition, specific environmental indicators are used
to verify the performance of wet and dry mills on water use and discharge, waste management,
and energy conservation. Similar to the coffee growing section of the guidelines, verifiers use
only a subset of indicators in evaluating processing conducted on small farms.

6.1 Methodology
Coffee processing data underwent a somewhat different process from that used in the coffee
growing section due to some unique features of the program. For instance, the environmental
indicators for wet and dry mills have their own codes and thus do not overlap, although the social
responsibility indicators are the same. To address these variations in supply systems, we firstcategorized all mill reports according to 4 categories: small farms doing wet processing; wetmills; dry mills, and; mills doing both wet and dry processing. In cases where a single mill did
both wet and dry processing, we counted the mill twice – once as wet and once as dry - in theenvironmental section as the indicators applicable for the coffee processing section are uniquely
coded for whether wet or dry processing took place at the facility. Social responsibility data wasanalyzed only for the stand-alone mills, as the working conditions and hiring practices on small
farms were already analyzed in the farm section of this report.
In cases where the environmental indicators were the same for wet and dry milling but given
separate codes, we combined them into a single indicator grouping for reporting purposes. We
extrapolated performance of on-farm wet processing based on mill counts provided by thecoffee agronomy database, as only a sample of small farms were actually verified and thenumber of farms with on-site wet milling was not captured in the VRS. We double-checked allof the data for duplicate mill entries, as several mills processed coffee for multiple applications,and removed these duplicates for the purposes of analyzing indicator-level compliance rates.30
Two additional mill records were excluded from the analysis based on errors in recording themill type included in the application (e.g. all indicators were marked not applicable). For more
information on the methodology, see Appendix E.

6.2 General Participation Data
Number of mills by type
Number of workers employed by stand-alone mills
In FY08 54,375 operations reported processing coffee. (See Table 21.) The vast majority ofthese (53,400) were small farms that conducted some wet-milling prior to transporting the coffee
to the cooperative or export company for further processing. This represented 38 percent ofsmall farms participating in the program in FY08. There were a couple of countries (e.g. Costa
Rica, Bolivia) where no on-farm processing of coffee was done by small-scale growers.
The remaining 975 operations verified against the coffee processing indicators were wet and drymills that either processed coffee produced by multiple farms (including coffee produced bysmall farms) or coffee produced by a medium or large farm. These mills may process coffee
from cooperatives or multiple supply chains, and hence applications. We refer to these as“stand-alone” mills for the purposes of this analysis. Of the stand-alone mills, the majority (e.g.
30Removing these duplicates could affect the results of the analysis if the compliance rating for practices varied
across the reports. We randomly deleted the duplicate records to minimize the effects of this on the results reported.

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73 percent) did wet coffee processing. Only a small percentage (20 percent) did dry processing.
An additional 69 mills did both wet and dry processing.
Mill TypeWet - stand alone

NumberParticipating
715

Wet - on farm

53,400Wet and Dry

69

Dry

191

Total

54,375

Table 21: Number of mills participating in the program in FY08

The 975 stand-alone mills participating in C.A.F.E. Practices span 18 countries and the numberof mills participating in any particular country varies from just one in Bolivia to 211 in
Nicaragua. (See Table 22.) For most countries the number of wet mills exceeded the number ofdry mills. The only exception occurred in Indonesia which had 89 dry mills and only 55 wet.
Some countries appear to have consolidated coffee milling substantially, with a few mills having
the capacity to conduct both wet and dry processing handling the majority of the coffee. This
was the case in Bolivia, Zambia, Costa Rica, El Salvador and Papua New Guinea.
Country
Bolivia
BurundiColombia
Costa Rica
El Salvador
Ethiopia
GuatemalaHonduras
Indonesia
Kenya
MexicoNicaragua
PanamaPapua New Guinea
Peru
RwandaTanzania
ZambiaGrand Total

Wet
5
30
4
10
19
189
43
55
8
51
187
1
5
46
10
52
715

Dry
3
11
3
3
12
10
6
89
1
5
24
1
2
13
4
4
191

Both
1
35
13
3
6
1
2
6
2
69

Total
1
8
41
42
26
34
205
50
144
9
58
211
2
13
59
14
56
2
975

Table 22: Number of stand-alone mills participating in C.A.F.E. Practices in FY08 by country

Stand-alone mills participating in C.A.F.E. Practices employed 44,445 workers in FY08, the vast
majority of which were seasonal or temporary employees who worked during the harvest season.
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(See Table 23.) These figures include only stand-alone mills as the on-farm labor forsmallholder processing was captured in the farm analysis.
Mill Type
Full-TimePart-Time
Temporary
Total
Wet
1,165
397
13,026
14,588
Dry
3,289
776
18,58322,648
Both
1,098
2,800
3,311
7,209
Total
5,552
3,973
34,92044,445
Table 23: Number of workers employed by stand-alone mills participating in C.A.F.E. Practices in
FY08

6.3 Social Responsibility (stand-alone mills)
As seen above, mills employ large numbers of workers and C.A.F.E. Practices verification looks
at mill performance across a range of social responsibility indicators. The indicators used formills are the same as those used to verify farms and as such the particular indicators and analyses
presented here mirror those presented in the farm section of this report. This section analyzes
performance of only the 975 stand-alone mills and does not include labor practices on small
farms that process coffee using wet milling processes, as the social responsibility data for thesefarms was already included in the coffee growing section of this report.
6.3.1 Minimum labor standards
Number of mills where child labor occurred
Number of mills where forced or bonded labor occurred
Number of mills where minimum wage was not paid to workers
Number of mills non-compliant with more than one of the minimum labor standards
Of the mills verified in FY08, 25 failed to comply with at least one of the zero tolerance
indicators, resulting in a non-compliant status for the application. (See Table 24.) None of themills that did both wet and dry processing failed to comply with these indicators. Wet mills had
the highest number of incidents and across a larger number of the indicators. This may be due tothe significantly larger number of wet mills verified.
The indicator with the most incidents of non-compliance across both types of mills was thefailure to pay seasonal or temporary workers the legally established minimum wage. This wasfollowed by the failure to pay full-time workers the minimum wage. Two wet mills, one in Peru
and another in Nicaragua, had incidents of child labor. The majority of the mills failing tocomply with the zero tolerance indicators were from Burundi (4 applications) and Honduras (7applications). In the case of Burundi all of the associated applications received a non-compliant
status and no coffee was purchased from that country as part of the C.A.F.E. Practices program.

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Wet Mills

Dry Mills

SR-HP1.1

All full-time workers are paid the nationally or
regionally established minimum wage

6

1

SR-HP1.2

All part-time workers are paid the nationally or
regionally established minimum wage

2

0

12

3

2

0

0

0

5

0

SR-HP1.3
SR-HP4.1
SR-HP4.2
SR-HP4.3

All temporary/seasonal workers are paid thenationally or regionally established minimum wage
Employer does not directly contract any persons
under the age of 14
Employment of authorized minors older than 14
does not conflict with their access to education
Management has an enforced policy prohibiting
discrimination on the basis of gender, race,
ethnicity, age or religion as per ILO Convention 111

Employer prohibits the use of forced, bonded,0
indentured or involuntary convict labor
Table 24: Incidents of non-compliance on participating mills by indicator in FY08
SR-HP4.4

0

A small number of wet mills failed to comply with multiple zero tolerance indicators. (See Table25.) One mill in Nicaragua failed to comply with three of these indicators, two regarding
minimum wage and one on child labor. Four mills (3 from Honduras, 1 from Burundi) failed tocomply with two indicators. The majority of these were based on a failure to pay workers theminimum wage across 2 worker categories. One, however, was due to a minimum wage issue aswell lacking a policy prohibiting discrimination in the workplace.
No. of mills NC with:
Wet Mills
Dry Mills
Total
3 ZT Indicators
10
1
2 ZT Indicators
40
4
1 ZT Indicator
16
4
20
Table 25: FY08 Mill incidence of non-compliance with zero tolerance indicators

On the more positive side, these incidents of non-compliance with minimum labor standards
represent a very minor proportion of mills participating in the program. In FY08 compliance
rates were 98 percent and higher for each of these indicators. (See Table 26.) All mills auditedensured that any employment of minors over 14 did not conflict with their access to educationand there were no incidents of forced, bonded, indentured or involuntary labor. Dry mills alsoachieved 100 percent compliance for paying part-time workers the minimum wage and notcontracting any persons under 14 years of age. Mills that did both wet and dry processing had
no incidents of non-compliance with any of the zero tolerance indicators.

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Indicator
SR-HP1.1
SR-HP1.2
SR-HP1.3
SR-HP4.1

All full-time workers are paid the nationallyor regionally established minimum wage
All part-time workers are paid the nationallyor regionally established minimum wage
All temporary/seasonal workers are paid
the nationally or regionally establishedminimum wage
Employer does not directly contract any
persons under the age of 14

March 2011

Wet

Dry

Both

All

98.2%

99.4%

100%

98.8%

97.4%

100%

100%

98.7%

98.0%

98.1%

100%

98.2%

99.7%

100%

100%

99.8%

SR-HP4.2

Employment of authorized minors older
than 14 does not conflict with their accessto education

100%

100%

100%

100%

SR-HP4.3

Management has an enforced policy
prohibiting discrimination on the basis ofgender, race, ethnicity, age or religion as
per ILO Convention 111

99.2%

100%

100%

99.5%

SR-HP4.4

Employer prohibits the use of forced,
bonded, indentured or involuntary convict
labor

100%

100.0%

100%

100%

Table 26: Mill compliance rates for zero tolerance indicators in FY08

6.3.2 Improved compensation
Number of mill workers paid at and above minimum wage
Number of mill workers paid a living wage
Percentage of mills paying overtime at or above the legal minimum requirement
As noted above the overwhelming majority of mills participating in the program paid theirworkers at least the minimum wage in FY08. This benefitted 39,815 workers, the majority ofwhich were temporary workers employed by the wet and dry mills. The vast majority of mills
participating in the program paid their workers more than the minimum wage, regardless ofwhether they were full-time, part-time or seasonal. (See Table 27.) For instance 23,871 workers
received wages that exceeded the legal minimum, representing over half of the total mill
workers. Dry mills had the highest compliance rates across all worker types except for
temporary workers. Interestingly, dry mills were more likely to pay their part-time workers
wages that exceeded the legal minimum than either full-time or temporary workers. This may be
due to the relatively low number of part-time workers employed by these particular mills.

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Indicator

Wet

Dry

All full-time workers are paid more than thenationally or regionally established
80%
85%
minimum wage
All part-time workers are paid more than
SRthe nationally or regionally established76%
98%
HP1.12minimum wage
All temporary/seasonal workers are paid
SRmore than the nationally or regionally82%
73%
HP1.13
established minimum wage
SRFull-time workers are paid at least a living
83%%
87%
HP1.14
wage
Table 27: Percentage of mills paying above the minimum wage in FY08
SRHP1.11

Both

All

84%

82%

76%

84%

75%

80%

92%

85%

In addition, participating mills paid 4307 full-time workers a living wage. (See Table 28.) Aswith the coffee growing data, the number of mills reporting that they used full-time labor isinconsistent between the two indicators used to assess wages paid to these workers. For
instance, 559 mills reported employing full-time labor according to indicator SR-HP1.11,
whereas 594 reported doing so for indicator SR-HP1.14, and both figures are different from thenumber of mills reporting on SR-HP1.1. The range of reporting differs by 35 mills, or roughly 6
percent of the population using the 594 figure as the population size. This is another area where
the VRS could be used to ensure greater consistency in reporting across these related indicators.
For instance, if a mill reported using full-time workers, the verifier would be required to reporton all three of these indicators.
Mill Type

Indicator

Full-TimePart-Time

Temporary

Greater than minimum wage
876
295
Living wage
795
NA
Greater than minimum wage
2435
721
Dry
Living wage
2686
NA
Greater than minimum wage
937
342
Both
Living wage
826
NA
Greater than minimum wage
4248
1358
Total
Living wage
4307
NA
Table 28: Number of mill workers earning wages above the legal minimum
Wet

7423
NA
8326
NA
2516
NA
18,265
NA

Total
8594
795
11482
2686
3795
826
23,8714307

All participating mills in Bolivia, Mexico, Panama, Papua New Guinea and Peru paid full-timeworkers a living wage. (See Figure 32.) This is interesting in that for some of these countries
compliance with the living wage indicator was greater than the minimum wage compliance rate
for full-time workers. This could be due to minimum wage standards in some countries being
based on living wage standards, but this assumption should be further tested to ensure verifiers
understand the difference between these indicators. Zambia had no mills paying workers wages
that exceeded the minimum, but this was the only country where this was the case.

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100%
80%
60%
40%

Living Wage, Full-Time> Min Wage, Full-Time

20%
0%> Min Wage, Part-Time> Min Wage, Temporary

Figure 32: Percentage of mills paying workers more than minimum wage, by country in FY08

The majority of mills participating in the program also paid overtime wages that met the national
legal requirements (81 percent), with wet and dry mills achieving compliance rates 81 and 75
percent, respectively. Mills that did both wet and dry processing greatly outperformed mills only
doing one type of processing with a compliance rate of 91 percent. However, these percentages
are cut in half when asked if overtime pay exceeded the legal requirements. These sharp drops inpercentages are also reflected in the country level analyses although the wet mill data showssome exceptions. For instance, Colombian wet mills achieved 100 percent compliance forpaying overtime at the legal rate and also for exceeding these levels. Indonesian wet mills also
demonstrated consistency in this regard with 96 percent compliance for both indicators.
6.3.3 Worker benefits
Number of workers receiving legally mandated benefits
Number of full-time workers receiving pension plans that exceed the national standard
Number of workers receiving paid sick leave
Number of workers receiving paid vacation
Percentage of mills where hours worked by employees did not exceed legal requirements
Legally mandated benefits: The vast majority of mills participating in the program paid thelegally mandated benefits to their workers, regardless of whether they were full-time, part-timeor temporary. (See Table 29.) Dry mills at 96 percent compliance slightly outperformed wet
mills (at 78%) on these particular indicators, especially for paying these benefits to temporary
employees.

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Wet

Dry

Both

All

SR-HP1.6

If nationally legally mandated benefits
are required for full-time workers, thenthese are paid by employer

94%

98%

100%

96%

SR-HP1.7

If nationally legally mandated benefits
are required for part-time workers, thenthese are paid by employer

95%

100%

93%

95%

SR-HP1.8

If nationally legally mandated benefits
are required for seasonal/temporary
workers, then these are paid by employer

78%

96%

95%

83%

Table 29: Percentage of mills providing legally-mandated benefits to workers

These compliance rates resulted in 21,154 workers receiving the required benefits. (See Table30.) The majority of these workers were temporary or seasonal employees. Participating mills
in El Salvador and Panama achieved 100 percent compliance for full-time, part-time andtemporary workers. Mills in Mexico, struggled the most to achieve compliance for paying thesebenefits to workers, with the lowest rate of compliance for full-time and temporary workers. .
Type of Worker
Wet
Dry
Both
Total
Full-Time
973
3077
1019
5069
Part-Time
317
534
332
1183
Temporary
6949
10,7042249
19,902
Total
8239
14,3153600
26,154
Table 30: Number of mill workers receiving legally-mandated benefits in FY08

When considering only wet mills, Bolivia, El Salvador, Kenya, Panama, Rwanda and Tanzania
had the highest compliance rates for paying workers the required benefits. (See Table 31.)
Colombia had the lowest level of compliance for providing these benefits to full-time workers at
33 percent, but only 3 mills reported even using full-time employees. Most other countries
achieved compliance rates of 80 percent of higher for full-time workers. When looking at
temporary workers, Mexican wet mills had the lowest levels of compliance, with only 11 percent(e.g. 2 of 18 mills) paying these benefits to their workers. All of the other countries were above
67 percent compliance for this particular indicator. Starbucks may wish to further explore thereason for low performance of wet mills in both Mexico and Colombia.

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Country
Bolivia
BurundiColombia
Costa Rica
El Salvador
Ethiopia
GuatemalaHonduras
Indonesia
Kenya
MexicoNicaragua
PanamaPapua New Guinea
Peru
RwandaTanzania
ZambiaMarch 2011

Full-TimePart-Time
Temporary
Dry
Both
Wet
Dry
Both
Wet
Dry
Both
- 100%- 100%
100%
67%
33% 100%- 100%
75% 100%
100% 100% 100%
- 100%
67% 100%
95%
100% 100% 100% 100% 100% 100% 100% 100% 100%
100% 100% 100%
- 100% 100% 100%99% 100% 100%96% 100% 100%93% 100% 100%83% 100% 100% 100% 100% 100%71%
83% 100%96%- 100%
96%
97%
100% 100%- 100%- 100%
60% 100% 100%67% 100%
11%
67% 100%
92% 100%
67%
68%
96%
100% 100%- 100% 100%
- 100%
100% 100% 100%
- 100%
90% 100% 100%
- 100% 100%
100% 100%- 100% 100%
100% 100%- 100% 100%
- 100%
50%
50%

Wet

Table 31: Percentage of mills paying legally-mandated benefits to workers

For dry mills all but 2 countries (Indonesia and Burundi) achieved full compliance for paying thelegally required benefits to full-time workers. All of the countries will mills employing part-timeworkers achieved 100 percent compliance for this indicator. Compliance for temporary workers
was similarly high with all countries achieving at least 67 percent compliance and 10 countries
with all participating mills in compliance.
Mills that did both dry and wet processing also had high compliance rates across all categories ofworkers. Of those countries having combined wet and dry milling operations, most achieved 100
percent compliance for each worker type although only half of the Zambian mills (n=2) provided
these benefits to part-time and temporary employees.
Pension: While the previous indicators would have included pension plans where they werelegally mandated, this indicator focuses on those mills providing pension plans that exceed theseminimum legal requirements to full-time workers. As such the compliance rates are expectedly
low, with dry mills achieving 35 percent compliance and wet mills at 22 percent. Mills that did
both wet and dry processing, however, had much higher compliance rates averaging 52 percent.
Bolivia achieved a 100 percent compliance rate for this indicator, but only had one mill ( doing
both wet and dry processing) participating in the program for the given year. For wet mills
compliance rates ranged from 100% compliance in Panama (n=1) to zero percent compliance inIndonesia (n=13) and Kenya (n=8). (See Table 32.) Nicaragua and Guatemala had the highest
number of wet mills verified in FY08 and their compliance rates varied significantly at 2 and 35
percent, respectively. All participating dry mills in Papua New Guinea (n=4) and Kenya (n=1)
achieved 100 percent compliance, whereas none in Panama (n=1) or Tanzania (n=4) did.
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Indonesia had the highest number of dry mills reporting on this indicator and had a somewhat
low level of compliance. Those mills doing both types of processing had a wide range ofcompliance – from 0 percent in Honduras and Zambia to 100 percent in Bolivia and Ethiopia.
Country
Bolivia
BurundiColombia
Costa Rica
El Salvador
Ethiopia
GuatemalaHonduras
Indonesia
Kenya
MexicoNicaragua
PanamaPapua New Guinea
Peru
RwandaTanzania
Zambia

Wet

Dry

20%
67%
50%
50%
37%
35%
11%
0%
0%
18%
2%
100%
40%
20%
20%
43%
-

33%
60%
67%
33%
36%
44%
50%
23%
100%
60%
17%
0%
100%
55%
75%
0%
-

Both
100%
52%
62%
100%
33%
0%
50%
50%
0%

Table 32: Percentage of mills providing pension plans exceeding legal requirements to full-timeworkers

Paid sick leave program: The majority of mills paid sick leave to full-time workers, withaverage compliance rates of 85 percent for wet mills, 97 percent for dry, and 96 percent for mills
doing both. These percentages dropped precipitously when extended to include all workers
employed by the mills, with 47 percent compliance for wet mills, 50 percent for dry and 83
percent for mills doing both. Based on these compliance rates, one can conclude that 17,504 mill
workers received paid sick leave benefits in FY08. However, there is a large discrepancy in thenumber of workers receiving sick leave benefits according to SR-HP3.5 which looks at only fulltime workers and SR-HP3.6 which includes all workers. When only full-time workers were
included in the analysis of both indicators, a much higher number of workers appear to bereceiving paid sick leave based on compliance rates achieved for SR-HP3.5 than for SR-HP3.6.
For instance 5373 full-time workers received paid sick leave according to SR-HP3.5, while only
2892 full-time workers received this benefit according to SR-HP3.6. The same trend occurs for
wet mills as well. The reason for this is unclear, but may be due to a difference in the number ofmills reporting on these indicators.
Analysis of the data by country shows a high level of compliance for providing paid sick leave tofull-time workers in most countries, especially for dry mills where only 3 of the 16 countries
reporting on this indicator had compliance rates below 100 percent, and the lowest rate was 90
percent in Nicaragua. Wet mills also had high compliance levels across all countries, but a lower
number of countries (e.g. 7) achieved 100 percent compliance, and Honduras had the lowest
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level at 62 percent, which is still quite high. Mills doing both wet and dry processing had thewidest range of performance when looking at country level performance, with 6 countries
achieving 100 percent compliance, including Costa Rica with 36 mills reporting. However, thesole mill doing both wet and dry processing in Honduras was not in compliance with thisindicator.
When the threshold is raised to include all workers compliance rates dropped for most countries,
as expected. However, El Salvador and Panama continued to achieve 100 percent complianceacross all mill types. Burundi, Ethiopia and Kenya had no wet or dry mills providing paid sickleave to all of their workers.
Annual leave (vacation): Most mills provided annual leave benefits that met legal requirements,
with 90 percent complying with this particular indicator. In FY08, 40,912 workers were
employed by mills that had annual leave programs that complied with local laws. Dry mills andmills doing both wet and dry processing were 10 percent more likely to provide vacation toworkers than wet mills. The country data shows compliance rates ranging from 100 percent for
5 countries to 53 percent for Tanzania. Of the 16 countries participating, 12 achieved 100
percent compliance for this indicator across all of their dry mills – a very high level of
performance. Countries with mills doing both wet and dry processing also had high rates ofcompliance, although the range of scores varied the greatest within this sub-category: 6 countries
achieved 100 percent compliance whereas Honduras’ sole mill did not comply with thisindicator.
Hours worked per week: Most mills ensured that their workers did not work more total hours ina day or week than is permitted under local laws (SR-HP3.3). For instance, 91 percent of allmills complied with this indicator and there was no significant difference in compliance rates
between the 3 types of mills. Wet mills did have a slightly higher compliance rate than theothers, but only by 3 to 4 percentage points. Country level results show that 6 of the 18 countries
achieved 100 percent compliance across all mill types. However, in East Africa, mills in
Tanzania and Burundi had compliance rates of 19 and 25 percent, respectively. In Tanzania thiswas mostly due to a very small percentage of wet mills in compliance with the legal
requirements. Across the 18 countries there was no evident trend in one type of mill being more
likely to comply with this indicator than another.
6.3.4 Worker living conditions
Number of mills offering housing to a portion of their workers
Number of workers with access to potable water
Housing: The majority of mills participating in the program offered housing to some of theirworkers. Where this was the case, the housing was deemed habitable for the overwhelming
majority. Mills that did both wet and dry processing had 100 percent compliance with thisindicator, while 98 percent of dry mills also complied. Wet mills had the lowest compliance
rates of 93 percent. Looking at compliance rates by country shows that 11 countries had all of
their mills complying with this indicator. Ethiopian wet mills had the lowest performance, with
71 percent offering habitable dwellings to the workers living on site – a figure that is still quite
high.

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In the future Starbucks might consider collecting information on the number of workers living onsite and the percentage of the workforce that this figure represents. This will enable reporting on
the number of workers benefitting from this provision, which may be useful for communications
purposes. Further definition of “habitable” (e.g. cement vs. dirt floors) may also be needed forthis indicator if public communication of the results were to be made.
Potable Water: Mills doing both wet and dry processing proved the most likely to provide
potable water to workers, with these mills achieving 98 percent compliance. Dry mills were thesecond most likely at 96 percent, followed by wet mills at 84 percent. The correlation between
wet milling and lack of potable water for 16 percent of mills may be due to the remote locationsof the wet mills, but Starbucks may wish to undertake further investigation of this issue to ensurethat the wet milling process is not responsible for the lack of potable water at these sites. These
compliance rates are all lower than the compliance rates achieved for the housing indicatorwhich is somewhat surprising, although the number of farms reporting is higher across all 3
categories of mills. It is interesting to note that for all 3 types of mills there were some for which
this indicator was not- applicable. This is also surprising, as one would think that this is one of
the few universally applicable indicators within the guidelines.
Most countries demonstrated high performance on this indicator for dry mills and those doing
both types of processing were analyzed. An exception, however, occurs in the case of Honduras
where the one mill doing both wet and dry processing failed to provide workers with potablewater. Honduras also had the lowest level of compliance among all the countries with wet mills
by a significant margin (26 percent below Nicaragua, the next closest country). Further
investigation into wet milling practices in Honduras may be warranted to try and improve theirperformance and determine whether there is a correlation between the wet milling process andthe lack of potable water available to the workers.
6.3.5 Access to education
Percentage of mills located in areas with insufficient access to education providing primaryor secondary education to children of workers living on site
Percentage of mills located in areas with convenient access to education supporting schools
through in-kind or financial support
Percentage of mills providing direct incentives for education
Mills in locations with insufficient access to education: For 38 percent of mills there wasinsufficient access to primary schools for children living on site. This figure drops to 32 percent
when secondary schools are considered. However, these figures do not necessarily match thenumber of mills reporting on indicator SR-WC2.4 which asks whether the mill is located in anarea that has convenient access to education. This is an important data point and efforts should
be taken to reconcile the number of mills reporting on these indicators to ensure accuracy.
The actual number of children living on site of school age, however is not collected during theverification process and thus is unknown. This is a data point that might be considered forinclusion in future audits as it could greatly increase the effectiveness of public communications
and would also enable the significance of this indicator to be assessed (e.g. How many children
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Access to supplementary education where no local school is available: Where access topublic education was insufficient for the children of workers living on-site, all of the dry mills
and those mills doing both wet and dry processing provided supplementary schooling to thesechildren. Of the wet mills where this was the case, 98 percent provided supplementary schoolingfor children of primary school age, but this figure dropped to 85 percent for secondary schooling.
Guatemala and Mexico were the only countries failing to achieve 100 percent compliance forthis indicator for children of primary school age. Neither of the 2 Ethiopian wet mills located inareas without secondary schools provided supplementary education for children living on thesite. This was clearly the exception among the countries with wet mills, as all of the others
achieved compliance rates of over 78 percent.
In-kind or financial support to local schools: Over half (e.g. 57 percent) of all the mills
participating in the program in FY08 provided some support to local schools. Mills that did both
dry and wet processing were the most likely to support these institutions, with an 84 percent
compliance rate. Dry mills were the least likely to do so, but 37 percent of them did provide
support to local schools. Wet mills were 23 percent more likely than dry mills to support local
schools. These trends are also reflected in the country level analysis, although in Kenya,
Mexico, Peru and Tanzania dry mills were more likely than wet mills to support local schools
through financial contributions. Compliance rates ranged considerably for this indicator from 13
percent in Burundi to 100 percent in Bolivia and Zambia. The high compliance rates for Bolivia
and Zambia are most likely due to each country having only a single mill participating in the
program in FY08.
Direct incentives for education: Across all of the participating mills, 53 percent provided direct
incentives for education via scholarships, materials, books, transportation or food. Once again
mills doing both wet and dry processing achieved the highest compliance rate (74 percent)followed by wet mills (57 percent) and dry mills (35 percent). These rates track quite closely tothose reported for in-kind or financial support and this may be due to a certain degree of overlap
in what these indicators are asking. The only difference is that this particular indicator should
apply to all mills, although according to the reporting levels this was not the case across any ofthe mill categories. Thus, there may be some confusion among verifiers as to when these
indicators apply to a given mill and further guidance and training may be required. The VRS
could be used to rectify this by requiring verifiers to report against this particular indicator for all
mills.
Compliance rates varied greatly across the 18 participating countries. Mills in Zambia andBolivia achieved 100 percent compliance for this indicator, and Papua New Guinea, Peru and
Kenya each had compliance rates above 75 percent. None of the mills in Burundi provided
direct incentives to schools, and mills in Indonesia and Rwanda also had low levels ofcompliance (e.g. less than 20 percent).
6.3.6 Access to medical care
Percentage of mills with convenient access to public medical care
Percentage of mills supporting local medical facilities through in-kind or financial support
Percentage of mills offsetting the cost of health services for workers
Access to medical care: Most of the mills verified were located in areas with convenient accessto medical care. Only 155 mills, or 16 percent, were located in areas that lacked convenient82

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access to medical care (based on the mills reporting that indicator SR-WC3.3 was notapplicable). The vast majority (e.g. 88 percent) of these were wet mills. Across the countries
participating in the program, Colombia and Mexico had the highest percentage of mills locatedin areas lacking convenient access to medical care. (See Figure 33.) Conversely, all of the millsin Bolivia, Burundi, El Salvador, Panama, Papua New Guinea, Rwanda and Zambia were inlocations where medical care was available.

100%
80%
60%
40%
20%
0%

Figure 33: Percentage of mills lacking convenient access to medical care by country

In-kind or financial support to local medical facilities: Of those mills with access to medicalcare, 36 percent provided in-kind or financial support to these facilities. Mills that did both wet
and dry processing had a significantly higher compliance rate (e.g. 62 percent) than those only
doing wet or dry processing (36 and 30 percent, respectively).
Bolivia, Burundi, Panama, Rwanda and Zambia all achieved 100 percent compliance with thisindicator across all mill types. At the other end of the spectrum, Colombia had the lowest
compliance rate of only 13 percent. The only other country performing below 50 percent was
Mexico (at 25 percent) and this was due to a very low rate of compliance among wet mills in thatcountry.
Offsetting costs of health services: Verifiers indicate whether mills are offsetting the costs ofhealth care for full-time workers as well as for all types of workers. As might be expected, mills
were much more likely to do so for full-time workers than for all workers. The vast majority ofmills (e.g. 92 percent) provided these benefits to full-time workers, with little variation by mill
type. These rates drop significantly for dry and wet mills when all workers are considered.
However, wet/dry mills continue to perform well with a compliance rate of 92 percent, a rate
only 6 percentage points lower than that achieved for full-time workers. It is interesting to notethat 81 mills reported not applicable for SR-WC3.6. It is not clear why verifiers would mark thisindicator NA, unless they thought it did not apply in countries where government covers thecosts of health care. Starbucks may wish to discuss this issue with verifiers during subsequent
training sessions.
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Five countries achieved 100 percent compliance for offsetting the costs of health services forfull-time workers and all countries had compliance rates above 78 percent. When expanded toinclude part-time and temporary workers the range increased greatly with Tanzania at 7 percent
and Bolivia and Panama at 100 percent. Both Bolivia and Panama only had a single millparticipating in the program. Guatemala with 179 wet mills, 9 dry mills and 6 mills doing both
had a high average compliance rate of 89 percent when all workers are considered, which is quite
significant. Peru also had a high level of participation with an average of 97 percent compliancefor all workers.
6.3.7 Coffee Processing - Social Responsibility Conclusions and Recommendations
Mills achieved compliance rates of over 90 percent for 30 of the 71 indicators used to verifysocial responsibility practices. Only 9 had compliance rates below 50 percent. Within the Hiring
Practices section, mills had low performance levels for 6 indicators, most of which were related
to the existence of workers associations and collective bargaining agreements. Other indicatorswith low performance levels were those asking if mills paid overtime and pension benefits thatexceeded the legal requirements. Within the Working Conditions section mills had averagecompliance rates below 50 percent for only 3 indicators. Two of these asked for written policies
and records for training and safety procedures. The other assessed if mills supported local
medical facilities. Thus, most mills complied with the majority social responsibility indicators.
The exceptions to this occurred for indicators requiring additional paperwork or financial
outlays.
Wet/dry mills consistently achieved the highest compliance rates across the range of social
responsibility indicators used to evaluate mills participating in the program, sometimes
significantly outperforming those that only did only wet or dry processing. This may be due tothe size of these operations and the financial resources available to them. Another factor might
be the smaller number of wet/dry mills included in the population size, which was significantly
lower than that included in the wet or dry categories. Wet mills represented the largest
proportion of mills in the program and had lower compliance rates across most of the indicators,
and most significantly among the zero tolerance indicators. The wet mills also had the lowest
performance on the potable water indicator, which may warrant further investigation todetermine whether this is due to the quality of water being discharged from the mill. The resultsof this analysis show a clear need for additional outreach and training on social responsibility
among participating wet mills.
The access to medical care and education sections might warrant the consideration of additional
indicators in future versions of the standards. For instance, it would be good to include
information on the distance to clinics and schools. Another consideration would be to include
some information on the level of investments made into these facilities, as currently a millinvesting $1.00 in a clinic would receive the same score as one investing 1 percent of its profits.
Related to education, it would also be good to collect information on the number of children
living in housing provided by the mill. Furthermore, on the housing indicator it would be nice toknow the percentage of employees provided housing.
Reporting consistency continued to be an issue for mills in the social responsibility section of thescorecard. The VRS can play a significant role in policing reporting to ensure for consistency inthe number of mills reporting on indicators that are closely related. The indicators might also be
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further reviewed to identify those for which all mills should be reporting (e.g. access to potablewater) and this guidance should be provided as part of a revised verifier training curriculum.
Only a portion of the social responsibility indicators included in the C.A.F.E. Practices
guidelines were analyzed in the above section, although compliance rates for all of them areavailable in Appendix F.

6.4 Coffee Processing – Environmental Responsibility
The Environmental Responsibility indicators for coffee processing included in the C.A.F.E.
Practices scorecard focus on water conservation, waste management, and energy conservation.
For each of these focal areas we identified and analyzed key indicators for use in monitoring mill
performance over time. The C.A.F.E. Practices program distinguishes between wet processingthat takes place on small farms and at a stand-alone mill.31 Verifiers use only a subset ofindicators to evaluate the environmental performance of smallholder wet milling, which present
some challenges when reporting on the environmental performance of wet mills as a whole.
Due to the sampling that occurs as part of the group certification process for small farms, thesmallholder wet milling results are extrapolated to the population of small farms in a given
application based on data provided at the application level regarding the number of smallholders
that processed coffee.32 In some cases the number of small farms with on-site wet milling is notavailable which resulted in the exclusion of 76 applications – and 2 countries (El Salvador and
Honduras) from the analysis. In addition, not all of the applications that included smallholders
who conducted some wet processing on their farms have reports for the coffee processing
indicators as some were verified off harvest when no coffee was processed. Thus, while there
are a reported 53,400 smallholders who conduct wet processing on the farm, verification reports
represent only 34,075 small farms, or 64 percent.
In reporting the results, we do not distinguish between mills only doing wet or dry processing
and those doing both, as the indicators are unique for each type of mill and counting these mills
in both does not distort the results as it would have done in the social responsibility section.
6.4.1 Wet Coffee Processing
Each of the 18 countries represented in the program in FY08 applied wet milling practices toconvert the coffee cherry into parchment coffee. In half of these countries (Colombia, Costa
Rica, Guatemala, Indonesia, Mexico, Nicaragua, Peru, El Salvador and Honduras) a portion of
the wet processing took place on small farms. However, smallholder wet milling performance
was not verified in Costa Rica, El Salvador or Honduras as the verifications did not occur during
the harvest season. Colombia had the highest number of smallholders conducting some wet
processing on their farms, a figure that dwarfed that of any other country. (See Table 33.)
Guatemala and Nicaragua had the highest number of stand-alone mills verified in FY08.

31Stand-alone mills are those assigned an identification code beginning with an “M”. Small farms conducting wet
processing are identified according to their farm identification number (beginning with an “F”).
32
The extrapolation methodology is available in Appendix E.

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Wet
Total
Small
Stand-Alone
Bolivia1
1
Burundi55
Colombia
18,76018,79030
Costa Rica
NA*
39
39
El Salvador
NA*
23
23
Ethiopia
22
22
Guatemala
2050
2245
195
HondurasNA*
44
44
Indonesia
2501
2556
55
Kenya
8
8
Mexico4711
4764
53
Nicaragua
1942
2129
187
Panama1
1
Papua New Guinea
11
11
Peru
4111
4157
46
Rwanda10
10
Tanzania
52
52
Zambia2
2
Total
34,075784
34,859
Table 33: Number of wet mill facilities by country and typeCountry

NA = not-available

6.4.1.1 Water Conservation
Percentage of wet mills that have reduced their water use
Percentage of mills recycling water
Percentage of mills achieving a coffee cherry/water ratio of less than 1:1
Percentage of mills conducting water testing
Percentage of mills producing no negative impact results from wastewater management
Percentage of mills that have evidence of no contamination of water bodies from processing
Recording and reducing water use: This indicator applied to all facilities conducting wet
processing, including small farms. According to the verification report data, a small proportionof wet mills recorded and tracked the volume of water used to process coffee. This is one
indicator where there was a significant difference in compliance rate according to the type of
facility doing the wet processing: 52 percent of stand-alone mills complied with this practice,whereas only 11 percent of the small farms that processed coffee monitored water use. This
indicator is only applicable to mills processing at least 7500 lbs of green coffee. As such, it did
not apply to over 36,000 small farms that did wet processing on the site and 16 stand-alone wet
mills.
The ability to comply with the indicator asking whether the facility decreased its water use overtime is dependent on whether or not it tracked water use. However, a larger number ofsmallholders received a compliance rating for decreasing their water use than those who tracked
and monitored this indicator. This calls into question the 46 percent of small farms reported tohave reduced their water use over time. The VRS could correct this particular reporting error by86

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only allowing mills that reported compliance on CP-WC1.1 to report a comply rating for CPWC1.4.
Small farms in El Salvador achieved 100 percent compliance with the tracking and reporting
indicator, but most other countries had rates vastly lower with Ethiopia and Rwanda having nosmallholder complying with this indicator. Compliance rates for stand-alone mills also varied
greatly with Bolivia (n=1) achieving 100 percent compliance and Burundi (n=5) and Panama(n=1) having no mills achieving compliance. Guatemala (n=192), Costa Rica (n=38), and El
Salvador (n=23) had compliance rates exceeding 80 percent.
Compliance rates for reductions in water use vary greatly across the countries for both small
farm and stand-alone wet mills. The one stand-alone mill in Bolivia complied with thisindicator, giving the country a 100 percent compliance rate, while in Indonesia all 55 mills were
not in compliance. Guatemala had 143 mills reporting on this indicator and 71 percent of themwere decreasing their water use. Nicaragua, on the other hand, had 162 mills reporting and only
34 (e.g. 21%) reduced their water use. Countries where smallholders did some wet processing
also had a range of compliance levels. Indonesia had no farms demonstrating a reduction inwater used for wet processing, while in Colombia 58 percent of farms reduced their water use.
Water recycling: This indicator applies only to stand-alone wet mills, of which over half (54%)
were implementing some water recycling to transport coffee before and during the pulping
process. All of the mills in Bolivia, El Salvador, Kenya, Rwanda and Zambia practiced water
recycling, but none in Burundi, Indonesia or Panama were doing so. Countries with the highest
number of mills reporting on this indicator had mixed results. For instance, Guatemala, with 187
mills had 80 percent of the mills recycling water, whereas in Nicaragua, only 43 percent of the
166 mills were doing so.
Water testing: When wastewater is discharged from a wet processing facility, 21 percent ofsmall farms and 37 percent of stand-alone mills conducted tests on a monthly basis to monitor
key water quality indicators. Small farm compliance rates were less than 50 percent across mostcountries for this indicator. Colombia at 29 percent had the highest percentage of smallholders
monitoring water quality, while Indonesia had no small farms conducting water quality tests.
Compliance rates for stand-alone wet mills showed a much broader range, with all participatingmills in Bolivia, Colombia, El Salvador and Ethiopia conducting water testing. However, nomills in Burundi, Rwanda or Tanzania were doing so. Guatemala had the highest number ofmills reporting on this indicator (74) and only 26 of these were testing water quality of discharge.
No evidence of negative impacts: This indicator (CP-WC2.4) was applicable only to standalone wet mills, of which 80 percent were found to be compliant. Wet mills in seven countries(with the number of mills reporting ranging from 1 to 39) achieved 100 percent compliance withthis indicator, but in Burundi none of the 5 stand-alone mills were able to meet this standard.
Mexican wet mills also struggled with this indicator as only 40 percent were able to demonstrate
no evidence of negative impacts on neighboring water bodies and streams. Guatemala andNicaragua had the most mills reporting and achieved 80 and 78 percent compliance rates,
respectively.

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6.4.1.2 Waste Management
Percentage of mills employing worm composting
Percentage of mills re-using organic byproducts as soil amendment
Percentage of mills effectively managing wastes to protect the environment
Composting and reuse of organic matter: The majority of small farm and stand-alone wet
mills composted or used worms to decompose organic wastes produced during the wet milling
process. Among the small farms carrying out wet processing on the site, 68 percent werecomposting organic matter, and this figure jumped to 88 percent for stand-alone mills. In 7
countries (Bolivia, Burundi, Costa Rica, Indonesia, Kenya, Panama and Rwanda) all of theparticipating stand-alone wet mills used composting or worms to manage processing wastes.
Some of these countries only had a small number of mills reporting, although in Costa Rica all
39 mills were in compliance. All countries had at least half of the stand-alone mills adopting thisbest practice. Zambia (n=2) and Mexico (n=52) had the lowest percentage of mills applying
composting techniques to process wastes, with compliance rates of 50 and 60 percent,respectively. All of the small farms in Indonesia that processed coffee on the site used thesepractices to effectively manage organic wastes. And all countries where small farms did somewet processing had more than half of the farms applying these techniques. Colombia at 56
percent had the lowest adoption rate.
Both types of wet mills also tended to apply these byproducts as soil amendments on their farmor on local farms located nearby. Once again stand-alone mills were more likely to comply withthis practice as demonstrated by the 95 percent compliance rate compared to 91 percent for small
farm wet mills. Stand-alone wet mills that used sedimentation pools were also likely to recover
the solids and apply them to coffee farms, as demonstrated by a 77 percent compliance rate withthis indicator.
Across all countries stand-alone mills were extremely likely to use wet processing byproducts as
soil amendments on farms. Scores ranged from a low of 80 percent in Papua New Guinea to 100
percent compliance across 9 of the 18 participating countries. Countries in which small farms
did some wet processing were also very likely to apply waste products as soil amendment on
their farms. In Indonesia all of the farms adopted this practice, and Guatemala, Peru andNicaragua were closely behind with compliance rates of over 97 percent. Colombia had thelowest adoption level for this particular indicator, but it was still quite high at over 85 percent.
6.4.1.3 Energy Conservation
Percentage of mills that have reduced energy use
Percentage of mills with commitments to production and/or purchase of renewable energy
Percentage of mills using wood to dry coffee
Percentage of mills using responsible wood sources to dry coffee
Recording and reducing energy use: These 2 indicators (CP-EC1.1 and CP-EC1.5) wereapplicable only to stand-alone mills. Over half of participating wet mills (59%) reported theiruse of energy for processing, and 34 percent demonstrated a decrease in the amount used per
pound of green coffee over time. All of the participating wet mills in Bolivia, Burundi, Panama
and Zambia recorded energy use, but in each case the population of mills was quite low, ranging
from just 1 to 5. Countries with higher populations of wet mills like Guatemala (n=189) and88

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Nicaragua (n=183) demonstrated a wide range in compliance levels. For instance, Guatemala
had 86 percent of mills reporting energy use, but only 36 percent of those in Nicaragua were
doing so. Indonesia, with a population of 54 wet mills reporting on this indicator, had the lowest
percentage of mills tracking energy use at just 9 percent. Most countries, however, hadcompliance rates of over 50 percent. The same high degree of variation occurred for decreasingenergy use over time. Countries with very low numbers of mills reporting on the indicator madeup both extremes of the spectrum, with 3 countries at 100 percent and 3 with no mills complying.
The only exception to this was Indonesia where all 49 mills reporting on this indicator had notdecreased their energy use over time. Colombia with a population of 24 mills reporting on thisindicator had a compliance rate of 92 percent.
Renewable energy: This indicator (CP-EC1.6) was only used to assess stand-alone mills, andon average 53 percent of mills demonstrated a commitment to the production or purchase ofrenewable energy sources. Compliance rates ranged from 100 percent for Bolivia, Panama andPeru to no mills in compliance in Burundi, Indonesia, and Kenya. To enable improved reporting,
Starbucks might consider tracking the types of renewable energy sources used by these mills.
Wood fuel sourcing: Of those stand-alone wet mills that used wood to dry coffee 99.5 percent
ensured that it came from prunings of shade trees, responsibly managed forests or other minimal
impact harvesting practices. Mills in 9 countries reported using wood to dry coffee, and 8 ofthese had all mills in compliance with this practice. Costa Rica was the only country with lessthan full compliance, but in this case there was only one mill using other sources of wood for
fuel. Given the high rates of compliance, Starbucks could consider making this indicator a zerotolerance indicator as the use of wood from unmanaged natural forests can be a driver of
deforestation in biologically-rich coffee production landscapes.

6.4.2 Dry Coffee Processing
Dry coffee processing takes place in stand-alone mills that may be associated with a large farm,an export company or a cooperative. In FY08, 260 dry mills underwent verification, 69 of which
did wet processing as well. Indonesia had the most dry mills participating in the program. (SeeTable 34.) In other countries with high rates of participation in C.A.F.E. Practices as
demonstrated by the number of applications received the dry processing appeared to be much
more consolidated at a small number of mills.
Key environmental concerns related to dry milling include waste management, especially as
concerns the parchment hulls that are generated during the process, and the use and source ofenergy necessary to carry out the process.

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Dry

Bolivia1
Burundi3
Colombia
11
Costa Rica38
El Salvador
16
Ethiopia
15
Guatemala16
Honduras
7
Indonesia
89
Kenya
1
Mexico7
Nicaragua
24
Panama1
Papua New Guinea
8
Peru
13
Rwanda4
Tanzania
4
Zambia2
Total
260
Table 34: Participating dry mills by country

6.4.2.1 Waste Management
Percentage of mills reusing parchment hulls
Reuse of parchment hulls: In FY08, 95 percent of dry mills used parchment hulls to fueldryers, generate energy or provide other benefits. All of the dry mills in 14 of the 18 countries
with dry mills complied with this indicator. Tanzania and Papua New Guinea had the lowest rateof compliance, but still had three-fourths of the mills reusing parchment hulls for energy or otherbenefits. Indonesia and Nicaragua also had high levels of compliance at 88 and 96 percent,respectively.
6.4.2.2 Energy Conservation
Percentage of mills that have reduced energy use
Percentage of mills with commitments to production and/or purchase of renewable energy
Percentage of mills using wood to dry coffee
Percentage of mills using responsible wood sources to dry coffee
Recording and reducing energy use: The overwhelming majority of dry mills (92 percent)recorded the amount of energy used on the mill and over half (53%) showed a decline in theamount used over time. In 8 of the 18 countries participating in C.A.F.E. Practices all of the drymills recorded energy use data, and all countries had at least half of the mills tracking energy
use.
These compliance rates drop significantly for most countries, however, when asked if energy use
has decreased over time. Only Bolivia and Kenya demonstrated total compliance with thisindicator and in each of these countries only one mill participated in the program. Zambia and90

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Burundi had no mills showing a decline in energy use in dry milling processes over time, buteach country only had a single mill reporting on this indicator. Countries with higher reporting
rates demonstrated mixed results. For instance, Indonesia had 68 mills reporting on thisindicator, and a compliance rate of 47 percent, while Costa Rica had 24 mills reporting and acompliance rate of 75 percent. Guatemala (n=16) also had a high percentage (e.g. 81%) ofmills demonstrating a reduction in energy use.
Renewable energy: In FY08, 62 percent of dry mills demonstrated a commitment to renewableenergy. At the country level compliance rates ranged from 100 percent for Panama to no mills
complying in Bolivia, Kenya or Zambia. In each of these cases a very low number of mills
reported on this indicator. Costa Rica, with 26 mills reporting, achieved a very high compliance
rate of 92 percent. Indonesia, on the other hand had 71 mills reporting and only 30 percent
demonstrating a commitment to renewable energy.
Sustainable wood sourcing: Of the dry mills participating in C.A.F.E. Practices, 98 percent
ensured that wood used for drying coffee was harvested according to sustainable practices. This
indicator was only applicable in 7 of the 18 countries participating in the program and all but
Ethiopia achieved 100 percent compliance. Ethiopia had no mills comply with this indicator,
although only 1 of the 15 dry mills in this country appeared to use wood as a fuel source. Due tothe high compliance rates across the board and the significant risk associated with deforestationin most of the coffee producing countries, Starbucks might consider weighting this indicator
more heavily and making it a zero tolerance indicator within the program.
6.4.3 Coffee Processing – Environmental Responsibility Conclusions andRecommendations
Dry mills tended to outperform wet mills on environmental responsibility, although they areassessed against fewer indicators. This was especially the case for the energy efficiency sectionof the scorecard where dry mills had higher compliance rates than wet mills for all but one
indicator. That one indicator was the one asking about the source of wood used. In this case thedry mills were only 1 percentage point less likely to use prunings from shade trees or responsibly
managed forests than wet mills. This is significant given the much higher rate of wood usage fordry mills. A few countries in East Africa appeared to face some challenges in complying withthe dry milling indicators which may provide some direction for outreach and technicalassistance for that region.
All wet mills, regardless of whether they were small farms or stand-alone operations performed
the best within the waste management portion of the scorecard. Most mills were using
byproducts as soil amendments or for other beneficial reuse. There was a higher degree ofvariation within the water use and water discharge sections of the scorecard. Only a very low
percentage of small farms processing coffee were able to comply with the water indicators as
most did not have the required monitoring and tracking systems. Indonesian and Mexican wet
mills tended to have the lowest compliance rates across the various indicators and may warrant
some further training and outreach on water management. Among the stand-alone mills manyrecorded water and energy use although few demonstrated a decline in these use rates over time.
Both wet and dry mills appeared to use sustainable sources of wood as a fuel source, which has
clear benefits for conservation of nearby forests. Due to the potential issues around the use ofwood from primary forests or other important habitats for energy in wet and dry mills and the91FINAL REPORT - FY08 Results Assessment
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already high levels of compliance, Starbucks might consider adding this indicator to the list of
zero tolerance indicators. This would provide additional assurance against deforestation andwould not result in a high degree of non-compliance as the vast majority of wet and dry mills(e.g. all but one mill in both cases) are in compliance with this practice.

7. Conclusions & Recommendations
This report represents the first attempt at undertaking a comprehensive analysis of the C.A.F.E.
Practices program to determine farm, mill and PSO level performance. The analysis shows ahigh level of performance among the various actors within the Starbucks supply chain.
Highlights include:
Small farms of less than 12 hectares make up the vast majority (e.g. 99 percent) of thenearly 141,000 farms participating in the program.
The geographic reach of the program is vast for the three years analyzed included coffee
producers in 20 countries across four continents. These countries significantly overlap
with 8 of the world’s most biologically rich but most threatened regions.
Coffee suppliers achieved high levels of performance across the majority of the socialand environmental indicators selected to assess performance among mills and farms.
Starbucks buys significant volumes from small farms –this amounted to at least half ofthe company’s coffee purchases in FY08.
Coffee farms are making valuable contributions to the conservation of habitat in these
globally important areas for conservation. Participating farms have designated 102,281
hectares as conservation areas and 99 percent have not converted any natural forest areas
to coffee production during the since 2004.
Coffee production is contributing to social and economic development in these regions.
Participating farms employed 1.1 million workers and the majority of large and medium
sized farms made efforts to extend health care and education benefits to workers and theirfamilies.
Although there were some instances of non-compliance among the zero tolerance
indicators, this should be seen as evidence that the program is effective in identifying
coffee producers that do not comply with minimum best practices, and thus enables
Starbucks to send clear messages to these entities by not approving their participation in
the program.
The analysis also identified some areas for potential improvement of the C.A.F.E. Practices
program as it moves forward.
Currently there are no environmental indicators that rise to the zero tolerance level, whichweights the program more strongly in the social direction. Analysis of the environmental
indicator on no clearing of natural habitat shows that raising this indicator to the zero
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tolerance level would not result in significant changes in the number of applications
achieving an approved status. Another environmental processing indicator that might beconsidered for zero tolerance is the practice of ensuring wood used for energy for coffee
processing is sourced from pruned shade trees or other sustainably managed forests.
There are some indicators that recognize legal compliance that are not consideredminimum requirements for achieving an approved status. While the C.A.F.E. Practices
program was designed as a continuous improvement program that offers incentives tofarmers to adopt more practices over time, the program should also provide the necessary
assurance that farms and mills are in compliance with local legislation. Given the highlevel of investment required to manage the program, Starbucks should consider
expanding the minimum requirements to secure assurance that suppliers are in legal
compliance. Starbucks should consider undertaking a review of the guidelines to identify
those practices that refer to legal minimums and making these minimum requirements.
Throughout the program there was some inconsistency in the number of farms or mills
reporting on any given indicator. While this is due in part to the not applicable option for
any indicator which must be explained by the verifier, it has resulted in someinconsistencies across related indicators. The VRS can play a key role in addressing thisissue by requiring consistency in reporting across related indicators for individual farms
or mills. Thus, if the verifier records that the farm employs part-time workers, then all of
the part-time worker indicators would require a compliant or non-compliant rating andthe verifier would not be permitted to enter not applicable.
In some cases some key data points were not available to calculate the number ofindividuals affected by the indicator. For instance, lack of data on the number of workers
that live on a farm prohibits the calculation of the number benefitting from the provisionof habitable dwellings. The same is true for the number of children benefitting fromeducational services provided by the farm or mill. Finally, Starbucks may wish toinclude information on the percentage of workers that are women. On the environmentalside the greatest gap was in the total area of watercourse buffer zones on the farm, since
the indicators only look at width and percentage of the watercourse with a buffer.
The assessment of small farms against only a portion of the total indicators in the Generic
Evaluation Guidelines creates some challenges, especially for the access to medical caresection of the scorecard. This is one instance where there is no indicator to assess small
farms on this criteria. The education section also has challenges, although in that case anadditional indicator was added to the smallholder scorecard that is not used to assess
medium or large farms. Collecting information on the distance to the nearest primaryschool and medical clinic might serve as an effective way to fill these gaps.
Finally, we would recommend that Starbucks take the following steps to integrate this type ofanalysis into management of the program on an annual basis. The following actions would serve
to facilitate and improve the data management and analysis procedures.

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This type of analysis should be incorporated into annual reviews of the program and theintegration of the extrapolation methodology and compliance rate calculations should beincorporated within the Verifier Reporting System (VRS) to facilitate the process.
The sheer number of indicators included in the program and the volume of data made itnecessary to focus on a subset of key indicators within this first report. A core set of
performance indicators should be identified that can be analyzed on an annual basis totake stock of environmental and social performance within the Starbucks coffee supply
chain. The number included in this report is probably still too large even though it
represents only a fraction of the total number of indicators included in the C.A.F.E.
Practices scorecard.
Due to the amount of time necessary to manually extrapolate compliance rates for small
and medium farms, some priorities were set among the indicators to be included in that
section of the analysis. In the future, as this process becomes automated and integrated
within the VRS, these gaps will be filled.
As the extrapolation and reporting of compliance rates becomes more automated,
Starbucks may wish to explore the potential to report results on a country by country
basis and use these results to inform supplier and verifier training sessions held inselected countries.

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8. References
ISEAL Alliance. 2009. “Common Requirements for the Certification of Producer Groups.”
Available at http://www.isealalliance.org/index.cfm?fuseaction=Page.ViewPage&PageID=499(August 31, 2009).
Mittermeier, Russell A., Patricio Robles Gil, Michael Hoffman, John Pilgrim, Thomas Brooks,
Cristina Goettsch Mittermeier, John Lamoreux, and Gustavo A.B. da Fonseca. Preface by Peter
A. Seligmann. 2007. Hotspots Revisited. CEMEX.
Starbucks Coffee Company. 2009. “StarbucksTM Shared PlanetTM Global Responsibility Report
2009.” Available at http://assets.starbucks.com/assets/ssp-g-p-full-report.pdf.
Starbucks Coffee Company and Scientific Certification Systems. 2008. “Verifier and Inspector
Operations Manual 3.1.”
The World Bank. 2009. “The World Bank List of Economies (April 2009).” Available at:
http://web.worldbank.org/WBSITE/EXTERNAL/DATASTATISTICS/0,,contentMDK:2042045
8~menuPK:64133156~pagePK:64133150~piPK:64133175~theSitePK:239419,00.html.

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9. Appendices
Appendix A: Farm Compliance Rates for C.A.F.E. Practices Indicators
97
Appendix B: Producer Support Organization Compliance Rates by Indicator 112
Appendix C: Mill Compliance Rates for C.A.F.E. Practices Indicators
117
Appendix D: Farm Reporting Rates for C.A.F.E. Practices Indicators
130
Appendix E: Mill Reporting Rates for C.A.F.E. Practices Indicators
137

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Appendix A: Farm Compliance Rates for C.A.F.E. Practices Indicators
Indicator

SR-HP1.1

All full-time workers are paid
the nationally or regionallyestablished minimum wage

SR-HP1.2

All part-time workers are paid
the nationally or regionallyestablished minimum wage

SR-HP1.3

All temporary/seasonal workers
are paid the nationally or
regionally established minimum
wage
Overtime pay meets national
requirements

SR-HP1.9

Overtime pay exceeds national
requirements
SR-HP1.10

SR-HP1.11

SR-HP1.12

SR-HP1.13

All full-time workers are paid
more than the nationally or
regionally established minimum
wage
All part-time workers are paid
more than the nationally or
regionally established minimum
wage
All temporary/seasonal workers
are paid more than thenationally or regionallyestablished minimum wage
Full-time workers are paid atleast a living wage

SR-HP1.14

Farm Size

Compliance

All

Small

Medium

Large

C

4436

613

374

5422

NC

1137

31

10

1178

Total (C+NC)

5573

644

384

6601

% Compliance79.59%

95.19%

97.40%

82.15%

4271

183

118

4572

C
NC

18

1

3

22

Total (C+NC)

4289

184

121

4594

% Compliance99.58%

99.46%

97.52%

99.52%

C

88117

944

384

89445

NC

4204

22

4

4230

Total (C+NC)

92321

966

388

93675

% Compliance95.45%

97.72%

98.97%

95.48%

C

21246

218

174

21637

NC

1861

46

39

1946

Total (C+NC)

23107

264

213

23584

% Compliance91.95%

82.47%

81.69%

91.75%

C

13437

122

69

13628

NC

12760

115

141

13017

Total (C+NC)

26197

238

210

26645

% Compliance51.29%

51.46%

32.86%

51.15%

C

2664

488

282

3435

NC

2861

153

100

3114

Total (C+NC)

5525

641

382

6549

% Compliance48.22%

76.15%

73.82%

52.45%

C

4033

132

92

4257

NC

344

52

29

424

Total (C+NC)

4377

184

121

4681

% Compliance92.15%

71.87%

76.03%

90.93%

C

67430

736

286

68453

NC

25026

229

101

25356

Total (C+NC)

92456

965

387

93808

% Compliance72.93%

76.31%

73.90%

72.97%

C

4889

557

326

5772

NC

3969

84

54

4107

Total (C+NC)

8859

641

380

9879

% Compliance55.19%

86.95%

85.79%

58.43%

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ComplianceFarm Size
MediumSmall

SR-HP1.20

SR-HP3.5

SR-HP3.6

SR-HP3.8

SR-HP4.1

SR-HP4.2

SR-HP4.3

SR-HP4.4

SR-HP4.7

Provident/pension planthat exceeds legal
requirements is in place
for full-time employees
Employer has a paid sickleave program for full-time
employees

Employer has a paid sickleave program for all
workers

Employer has an annual
leave (vacation) programthat meets applicablelaws
Employer does notdirectly contract any
persons under the age of
14
Employment of authorizedminors older than 14 does
not conflict with theiraccess to education
Management has an
enforced policy prohibiting
discrimination on thebasis of gender, race,
ethnicity, age or religion
as per ILO Convention
111
Employer prohibits theuse of forced, bonded,indentured or involuntaryconvict labor
All workers receive equalpay for equal work,
except in case ofrecognizing seniority ofservice through higher
pay

All
Large

C

NA

126

99

225

NC

NA

486

284

770

Total (C+NC)

NA

612

383

995

% ComplianceNA

20.53%

25.85%

22.57%

C

NA

518

338

856

NC

NA

123

46

169

Total (C+NC)

NA

641

384

1025

% ComplianceNA80.75%

88.02%

83.47%

C

NA

436

225

661

NC

NA

487

172

659

Total (C+NC)

NA

923

397

1320

% ComplianceNA47.22%

56.68%

50.06%

C

NA

443

312

755

NC

NA

181

69

250

Total (C+NC)

NA

624

381

1005

% ComplianceNA71.04%

81.89%

75.16%

109659994

402

111056

348

5

1

354

Total (C+NC)

110008999

403

111410% Compliance99.68%

99.51%

99.75%

99.68%

C

44038

232

114

44385

NC

1061

7

1

1069

Total (C+NC)

45099

239

115

45453

% Compliance97.65%

97.23%

99.13%

97.65%

C

NA

996

407

1403

NC

NA

3

3

6

Total (C+NC)

NA

999

410

1409

% ComplianceNA

99.70%

99.27%

99.57%

101324988

403

102716

0

1

0

1

C
NC

C
NC
Total (C+NC)

101324989

403

102717% Compliance100.00%99.90%

100.00%100.00%

C

NA

979

404

1383

NC

NA

17

4

21

Total (C+NC)

NA

996

408

1404

% ComplianceNA

98.25%

99.02%

98.47%

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Farm Size

Compliance
Small

SR-WC1.1

Part-time/seasonal and full-timeCworkers living onsite have habitableNC
dwellings

618

349

12889

1412

73

27

1512

Total (C+NC)

13334

691

376

14401

% Compliance89.41%

89.37%

92.82%

89.50%

C

84370

756

355

85480

NC

5324

204

44

5572

Total (C+NC)

89694

960

399

91053

% Compliance

94.06%

78.74%

88.97%

93.88%

C

NA

451

293

744

NC

NA

248

84

332

Total (C+NC)

NA

699

377

1076

% Compliance
Workers have ready access toCsanitary facilities that do not impact
the local environmentNCNA64.51%

77.72%

69.14%

NA

729

306

1035

NA

229

91

320

Total (C+NC)

NA

958

397

1355

% Compliance
Where there is insufficient access topublic education, schoolchildren (of Cprimary school age) of workers wholive on-site have access to primary NC
educational instruction, facilities andmaterials that meet national
Total (C+NC)
requirements

NA

76.06%

77.08%

76.36%

NA

346

218

564

NA

12

5

17

NA

358

223

581

% Compliance
Where there is insufficient access topublic education, schoolchildren (of C
secondary school age) of workers
who live on-site have access toNCprimary educational instruction,
facilities and materials that meet
Total (C+NC)
national requirements

NA96.56%

97.76%

97.02%

NA

235

157

392

NA

42

34

76

NA

278

191

469

% Compliance
Employer provides direct incentivesCfor education in the form ofscholarships, educational materials, NC
boos, transportation, food, etc.
Total (C+NC)

NA

84.77%

82.20%

83.72%

NA

373

245

618

NA

487

150

637

NA

860

395

1255

% Compliance
Where there is convenient access toCpublic education programs,NC
employer supports those schools
either through in-kind donation or
Total (C+NC)
financial support% ComplianceNA

43.39%

62.03%

49.26%

NA

511

266

777

NA

432

124

556

NA

942

390

1332

NA

54.19%

68.21%

58.29%

SR-WC1.2

SR-WC1.4

SR-WC2.1

SR-WC2.2

SR-WC2.3

SR-WC2.4

All
Large

11922

Workers have ready access topotable water

SR-WC1.3

MediumWorker housing has buffer zones
from productive area andagrochemical storage facilities
sufficient to prevent agrochemical
exposure

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Farm Size

Compliance
Small

SR-WC2.5

SR-WC2.6

SR-WC3.3

Employer supports training or workshopsC
on additional skills or trades
NC

All

358

212

570

NA

609

198

807

Total (C+NC)

NA

967

410

1377

% ComplianceChildren of legal school age attend schoolC
where available and do not work duringNC
school hoursNA

37.02% 51.71%

41.40%

131484

NANA

131484

612

NA

NA

612

Total (C+NC)

132097NANA132097% Compliance99.54%

NANA

99.54%

C

NA

257

159

416

NC

NA

596

208

804

Total (C+NC)

NA

853

367

1220

% ComplianceNA

30.09% 43.32%

34.07%

C

NA

533

339

872

NC

NA

143

48

191

Total (C+NC)

NA

675

387

1062

% ComplianceNA

78.84% 87.60%

82.03%

C

NA

667

306

973

NC

NA

292

96

388

Total (C+NC)

NA

959

402

1361

% Compliance
Employer provides personal protectiveC
equipment to all applicable employees at
no costNCNA

69.54% 76.12%

71.49%

16726

549

298

17573

37073

271

104

37447

Total (C+NC)

53799

820

402

55020

% Compliance31.09%

66.97% 74.13%

31.94%

Where there is convenient access topublic medical care, employer supports
these facilities either through in-kind
donation or financial support

SR-WC3.5

Employer offsets the cost of healthservices for all workers

SR-WC4.1

Large

NA

Employer offsets the cost of healthservices for permanent workers

SR-WC3.6

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Farm Size

Compliance
Small

CG-WR1.1

CG-WR1.2

CG-WR1.3

CG-WR1.4

CG-WR1.5

CG-WR1.6

CG-WR1.7

Watercourse buffer zones areCmaintained adjacent to at least 25% ofNCwater bodies (>2m in width)

All
Large

NA

668

316

984

NA

55

5

60

Total (C+NC)

NA

723

321

1044

% Compliance
Watercourse buffer zones areCmaintained adjacent to at least 50% ofNCwater bodies (>2m in width)NA92.43%

98.44%

94.28%

31055

614

286

31955

8008

110

36

8153

Total (C+NC)

39062

724

322

40108

% Compliance79.50%

84.85%

88.82%

79.67%

C

23471

477

233

24181

NC

14494

246

90

14830

Total (C+NC)

37966

723

323

39011

% Compliance
Watercourse buffer zones areCmaintained adjacent to at least 50% ofNCseasonal/intermittent watercourses(>2m in width)
Total (C+NC)

61.82%

65.99%

72.14%

61.98%

NA

335

185

520

NA

183

71

254

NA

518

256

774

% ComplianceNA64.65%

72.27%

67.17%

C

NA

227

118

345

NC

NA

290

137

427

Total (C+NC)

NA

517

255

772

% Compliance
Watercourse buffer zones of at least 5m C
per side are maintained adjacent to atleast 50% of seasonal/intermittent and NC
water bodies
Total (C+NC)

NA

43.85%

46.27%

44.65%

NA

366

196

562

NA

331

129

460

NA

696

325

1021

% Compliance
Watercourse buffer zones of at least 5m C
per side are maintained adjacent to allseasonal/intermittent and water bodies NC

NA

52.51%

60.31%

54.99%

NA

250

134

384

NA

466

197

663

Total (C+NC)

NA

716

331

1047

% ComplianceNA34.95%

40.48%

36.70%

C

NA

350

191

541

NC

NA

325

129

454

Total (C+NC)

NA

675

320

995

% ComplianceNA51.84%

59.69%

54.37%

C

27411

621

285

28317

NC

8887

148

54

9090

Total (C+NC)

36298

770

339

37407

% Compliance75.52%

80.72%

84.07%

75.70%

Watercourse buffer zones aremaintained adjacent to all of water
bodies (>2m in width)

Watercourse buffer zones aremaintained adjacent to allseasonal/intermittent watercourses(>2m in width)

There is a plan to restore native
vegetation within the buffers
CG-WR1.12

CG-WR1.14

MediumAt least 50% of watercourse bufferzones are composed of native woody
vegetation

101FINAL REPORT - FY08 Results Assessment
Public Version

March 2011

Indicator

Farm Size

Compliance
Small

CG-WR1.15

CG-WR2.1

CG-WR2.2

CG-WR2.4

CG-WR3.1

CG-WR3.2

CG-WR3.3

All watercourse buffer zones arecomposed of native woody
vegetation

All

Medium

Large

C

21671

417

169

22257

NC

14864

352

173

15389

Total (C+NC)

36535

770

342

37646

% Compliance59.32%

54.23%

49.42%

59.12%

C

44110

422

235

44767

NC

13273

276

113

13662

Total (C+NC)

57384

698

348

58429

% Compliance76.87%

60.45%

67.53%

76.62%

C

NA

488

228

716

NC

NA

28

19

47

Total (C+NC)

NA

516

247

763

% Compliance
Synthetic fertilizers are not used or C
the farm is certified organicNCNA

94.59%

92.31%

93.85%

66627

211

31

66869

64085

620

355

65060

Total (C+NC)

130712831

386

131928% ComplianceIrrigation water use is tracked and C
recorded per lb. of green coffee,NC
per hectare and for each plot

50.97%

25.40%

8.03%

50.69%

NA

8

11

19

NA

12

15

27

Total (C+NC)

NA

20

26

46

% ComplianceNA39.79%

42.31%

41.20%

C

NA

6

18

24

NC

NA

36

13

49

Total (C+NC)

NA

41

31

72

% ComplianceIrrigation water use doesn't exceed C
the operation's net neutralNChydrological balanceNA

13.29%

58.06%

32.46%

NA

5

10

15

NA

9

12

21

Total (C+NC)

NA

14

22

36

% ComplianceNA

33.26%

45.45%

40.73%

There is no application of
agrochemicals within 10m of any
water body or watercourse

There is no application ofnematicides within 20m of any
water body or watercourse

There is a hydrological balance
assessment, including climatic,
community and competing
agricultural issues

102FINAL REPORT - FY08 Results Assessment
Public Version

Indicator

March 2011
Farm Size

Compliance
Small

CG-SR1.1

CG-SR1.2

CG-SR1.3

CG-SR1.4

CG-SR1.5

CG-SR1.6

CG-SR1.7

CG-SR1.8

CG-SR1.9

Farm managers demonstrate knowledge of C
farm areas at risk to erosion
NC

All

MediumLarge

116236977

405

117618

11541

0

3

11544

Total (C+NC)

127777977

408

129162% Compliance
There is an explicit soil management plan Cthat includes measures to minimizeNCsurface erosion

90.97%

100.00%99.26%

91.06%

NA

574

289

863

NA

431

118

549

Total (C+NC)

NA

1005

407

1412

% Compliance
Areas at high risk of erosion are identified C
on a map
NCNA

57.12%

71.01%

61.13%

27124

395

220

27739

62513

562

178

63253

Total (C+NC)

89637

957

398

90991

% Compliance
Productive areas on at least 25% of slopes C
over 10% slope are covered by shadeNC
trees and/or cover crops/vegetation

30.26%

41.29%

55.28%

30.48%

100745974

386

102105

1841

14

8

1862

Total (C+NC)

102585988

394

103967% Compliance
Productive areas on at least 50% of slopes C
over 10% slope are covered by shadeNC
trees and/or cover crops/vegetation

98.21%

98.61%

97.97%

98.21%

93428

944

371

94743

9143

44

22

9209

Total (C+NC)

102571

988

393

103952% Compliance
Productive areas on all of slopes over 10% C
slope are covered by shade trees and/orNC
cover crops/vegetation

91.09%

95.54%

94.40%

91.14%

71862

807

336

73005

29694

181

58

29934

Total (C+NC)

101557988

394

102939% Compliance
Productive areas on at least 25% of slopes C
over 20% slope are covered by shadeNC
trees and/or cover crops/vegetation

70.76%

81.66%

85.28%

70.92%

84822

900

362

86084

3424

68

20

3513

Total (C+NC)

88247

968

382

89597

% Compliance
Productive areas on at least 50% of slopes C
over 20% slope are covered by shadeNC
trees and/or cover crops/vegetation

96.12%

92.93%

94.76%

96.08%

76404

762

302

77469

11715

202

80

11997

Total (C+NC)

88119

964

382

89465

% Compliance
Productive areas on all of slopes over 20% C
slope are covered by shade trees and/orNC
cover crops/vegetation

86.71%

79.05%

79.06%

86.59%

58887

499

236

59622

29155

457

146

29758

Total (C+NC)

88042

956

382

89380

% Compliance66.89%

52.21%

61.78%

66.71%

103

FINAL REPORT - FY08 Results Assessment
Public Version

March 2011

Indicator

Farm Size

Compliance
Small

CG-SR1.10

CG-SR1.11

CG-SR1.12

CG-SR1.13

CG-SR1.16

CG-SR2.1

CG-SR2.2

CG-SR2.3

CG-SR2.5

Productive areas on at least 25% of
slopes over 30% slope are covered byshade trees and/or covercrops/vegetation

All

Medium

Large

C

63645

659

344

64648

NC

7977

89

12

8078

Total (C+NC)

71622

748

356

72726

% Compliance88.86%

88.11%

96.63%

88.89%

C

54584

580

321

55485

NC

16674

156

38

16868

Total (C+NC)

71258

736

359

72353

% Compliance76.60%

78.82%

89.42%

76.69%

C

44462

444

277

45183

NC

26857

288

82

27227

Total (C+NC)

71319

732

359

72410

% Compliance62.34%

60.68%

77.16%

62.40%

C

122796636

249

123680

NC

5510

210

157

5877

Total (C+NC)

128306845

406

129557% Compliance
Areas in which the risk of landslides is C
extreme are not cultivated, are left orNC
taken out of production and restoredwith native vegetation where practicable
Total (C+NC)

95.71%

75.19%

61.33%

95.46%

NA

335

183

518

NA

267

145

412

NA

602

328

930

% ComplianceNA

55.66%

55.79%

55.70%

C

127127863

407

128397

NC

3336

1

4

3341

Total (C+NC)

130463864

411

131738% Compliance97.44%

99.88%

99.03%

97.46%

C

113015830

382

114226

NC

17594

34

29

17657

Total (C+NC)

130609864411131884% Compliance
All of the productive area is covered by C
an organic matter layer and/or nitrogenNC
fixing cover crops

86.53%

96.05%

92.94%

86.61%

82649

641

306

83596

47769

223

105

48097

Total (C+NC)

130418864411131693% Compliance
At least 25% of the productive area isCplanted with nitrogen-fixing, leguminous
NC
trees

63.37%

74.19%

74.45%

63.48%

93834

839

391

95065

36754

24

18

36796

Total (C+NC)

130589863

409

131860% Compliance71.85%

97.28%

95.60%

72.09%

Productive areas on at least 50% of
slopes over 30% slope are covered byshade trees and/or covercrops/vegetation

Productive areas on all of slopes over
30% slope are covered by shade trees
and/or cover crops/vegetation

Herbicides are not used to control
ground vegetation/cover crops and are
only used in spot applications for
aggressive weeds

At least 25% of the productive area iscovered by an organic matter layer
and/or nitrogen-fixing cover crops

At least 50% of the productive area iscovered by an organic matter layer
and/or nitrogen-fixing cover crops

104FINAL REPORT - FY08 Results Assessment
Public Version

March 2011
Farm Size

Indicator

CG-SR2.6

CG-SR2.7

Compliance
At least 50% of the productive area isCplanted with nitrogen-fixing, leguminous
NC
trees

Medium

Large

All

69831

774

364

70969

60202

89

43

60334

Total (C+NC)

130033863

407

131303% Compliance
All of the productive area is planted with C
nitrogen-fixing, leguminous trees
NC

53.70%

89.74%

89.43%

54.05%

45018

596

288

45902

83535

266

118

83920

Total (C+NC)

128553863

406

129822% Compliance35.02%

69.12%

70.94%

35.36%

C

NA

111

17

128

NC

NA

745

382

1127

Total (C+NC)

NA

855

399

1254

% ComplianceNA

12.94%

4.26%

10.18%

Farm is certified organic
CG-SR2.12

Small

105

FINAL REPORT - FY08 Results Assessment
Public Version

March 2011
Farm Size

Indicator

CG-CB1.1

Compliance
Native trees are removed only
when they constitute a humanhazard or when they significantly
compete with coffee plants

The farm has a shade
management plan
CG-CB1.2

CG-CB1.3

The farm is implementing theshade management plan andmeeting the timeline for actions

Farm is covered by at least 10%
canopy cover
CG-CB1.4

Canopy cover is comprised of adiversity of tree species
CG-CB1.5

Canopy cover is retained atbiologically significant levels
CG-CB1.6

Invasive exotic species are notused for canopy cover
CG-CB1.7

CG-CB1.8

An average of 40% canopy coveris maintained across the
productive area of the farm

All

Small

MediumLarge

C101242926

387

102556

NC

6026

58

12

6096

Total (C+NC)

107269985

399

108652% Compliance94.38%

94.10%

96.99%

94.39%

C

NA

446

256

702

NC

NA

531

149

680

Total (C+NC)

NA

977

405

1382

% ComplianceNA

45.63%

63.21%

50.78%

C

NA

432

247

679

NC

NA

409

109

518

Total (C+NC)

NA

840

356

1196

% ComplianceNA51.36%

69.38%

56.73%

C

118602976

394

119972

NC

17394

24

16

17435

Total (C+NC)

1359961000

410

137407% Compliance87.21%

97.56%

96.10%

87.31%

C

96485

827

353

97665

NC

29800

176

55

30031

Total (C+NC)

1262851003

408

127696% Compliance76.40%

82.46%

86.52%

76.48%

C

104064857

351

105272

NC

24576

144

57

24777

Total (C+NC)

1286391001

408

130048% Compliance80.90%

85.59%

86.03%

80.95%

C

124277976

395

125649

NC

2114

25

11

2150

Total (C+NC)

126391

1001

406

127798% Compliance98.33%

97.52%

97.29%

98.32%

C

NA

792

322

1114

NC

NA

198

86

284

Total (C+NC)

NA

991

408

1399

% ComplianceNA

79.97%

78.92%

79.66%

106

FINAL REPORT - FY08 Results Assessment
Public Version

March 2011
Farm Size

Indicator

CG-CB1.9

CG-CB1.10

Compliance
At least 75% of the canopy cover iscomprised of locally native species
and/or the canopy consists of atleast 10 species that are locallynative or can be shown tocontribute to the conservation ofnative biodiversity
Where local ecological conditions
allow, shade canopy is comprisedof at least 2 distinguishable canopy
strataLocally native epiphytes are
retained in the canopy cover
CG-CB1.11

CG-CB1.12

CG-CB2.1

CG-CB2.2

CG-CB2.3

Biological legacies, such as cavity
trees and standing and/or fallen
dead trees, are retained or
recruited

There are specific implemented
measures to restrict unauthorizedhunting and commercial collectionof flora and fauna

Hunting threatened or rare wildlife
species is not allowed on theproperty

Farm management has created alist of wildlife species native to theregion and identified which of thosespecies are classified asvulnerable, endangered or criticallyendangered according to the IUCN
red list

All

Small

Medium

Large

C

78327

722

316

79365

NC

47508

280

91

47878

Total (C+NC)

1258351002

407

127244% Compliance62.25%

72.06%

77.64%

62.37%

C

NA

538

240

778

NC

NA

409

138

547

Total (C+NC)

NA

947

378

1325

% ComplianceNA56.80%

63.49%

58.71%

C

57215

828

364

58407

NC

24817

142

30

24989

Total (C+NC)

82032

970

394

83396

% Compliance69.75%

85.38%

92.39%

70.04%

C

64566

898

387

65850

NC

22283

78

21

22382

Total (C+NC)

86848

976

408

88232

% Compliance74.34%

91.98%

94.85%

74.63%

C

71284

745

372

72401

NC

26654

280

38

26972

Total (C+NC)

97938

1025

410

99373

% Compliance72.78%

72.69%

90.73%

72.86%

C

88498

803

337

89638

NC

4236

201

63

4500

Total (C+NC)

92734

1004

400

94138

% Compliance95.43%

80.02%

84.25%

95.22%

C

NA

299

181

480

NC

NA

721

225

946

Total (C+NC)

NA

1020

406

1426

% ComplianceNA29.30%

44.58%

33.65%

107

FINAL REPORT - FY08 Results Assessment
Public Version

March 2011
Farm Size

Indicator

CG-CB2.4

CG-CB2.5

ComplianceFarm management has consulted
with relevant government agencies
or universities to determine which
wildlife species are native to the
farm's region and classified asvulnerable, endangered, or criticallyendangered
A Wildlife Management Plan isdeveloped on the basis of the results
gathered from the consultations with
relevant government agencies oruniversities

Small

Medium

Large

All

C

NA

197

126

323

NC

NA

820

281

1101

Total (C+NC)

NA

1018

407

1425

% ComplianceNA

19.41%

30.96%

22.71%

C

NA

202

124

326

NC

NA

761

269

1030

Total (C+NC)

NA

963

393

1356

% ComplianceNA

20.94%

31.55%

24.02%

108

FINAL REPORT - FY08 Results Assessment
Public Version
Indicator

CG-CB2.6

March 2011
Compliance

The Wildlife Management
Plan is properly
implemented on the farm

CG-CB3.1

CG-CB3.2

CG-CB3.3

CG-CB3.5

CG-CB3.6

CG-CB3.7

CG-CB3.8

If areas of naturalvegetation are converted toagricultural production,these are not areas of highecological value and there
are equivalent set-asides
There is an assessment of
the farm for areas of highecological value

Areas of identified highecological value are clearly
defined, protected andmanaged with aconservation emphasis thatmaintains the highecological values
If areas of high ecologicalvalue are completely lacking
on the farm, there is a plan
to restore natural habitat,
forest and other naturalvegetative areas on a
portion of the farm

If areas of high ecologicalvalue are completely lacking
on the farm, managers have
implemented the plan torestore natural habitat orcondition on a portion of the
farm

Areas of high ecologicalvalue are protected fromfuture development throughthe declaration of private
reserves, conservationemphasis areas or legal
conservation easements

Small

Medium

All

Large

C

NA

194

151

NC

NA

598

179

777

Total (C+NC)

NA

792

330

1122

NA

24.48%

45.76%

30.73%

136398919

383

137700% Compliance
After March 2004, there isno conversion of naturalforest to agricultural
production

Farm Size

C
NC

345

546

42

5

592

Total (C+NC)

136943960

388

138292% Compliance99.60%

95.67%

98.71%

99.57%

C

NA

282

98

380

NC

NA

29

13

42

Total (C+NC)

NA

312

111

423

% ComplianceNA90.62%

88.29%

90.01%

C

NA

461

240

701

NC

NA

466

151

617

Total (C+NC)

NA

926

391

1317

% ComplianceNA49.73%

61.38%

53.19%

C

NA

468

253

721

NC

NA

284

93

377

Total (C+NC)

NA

752

346

1098

% ComplianceNA

62.24%

73.12%

65.67%

C

NA

117

46

163

NC

NA

238

42

280

Total (C+NC)

NA

355

88

443

% ComplianceNA32.96%

52.27%

36.80%

C

NA

97

41

138

NC

NA

239

37

276

Total (C+NC)

NA

336

78

414

% ComplianceNA28.85%

52.56%

33.33%

C

NA

113

108

221

NC

NA

559

226

785

Total (C+NC)

NA

673

334

1007

% ComplianceNA

16.85%

32.34%

21.99%

109

FINAL REPORT - FY08 Results Assessment
Public Version
Indicator

Compliance

CG-CB3.9

At least 3% of the farm is set
aside as a conservationemphasis area

CG-CB3.10

At least 5% of the farm is set
aside as a conservationemphasis area

CG-CB3.11

CG-CB3.12

March 2011

Biological corridors are
established to connect
multiple conservationemphasis areas
Multiple plant species
selected for contribution tobiodiversity are establishedwhere space allows within
the farm

Farm Size
Small

Medium

Large

All

C

NA

719

347

NC

NA

274

61

1066
335

Total (C+NC)

NA

993

408

1401

% ComplianceNA72.38%

85.05%

76.07%

C

NA

630

316

946

NC

NA

357

91

448

Total (C+NC)

NA

987

407

1394

% ComplianceNA63.79%

77.64%

67.83%

C

NA

423

246

669

NC

NA

357

111

468

Total (C+NC)

NA

780

357

1137

% ComplianceNA54.22%

68.91%

58.83%

C

NA

684

331

1015

NC

NA

319

78

397

Total (C+NC)

NA

1003

409

1412

% ComplianceNA

68.21%

80.93%

71.89%

110

FINAL REPORT - FY08 Results Assessment
Public Version

March 2011
Farm Size

Indicator

CG-EM1.1

CG-EM1.7

CG-EM1.8

CG-EM1.18

CG-EM1.19

CG-EM2.1

CG-EM2.3

Compliance

CG-EM2.5

MediumAll

Large

Farms do not use chemicals that are C
listed by the World HealthNC
Organization as Type 1A or 1B,
except as specified by the nematode
Total (C+NC)
amendment% ComplianceFarms calculate total toxic load ofCproductive area on farmNC

129168884

350

130402

2020

79

61

2160

131188963

41113256398.46%

91.81%

85.16%

98.37%

NA

73

128

201

NA

602

247

849

Total (C+NC)

NA

675

375

1050

% ComplianceNA

10.84%

34.13%

19.16%

C

NA

87

84

171

NC

NA

491

214

705

Total (C+NC)

NA

577

298

875

% ComplianceAgrochemicals are only applied as a Clast resort (after cultural and physicalNC
controls have failed)NA

15.00%

28.19%

19.49%

44593

437

261

45290

33781

270

105

34156

Total (C+NC)

78373

707

366

79446

% Compliance56.90%

61.75%

71.31%

57.01%

C

NA

482

243

725

NC

NA

513

164

677

Total (C+NC)

NA

995

407

1402

% ComplianceFarm managers implement aCmonitoring program which tracks
farm activities and improvements in NC
C.A.F.E. Practices
Total (C+NC)

NA

48.41%

59.71%

51.69%

NA

505

292

797

NA

439

112

551

NA

944

404

1348

% ComplianceNA53.52%

72.28%

59.15%

C

NA

530

271

801

NC

NA

474

136

610

Total (C+NC)

NA

1004

407

1411

% ComplianceNA52.78%

66.58%

56.76%

C

NA

513

271

784

NC

NA

335

97

432

Total (C+NC)

NA

848

368

1216

% Compliance
The management plan is updated on C
an annual basis
NCNA

60.46%

73.64%

64.45%

NA

441

248

689

NA

376

101

477

Total (C+NC)

NA

818

349

1167

% ComplianceNA53.95%

71.06%

59.07%

Toxic load is decreased over timethrough reduction in agrochemicaluse or selecting less toxic
alternatives

There is a written Integrated Pest
Management Plan that is properly
implemented in the field

Farm managers develop a written
management plan and supporting
documents

Farm managers implement thewritten management plan
CG-EM2.4

Small

111

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Public Version

March 2011

Appendix B: Producer Support Organization Compliance Rates by Indicator
Indicators
PS-MT1: Management and Tracking Systems
PS-MT1.1
All supply chain entities have a system for tracking
product from initial purchase through point of export

PS-MT1.2

Organization has an annually updated list of
producers participating in the C.A.F.E. Practices
program

PS-MT1.3

Each farm in the supply chain receives a receipt forcoffeePS-MT1.4

Participating farmers are given a written agreement
or identification card upon their commitment tocomply with the C.A.F.E. Practices

PS-MT1.5

Organization maintains farm inspection reports
documenting status of compliance of each farmaccompanied by a farm map and description

PS-SR1: Controlling Surface Erosion
PS-SR1.1
Producer Support Organization has an explicit soil
management plan that includes erosion reduction
strategies

PS-SR1.2

Producer Support Organization has identified
resources for erosion controls

PS-SR1.3

Producer Support Organization facilitates the
distribution of erosion controls for more than 5% ofproducers in network

PS-SR1.4

Producer Support Organization facilitates the
distribution of erosion controls for more than 15% ofproducers in network

ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% Compliance

336
9
10
345
97%
338
6
11
344
98%
322
28
5
350
92%
202
149
4
351
58%
184
167
4
351
52%

C
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% Compliance

263
91
1
354
74%
278
73
4
351
79%
201
142
12
343
59%
176
167
12
343
51%

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Indicators
PS-SR1.5

Producer Support Organization facilitates the
distribution of erosion controls for more than 25% ofproducers in network

PS-SR2: Maintaining Soil Productivity
PS-SR2.1
Where synthetic fertilizers are used, Producer
Support Organization has included in its soil
management plan a strategy for soil analysis (fornutrient deficiencies and organic matter content) from
representative plots in producer network
PS-SR2.2

Where synthetic fertilizers are used, Producer
Support Organization has included in its soil
management plan a strategy for foliar analysis (fornutrient deficiencies) from representative plots in
producer network

PS-SR2.3

Where synthetic fertilizers are used, Producer
Support Organization has developed a timeline for
implementing soil and foliar testing strategy

PS-SR2.4

Where synthetic fertilizers are used, Producer
Support Organization is implementing its soil andfoliar testing strategy according to the timeline

PS-CB1: Maintaining Shade Canopy
PS-CB1.1
Producer Support Organization has a shade
management plan including: identifying areas withgaps in shade and plans for replanting invasive
exotic/non-native trees with native species

PS-CB1.2

Producer Support Organization has identified
resources for the distribution of shade trees orseedlings

C
NC
NA
Total (C+NC)% Compliance

160
178
17
338
47%

C
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% Compliance

201
71
83
272
74%
122
126
107
248
49%
128
127
100
255
50%
101
141
113
242
42%

C
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% Compliance

249
103
3
352
71%
232
109
14
341
68%

C
NC
NA
Total (C+NC)% Compliance

170
158
27
328
52%

PS-CB2: Protecting Wildlife
PS-CB2.1

Producer Support Organization has created a list of
wildlife species native to the region and identified
which of those species are classified as vulnerable,
endangered or critically endangered according to the
IUCN red list (http://www.redlist.org)

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Indicators
PS-EM1: Ecological Pest and Disease Control
PS-EM1.1
Producer Support Organization does not use
chemicals that are listed by the World HealthOrganization as Type 1A or 1B, except as specified
by the nematode amendment*

PS-EM1.2

Producer Support Organization maintains purchaserecords of pesticides (specifying date, product,
formulation, active ingredient, quantity, supplier andprice of purchase for each pesticide)

PS-EM1.3

Agrochemicals are stored in a locked place with
controlled access

PS-EM1.4

Pesticides are applied using spot spraying based onincidence and infestation pattern (evidence frommonitoring)

PS-EM1.5

Agrochemicals are only applied as a last resort (after
cultural and physical controls have failed)

PS-EM1.6

Where coffee berry borer infestation exists, Producer
Support Organization facilitates the distribution of
biological control agents or methanol/ethanol traps
for more than 5% of affected producers in network

PS-EM1.7

Where coffee berry borer infestation exists, Producer
Support Organization facilitates the distribution of
biological control agents or methanol/ethanol traps
for more than 15% of affected producers in network

PS-EM1.8

Where coffee berry borer infestation exists, Producer
Support Organization facilitates the distribution of
biological control agents or methanol/ethanol traps
for more than 25% of affected producers in network

PS-EM2: Management and Monitoring
PS-EM2.1
Producer Support Organization implements amonitoring program to track farm activities andimprovements in C.A.F.E. Practices for more than
5% of the producers in its network

March 2011

C
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% Compliance

174
13
168
187
93%
66
19
270
85
78%
94
3
258
97
97%
77
24
254
101
76%
95
22
238
117
81%
176
95
84
271
65%
166
101
88
267
62%
138
128
89
266
52%

C
NC
NA
Total (C+NC)% Compliance

276
68
11
344
80%

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Indicators
PS-EM2.2

Producer Support Organization implements amonitoring program to track farm activities andimprovements in C.A.F.E. Practices for more than
10% of the producers in its network

PS-EM2.3

Producer Support Organization implements amonitoring program to track farm activities andimprovements in C.A.F.E. Practices for more than
15% of the producers in its network

PS-EM2.4

Producer Support Organization holds annual
planning meeting(s) to develop a written annual work
plan which details which C.A.F.E. Practices activities
are to be taken in the coming year

PS-EM2.5

Producer Support Organization develops a written
management plan and supporting documents,
including but not limited to:- A description of the farm production systems andcoffee productivity(coffee production per hectare,total annual coffee production) for the association- Producer Support Organization farmer training plan
- Ecological pest and disease management
measures- Soil quality improvement strategies- Producer Support Organization farmer resource
sharing

PS-EM2.6

Producer Support Organization implements thewritten management plan with more than 5% ofproducers in network

PS-EM2.7

Producer Support Organization implements thewritten management plan with more than 15% ofproducers in network

PS-EM2.8

Producer Support Organization implements thewritten management plan with more than 25% ofproducers in network

PS-EM2.9

The management plan is updated on an annual basis

C
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% Compliance
C

262
80
13
342
77%
256
85
14
341
75%
259
80
16
339
76%
201

NC

148

NA

6

Total (C+NC)

349% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% Compliance

58%
239
74
42
313
76%
225
88
42
313
72%
183
127
45
310
59%
202
58
95
260
78%

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Indicators
PS-EM2.10

Producer organization has documented materials fortraining members in its network on: shade
management; integrated pest control and disease
management; pruning, weeding and cultural
management; and processing and drying coffee

PS-EM2.11

Producer Support Organization has trained morethan 5% of its network on topics covered in PSEM2.5

PS-EM2.12

Producer Support Organization has trained morethan 15% of its network on topics covered in PSEM2.5

PS-EM2.13

Producer Support Organization has trained morethan 25% of its network on topics covered in PSEM2.5

CNC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% Compliance
C

272
79
4
351
77%
277
68
10
345
80%
262
77
16
339
77%
219

NC

120

NA

16

Total (C+NC)

339% Compliance

65%

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Appendix C: Mill Compliance Rates for C.A.F.E. Practices Indicators
Indicator
Hiring Practices
All full-time workers are paid
the nationally or regionallyestablished minimum wage
SR-HP1.1

SR-HP1.2

SR-HP1.3

All part-time workers are paid
the nationally or regionallyestablished minimum wage

All temporary/seasonal
workers are paid thenationally or regionallyestablished minimum wage

Management maintains
complete written earning
records
SR-HP1.4

Workers are paid regularly incash/cash equivalent
SR-HP1.5

SR-HP1.6

If nationally legally mandated
benefits are required for fulltime workers, then these arepaid by employer

SR-HP1.7

If nationally legally mandated
benefits are required for parttime workers, then these arepaid by employer

SR-HP1.8

If nationally legally mandated
benefits are required forseasonal/temporary workers,
then these are paid byemployer
Overtime pay meets national
requirements

SR-HP1.9

Wet

Mill Type
Dry

Both

C
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)

334
6
375
340
98.24%
762
637
78
97.44%
583
12
120
595
97.98%
595
42
78
637

153
1
37
154
99.35%
580
133
58
100.00%
157
3
31
160
98.13%
174
16
1
190

68
0
1
68
100.00%17052
17100.00%56
0
1356
100.00%
67
2
0
69

555
7
413
562
98.75%
151
2
822
153
98.69%
796
15
164
811
98.15%
836
60
79
896

% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% Compliance93.41%
6550
60
655
100.00%
313
21
381
334
93.71%
70
4641
74
94.59%
339
95
281
434
78.11%
294
68
353
362
81.22%

91.58%
189
0
2
189
100.00%141
3
0144
97.92%
170
174
17100.00%
100
4
87
104
96.15%
117
38
36
155
75.48%

97.10%
6900
69
100.00%620762
100.00%13
155
1492.86%
42225
4495.45%
61
6
2
67
91.04%

93.30%
913
0
62
913
100.00%
516
24
388
540
95.56%
100
5
870
105
95.24%
481
101
393
582
82.65%
472
112
391
584
80.82%

Compliance

All

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Indicator

Compliance
Overtime pay exceeds national
requirements

SR-HP1.10

SR-HP1.11

All full-time workers are paid
more than the nationally or
regionally established minimum
wage

SR-HP1.12

All part-time workers are paid
more than the nationally or
regionally established minimum
wage

SR-HP1.13

All temporary/seasonal workers
are paid more than thenationally or regionallyestablished minimum wage

Full-time workers are paid atleast a living wage
SR-HP1.14

In-kind payments are itemized inwriting
SR-HP1.15

Workers have access to theirearnings records
SR-HP1.16

Wages are paid directly to all
workers
SR-HP1.17

Wages are paid directly to all
workers
SR-HP1.17

March 2011

C
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% Compliance

Wet

Mill Type
Dry

Both

153
197
365
350
43.71%
269
68
378
337
79.82%
60
19
636
79
75.95%
486
108
121
594
81.82%
312
65
338
377
82.76%
5612
647
68
82.35%
582
56
77
638
91.22%
636
574
641
99.22%
636
574
641
99.22%

54
96
41
150
36.00%
131
23
37
154
85.06%
57
1
133
58
98.28%
117
43
31
160
73.13%
135
21
35
156
86.54%
10
5176
15
66.67%
169
20
2
18989.42%
186
4
1
190
97.89%
186
4
1
190
97.89%

2839
2
67
41.79%
57
11
1
68
83.82%
13452
1776.47%
42
14
13
56
75.00%
56
5
8
61
91.80%
6162
785.71%
68
1
0
69
98.55%
68
1
0
69
98.55%
68
1
0
69
98.55%

All
235
332
408
567
41.45%
457
102
416
559
81.75%
130
24
821
154
84.42%
645
165
165
810
79.63%
503
91
381
594
84.68%
72
18
885
90
80.00%
819
77
79
896
91.41%
890
10
75
900
98.89%
890
10
75
900
98.89%

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Indicator

SR-HP1.18

SR-HP1.19

SR-HP1.20

SR-HP2.1

SR-HP2.2

SR-HP2.3

ComplianceTime spent by workers in any
required trainings and meetings
is considered working time andworkers are compensated attheir normal rate
Financial disciplinary penalties
are not assessed against
workers

Provident/pension plan thatexceeds legal requirements is inplace for full-time employees

Workers have either directcommunication or designatedrepresentative with management
or employer

Workers can air workplacegrievances to management or
employer with no fear of reprisal

Workers' right to organize and/orcollectively bargain in
accordance with national laws
and international obligations is
acknowledged by management

SR-HP2.4

There is a workers' association
formed and governed by theemployees independent ofmanagement influence

SR-HP2.5

There are regular meetings
between management andemployees or employees'
association

SR-HP2.6

March 2011

Regular meetings between
management and employees (oremployees' association)
continually improve working
conditions

C
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% Compliance

Wet

Mill Type
Dry

Both

435
33
247
468
92.95%
637
375
640
99.53%
81
293
341
374
21.66%
643
171
644
99.84%
631
6
78
637
99.06%
509
21
185
530
96.04%
70
295
350
365
19.18%
470
103
142
573
82.02%
363
191
161
554
65.52%

169
5
17
174
97.13%
187
2
2
189
98.94%
53
99
39
152
34.87%
189
0
2
189
100.00%187
2
2
189
98.94%
145
739
152
95.39%
2779
85
106
25.47%
141428
183
77.05%
98
81
12
179
54.75%

66
2
1
68
97.06%
68
1
0
69
98.55%
35
32
2
6752.24%
6900
69
100.00%69
0
069
100.00%62
2
56496.88%
2520
24
45
55.56%
644
1
68
94.12%
61
6
2
67
91.04%

All
670
40
265
710
94.37%
892
6
77
898
99.33%
169
424
382
593
28.50%
901
1
73
902
99.89%
8878
80895
99.11%
716
30
229
746
95.98%
122
394
459
516
23.64%
675
149
151
824
81.92%
522
278
175
800
65.25%

119

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Indicator

SR-HP2.7

SR-HP2.8

SR-HP2.9

SR-HP3.1

SR-HP3.2

There is a workers' association
fund to which management
and workers contributematching funds

Workers have equal access to
association fund to financeprojects that improveconditions for workers
A collective bargaining
agreement exists between
employees and employer inregions or countries where
agricultural workers'organizations are establishedin the coffee sector
Workers do not work moreregular hours per day or week
than allowable under local
laws

Permanent workers must havethe equivalent of one
continuous 24 period off ineach 7 day period or whateveris required by law

SR-HP3.3

SR-HP3.4

Hours worked on potentiallyhazardous activities are
restricted in accordance withthe law

Employer has a paid sickleave program for full-time
employees

Employer has a paid sickleave program for all workers
SR-HP3.6

Wet

Mill Type
Dry

Both

32
109
574
141
22.70%
3167
617
98
31.63%

10
64
117
74
13.51%
1439
138
53
26.42%

219
39
30
70.00%
231
45
24
95.83%

C

46

18

9

73

NC

84

25

7

116

NA

585

148

53

786

Compliance

Workers must not work moretotal hours in one day or week
than allowable underapplicable laws

SR-HP3.5

March 2011

C
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% Compliance

All
63
182
730
245
25.71%
68
107
800
175
38.86%

Total (C+NC)

130

43

16

189

% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% Compliance35.38%
582
56
77
638
91.22%
375
17
323
392
95.66%
581
53
81
634
91.64%
312
44
359
356
87.64%
312
55
348
367
85.01%
288
321
421
609
47.29%

41.86%
171
19
1
190
90.00%
159
7
25
166
95.78%
168
21
2
189
88.89%
97
16
78
113
85.84%
148
439
152
97.37%
9392
6
185
50.27%

56.25%
65
4
0
69
94.20%
64
5
0
69
92.75%
62
7
0
69
89.86%
31
929
40
77.50%
65
3
1
68
95.59%
57
12
0
69
82.61%

38.62%
818
79
78
897
91.19%
598
29
348
627
95.37%
811
81
83
892
90.92%
440
69
466
509
86.44%
525
62
388
587
89.44%
438
425
427
863
50.75%

120

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Indicator

SR-HP3.7

SR-HP3.8

SR-HP3.9

SR-HP4.1

SR-HP4.2

SR-HP4.3

SR-HP4.4

SR-HP4.5

SR-HP4.6

March 2011
ComplianceIf overtime work is required aspart of the job, suchrequirements are clear at thetime of hiring

Employer has an annual leave(vacation) program that meets
applicable laws

If employees do not takeannual leave, employer caneither allow vacation time toaccrue or can pay wages forthe equivalent amount of time
accrued under the regular pay
scale
Employer does not directly
contract any persons under theage of 14

Employment of authorizedminors older than 14 does notconflict with their access to
education

Management has an enforced
policy prohibiting
discrimination on the basis ofgender, race, ethnicity, age orreligion as per ILO Convention
111
Employer prohibits the use offorced, bonded, indentured or
involuntary convict labor

Employer observes all legal
requirements for the work ofauthorized minors

Workers do not surrender theiridentity papers or other original
personal documents or pay
deposits as a condition of
employment

C
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% ComplianceMill Type
Dry

Wet

All

Both

350
50
315
400
87.50%
345
56
314
401
86.03%

151
26
14
177
85.31%
150
6
35
156
96.15%

6801
68100.00%64
326795.52%

569
76
330
645
88.22%
559
65
351
624
89.58%

C

258

100

24

382

NC

53

12

4

69

NA

404

79

41

524

311
82.96%
6542
59
656
99.70%
127
0
588
127
100.00%
657
5
53
662
99.24%
661
0
54
661
100.00%
109
3
603
112
97.32%
633
8
74
641
98.75%

112
89.29%
186
0
5
186
100.00%79
0112
79
100.00%
188
0
53
188
100.00%187
04
187
100.00%72
0603
72
100.00%189
02
189
100.00%2885.71%
6900
69
100.00%18051
18100.00%69
0
069
100.00%64
0564
100.00%13056
13100.00%69
0
069
100.00%45184.70%
909
264
911
99.78%
224
0
751
224
100.00%
914
5
106
919
99.46%
9120
63
912
100.00%
194
3
1262
197
98.48%
891
8
76
899
99.11%

Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% Compliance121

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Public Version
Indicator

SR-HP4.7

March 2011
Compliance

All workers receive equal pay
for equal work, except in caseof recognizing seniority ofservice through higher pay

CNCNA
Total (C+NC)% Compliance

Wet
608
15
92
623
97.59%

Mill Type
Dry
170
1
20
171
99.42%

Both
69
0
0
69
100.00%

All
847
16
112
863
98.15%

122

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Indicator

Workers have ready accessto potable water
SR-WC1.2

SR-WC1.4

SR-WC1.5

SR-WC2.1

SR-WC2.2

Wet

Mill Type
Dry

Both

399
31
285
430
92.79%
532
103
80
635
83.78%

64
1
126
65
98.46%
136
649
142
95.77%

470
22
47100.00%65
1
3
66
98.48%

510
32
433
542
94.10%
733
110
132
843
86.95%

C

351

27

31

409

NC

69

0

3

72

NA

295

164

35

494

Total (C+NC)

420

27

34

481

% ComplianceC
NC
NA
Total (C+NC)% Compliance83.57%
525
100
385
625
84.00%

100.00%
139
4
48
143
97.20%

91.18%
63
3
3
66
95.45%

85.03%
727
107
436
834
87.17%

C

458

78

57

593

NC

84

4

5

93

NA

173

109

7

289

Total (C+NC)

542

82

62

686

% Compliance84.50%

95.12%

91.94%

86.44%

C

282

59

26

367

NC

5

0

0

5

NA

428

132

43

603

287
98.26%

59
100.00%26
100.00%372
98.66%

C

200

58

23

281

NC

35

0

0

35

NA

480

133

46

659

235
85.11%

58
100.00%23
100.00%31688.92%

Compliance

Working Conditions
Part-time/seasonal and fulltime workers living onsite
have habitable dwellings
SR-WC1.1

SR-WC1.3

March 2011

Worker housing has bufferzones from productive area
and agrochemical storage
facilities sufficient to prevent
agrochemical exposure

Workers have ready accessto sanitary facilities that do
not impact the local
environment

Garbage from employerprovided housing andfacilities is removed to either
a municipal waste dump or
to a waste site located atleast 200 meters from any
water body
Where there is insufficient
access to public education,schoolchildren (of primaryschool age) of workers wholive on-site have access toprimary educational
instruction, facilities andmaterials that meet national
requirements
Where there is insufficient
access to public education,schoolchildren (of secondary
school age) of workers wholive on-site have access toprimary educational
instruction, facilities andmaterials that meet national
requirements

C
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% ComplianceTotal (C+NC)% ComplianceTotal (C+NC)% Compliance

All

123

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Indicator

SR-WC2.3

SR-WC2.4

SR-WC2.5

SR-WC3.1

Compliance
Employer provides direct
incentives for education in theform of scholarships,
educational materials, books,
transportation, food, etc.
Where there is convenient
access to public education
programs, employer supports
those schools either throughin-kind donation or financial
support
Employer supports training orworkshops on additional skills
or trades

Employer has
transportation/care plan inplace in case of medical
emergency

Employer provides sufficient
onsite first aid kits
SR-WC3.2

SR-WC3.3

SR-WC3.4

SR-WC3.5

Where there is convenient
access to public medical care,
employer supports thesefacilities either through in-kind
donation or financial support
Employer or manager
maintains communication withworkers in case of medical
emergencies

Employer offsets the cost ofhealth services for permanent
workers

Employer offsets the cost ofhealth services for all workers
SR-WC3.6

March 2011
Mill Type
Dry

Wet

All

Both

C

312

60

48

420

NC

239

111

17

367

NA

164

20

4

188

Total (C+NC)

551

171

65

787

% Compliance56.62%

35.09%

73.85%

53.37%

C

374

64

56

494

NC

245

111

11

367

NA

96

16

2

114

Total (C+NC)

619

175

67

861

% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% Compliance60.42%
321
296
98
617
52.03%
568
116
31
684
83.04%
514
177
24
691
74.38%

36.57%
115
72
4
187
61.50%
1825
4
187
97.33%
173
16
2
189
91.53%

83.58%
5612
1
68
82.35%
6900
69
100.00%65
4
069
94.20%

57.38%
492
380
103
872
56.42%
819
121
35
940
87.13%
752
197
26
949
79.24%

C

207

52

40

299

NC

372

124

25

521

NA

136

15

4

155

Total (C+NC)

579

176

65

820

% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% Compliance35.75%
639
36
40
675
94.67%
324
36
355
360
90.00%
423
217
75
640
66.09%

29.55%
184
4
3
188
97.87%
144
9
38
153
94.12%
93
95
3
188
49.47%

61.54%
6900
69
100.00%64
1
465
98.46%
615
3
66
92.42%

36.46%
892
40
43
932
95.71%
532
46
397
578
92.04%
577
317
81
894
64.54%

124

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Indicator

SR-WC4.1

Employer provides personal
protective equipment to allapplicable employees at no cost

SR-WC4.2

SR-WC4.3

Training occurs at regularintervals, at a minimum yearly,
free of charge, and during
regular working hours

SR-WC4.5

SR-WC4.6

SR-WC4.7

SR-WC4.8

Wet

Mill Type
Dry

Both

391
177
147
568
68.84%
229
380
106
609
37.60%
308
252
155
560
55.00%
270
251
194
521
51.82%
419
143
153
562
74.56%

138
177
6
315
43.81%
66
123
2
189
34.92%
100
88
3
188
53.19%
9084
17174
51.72%
132
49
10
181
72.93%

60
8
1
6888.24%
51
16
2
67
76.12%
53
15
1
68
77.94%
53
13
3
66
80.30%
56
7
6
63
88.89%

589
362
154
951
61.93%
346
519
110
865
40.00%
461
355
159
816
56.50%
413
348
214
761
54.27%
607
199
169
806
75.31%

C

208

55

55

318

NC

371

119

12

502

NA

136

17

2

155

Total (C+NC)

579

174

67

820

% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% Compliance35.92%
312
183
220
495
63.03%
110
28
577
138
79.71%

31.61%
127
34
30
161
78.88%
52
184
771.43%

82.09%
57
7
5
6489.06%
17052
17100.00%38.78%
496
224
255
720
68.89%
132
30
813
162
81.48%

Compliance

There is a written record
documenting instructor,
materials, and attendance for all
health and safety training

SR-WC4.4

March 2011

Training covers, at a minimum:use of protective equipment,
safe handling of hazardous
materials, operation ofequipment and personalsafety/hygiene
There are regular safety
meetings for all relevant
workers

There is a written protocol for
workers and management toreview safety procedures andtraining materials in the event ofaccidents, exposures tohazardous materials or spills

Workers utilize appropriate
protective equipment when
applying agrochemicals andoperating machinery

All workers who
handle/mix/apply agrochemicals
have access to eye baths, handwashing and showers posthandling of agrochemicals

CNC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% Compliance

All

125

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Indicator

SR-WC4.9

SR-WC4.10

March 2011
Compliance

Authorized minors andpregnant women areprohibited from handling or
applying agrochemicals OR
operating heavy machinery
Workers do not enter areas
where pesticides were appliedin the prior 48 hours without
protective gear

C
NC
NA
Total (C+NC)% ComplianceC
NC
NA
Total (C+NC)% Compliance

Wet

Mill Type
Dry

Both

361
10
344
371
97.30%
64
21
630
85
75.29%

113
0
78
113
100.00%70184
7100.00%41028
41100.00%7260
977.78%

All
515
10
450
525
98.10%
78
23
874
101
77.23%

126

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Indicator
CP-WC1CP-WC1.1

CP-WC1.2

CP-WC1.3

Compliance
Minimizing Water Consumption
The total volume of water used for
pulping, washing, and sorting for coffee
processing operations is tracked andrecordedIf water is used for separating coffee
cherry before pulping, then a siphon ofless than 3 cubic meters is used

Processing facility recycles water fortransporting coffee prior to pulping and for
pulping

The amount of water used on a per unit
basis shows a decrease over time
CP-WC1.4

The ratio between coffee cherry andwater is no more than 1:1
CP-WC1.5

CP-WC2CP-WC2.1

CP-WC2.2

CP-WC2.3

March 2011

Reducing Wastewater Impacts
Wastewater from pulping and washing ismanaged in a way that does not impact
the local or surrounding environment

If wastewater from pulping and washing isreleased into a leach field or lagoon orsprayed onto fields, the boundary of the
fields or lagoon is a minimum of 40m fromall watercourses
If wastewater is discharged to awatercourse or a sewer, the following
wastewater tests are conducted at all exit
points (and meet establishedenvironmental regulatory norms) on amonthly basis during operations:biological oxygen demand, chemical
oxygen demand, pH

Size & Mill Type
Small - Wet
Wet

Dry

C
NC
Total (C+NC)
NA% ComplianceC
NC
Total (C+NC)
NA% ComplianceC
NC
Total (C+NC)
NA% ComplianceC
NC
Total (C+NC)
NA% ComplianceC
NC
Total (C+NC)
NA% Compliance

1142
8532
9674
24530
11.81%
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
4079
5771
9850
24354
41.41%
1343
8826
10169
24035
13.21%

396
372
768
16
51.56%
275
210
485
299
56.70%
372
318
690
94
53.91%
244
353
597
187
40.87%
277
406
683
101
40.56%

NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA

C
NC
Total (C+NC)
NA% Compliance

21909
8482
30391
3813
72.09%

633
145
778
6
81.36%

NA
NA
NA
NA
NA

C

NA

452

NA

NC

NA

153

NA

Total (C+NC)

NA

605

NA

NA

NA

179

NA

% ComplianceNA74.71%

NA

C

1280

86

NA

NC

4570

149

NA

Total (C+NC)

5850

235

NA

NA

28354

549

NA

% Compliance21.88%

36.60%NA

127

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Indicator

March 2011
Compliance

CP-WC2.4

There is no evidence ofcontamination from processing
operations of neighboring or local
water bodies

CP-WM1

Waste Management Operations/Beneficial Reuse

CP-WM1.1

CP-WM1.2

CP-WM1.3

CP-WM1.4

CP-WM2CP-WM2.1

CP-EC1CP-EC1.1, 2.1

Processing wastes are managed insuch a way as to not contaminate thelocal environment

Skin, pulp, mucilage andunacceptable cherries are composted
or processed by worms

Organic processing byproducts areused as soil amendments by the farmor, in the case of a standalone
processor, distributed to local farmers

Solids are recovered fromsedimentation pools, composted andused by coffee farms

Waste Management
Parchment hulls from dry milling arerecovered for use in mechanicalcoffee dryers, generating energy orother beneficial uses

Energy Conservation/Impacts
The quantity of energy used on-site
for coffee processing operations shallbe reported

C
NC
Total (C+NC)
NA% Compliance

Size & Mill Type
Small - Wet
Wet
NA
613
NA
149
NA
762
NA
22
NA
80.45%

Dry
NA
NA
NA
NA
NA

C
NC
Total (C+NC)
NA% ComplianceC
NC
Total (C+NC)
NA% ComplianceC
NC
Total (C+NC)
NA% ComplianceC
NC
Total (C+NC)
NA% Compliance

25835
4921
30756
3448
84.00%
22049
10502
32551
1653
67.74%
26913
2775
29688
4516
90.65%
NA
NA
NA
NA
NA

699
79
778
6
89.85%
682
94
776
8
87.89%
732
38
770
14
95.06%
538
157
695
89
77.41%

NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA

C
NC
Total (C+NC)
NA% Compliance

NA
NA
NA
NA
NA

NA
NA
NA
NA
NA

218
12
230
30
94.78%CNC
Total (C+NC)
NA% Compliance

NA
NA
NA
NA
NA

431
299
730
54
59.04%

219
19
238
22
92.02%

128

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Indicator

Compliance
Parchment coffee is patio dried or driedin other energy efficient ways

CP-EC1.2,
2.2

The quantity of wood/other fuel used fordrying coffee is recorded
CP-EC1.3,
2.3

CP-EC1.4,
2.4

CP-EC1.5,
2.5

CP-EC1.6,
2.6

CP-EC1.7,
2.7

March 2011

Wood used for drying coffee comes frompruning of shade trees, responsibly
managed forests, or other minimal
impact harvests

The total amount of energy used per
pound of green coffee show a decreaseover time

Operation shows commitment to the
production of renewable energy throughthe production or purchase of energy
produced using renewable and highefficiency technologies

All on-site energy production operations
are demonstrably operated inaccordance with local permitting
requirements or they meet the maximum
practicable emissions standards

CNC
Total (C+NC)
NA% ComplianceC
NC
Total (C+NC)
NA% ComplianceC
NC
Total (C+NC)
NA% ComplianceC
NC
Total (C+NC)
NA% Compliance

Size & Mill Type
Small - Wet
Wet
68245
617
708
27
68953
644
2738
140
98.97%
95.81%
NA
202
NA
41
NA
243
NA
541
NA
83.13%
NA
192
NA
1
NA
193
NA
591
NA
99.48%
NA
183
NA
349
NA
532
NA
252
NA
34.40%

Dry
165
6
171
89
96.49%
52
456
204
92.86%
411
42
218
97.62%
109
97
206
54
52.91%

C

NA

368

139

NC

NA

331

86

Total (C+NC)
NA

NA
NA

699
85

225
35

% ComplianceC
NC
Total (C+NC)
NA% Compliance

NA
NA
NA
NA
NA
NA

52.65%
423
54
477
307
88.68%

61.78%
197
6
203
57
97.04%

129

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March 2011

Appendix D: Farm Reporting Rates for C.A.F.E. Practices Indicators
Indicator
Social Responsibility
Hiring Practices and Employment Policies
Minimum/ Living
Wage/Overtime
Regulation

SR-HP1.1

SR-HP1.2

SR-HP1.3
SR-HP1.9
SR-HP1.10
SR-HP1.11

SR-HP1.12

SR-HP1.13

All part-time workers are paid thenationally or regionally establishedminimum wage
All temporary/seasonal workers are paid
the nationally or regionally establishedminimum wage
Overtime pay meets national
requirements
Overtime pay exceeds national
requirements
All full-time workers are paid more than
the nationally or regionally establishedminimum wage
All part-time workers are paid more than
the nationally or regionally establishedminimum wage
All temporary/seasonal workers are paid
more than the nationally or regionallyestablished minimum wage

Medium

Large

4%

61%

98%

3%

18%

29%

66%

92%

94%

17%

25%

52%

19%

23%

51%

4%

61%

93%

3%

18%

29%

66%

92%

94%

6%

61%

92%

NA

58%

93%

NA

61%

93%

SR-HP3.5

Full-time workers are paid at least a living
wage
Provident/pension plan that exceeds legal
requirements is in place for full-time
employees
Employer has a paid sick leave programfor full-time employees

SR-HP3.6

Employer has a paid sick leave programfor all workers

NA

88%

97%

SR-HP3.8

Employer has an annual leave (vacation)
program that meets applicable laws

NA

60%

93%

79%

95%

98%

32%

23%

28%

NA

95%

100%

SR-HP1.14
SR-HP1.20
Vacation/ Sick
Leave
Regulation

All full-time workers are paid thenationally or regionally establishedminimum wage

Small

Child Labor/
Discrimination/
Forced Labor

Employer does not directly contract any
persons under the age of 14
SR-HP4.1

SR-HP4.2

SR-HP4.3

Employment of authorized minors older
than 14 does not conflict with their accessto education
Management has an enforced policy
prohibiting discrimination on the basis ofgender, race, ethnicity, age or religion as
per ILO Convention 111

130FINAL REPORT - FY08 Results Assessment
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Indicator
Social ResponsibilityChild Labor/
Discrimination/ SR-HP4.4
Forced Labor
SR-HP4.7

March 2011
Small

MediumLarge

Employer prohibits the use of forced, bonded,indentured or involuntary convict labor

73%

94%

98%

All workers receive equal pay for equal work,
except in case of recognizing seniority ofservice through higher pay

NA

95%

99%

Part-time/seasonal and full-time workers living
onsite have habitable dwellings

10%

66%

91%

64%

92%

97%

NA

67%

165%

NA

91%

97%

NA

34%

54%

NA

27%

46%

NA

82%

96%

NA

90%

95%

NA

92%

100%

NA

81%

89%

NA

64%

94%

Worker Conditions
Access to
Housing,
Water and
Sanitary
Facilities

SR-WC1.1
SR-WC1.2
SR-WC1.3

SR-WC1.4
Access to
Education
SR-WC2.1

SR-WC2.2

SR-WC2.3

SR-WC2.4

SR-WC2.5
Access to
Medical Care

SR-WC3.3

SR-WC3.5

Workers have ready access to potable water
Worker housing has buffer zones fromproductive area and agrochemical storage
facilities sufficient to prevent agrochemical
exposure
Workers have ready access to sanitaryfacilities that do not impact the local
environment
Where there is insufficient access to public
education, schoolchildren (of primary schoolage) of workers who live on-site have accessto primary educational instruction, facilities
and materials that meet national requirements
Where there is insufficient access to public
education, schoolchildren (of secondary
school age) of workers who live on-site haveaccess to primary educational instruction,
facilities and materials that meet national
requirements
Employer provides direct incentives foreducation in the form of scholarships,
educational materials, boos, transportation,
food, etc.
Where there is convenient access to public
education programs, employer supports thoseschools either through in-kind donation or
financial support
Employer supports training or workshops on
additional skills or trades
Where there is convenient access to public
medical care, employer supports thesefacilities either through in-kind donation or
financial support
Employer offsets the cost of health services
for permanent workers

131FINAL REPORT - FY08 Results Assessment
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Indicator
Social Responsibility
Access to
Medical Care

SR-WC3.6

Employer offsets the cost of health services for all
workers

Access to
Employer provides personal protective equipment
Training,
to all applicable employees at no cost
SR-WC4.1
Health &SafetyCoffee Growing -- Environmental Leadership
Protecting Water Resources
Watercourse
Protection

Medium

Large

NA

92%

98%

39%

78%

98%

Watercourse buffer zones are maintained adjacent
to at least 25% of water bodies (>2m in width)

NA

69%

78%

CG-WR1.2

Watercourse buffer zones are maintained adjacent
to at least 50% of water bodies (>2m in width)

28%

69%

78%

CG-WR1.3

Watercourse buffer zones are maintained adjacent
to all of water bodies (>2m in width)

27%

69%

79%

CG-WR1.4

Watercourse buffer zones are maintained adjacent
to at least 50% of seasonal/intermittent
watercourses (>2m in width)

NA

49%

62%

NA

49%

62%

NA

66%

79%

CG-WR1.7

Watercourse buffer zones of at least 5m per side
are maintained adjacent to allseasonal/intermittent and water bodies

NA

68%

81%

CG-WR1.12

There is a plan to restore native vegetation within
the buffers

NA

64%

78%

CG-WR1.14

At least 50% of watercourse buffer zones arecomposed of native woody vegetation

26%

73%

82%

CG-WR1.15

All watercourse buffer zones are composed ofnative woody vegetation

26%

73%

83%

CG-WR2.1

There is no application of agrochemicals within
10m of any water body or watercourse

45%

73%

85%

CG-WR2.2

There is no application of nematicides within 20mof any water body or watercourse

NA

55%

60%

CG-WR2.4

Synthetic fertilizers are not used or the farm iscertified organic

99%

90%

94%

CG-WR3.1

Irrigation water use is tracked and recorded per lb.
of green coffee, per hectare and for each plot

NA

2%

6%

CG-WR3.2

There is a hydrological balance assessment,
including climatic, community and competing
agricultural issues

NA

4%

8%

CG-WR3.3

Irrigation water use doesn't exceed the operation's
net neutral hydrological balance

NA

1%

5%

CG-WR1.6

Water andResources
Irrigation

Small

CG-WR1.1

CG-WR1.5

Water Quality
Protection

March 2011

Watercourse buffer zones are maintained adjacent
to all seasonal/intermittent watercourses (>2m inwidth)
Watercourse buffer zones of at least 5m per side
are maintained adjacent to at least 50% of
seasonal/intermittent and water bodies

132FINAL REPORT - FY08 Results Assessment
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Indicator
Coffee Growing -- Environmental Leadership
Protecting Soil Resources
Controlling
Surface
ErosionMarch 2011
Small

MediumLarge

CG-SR1.1

Farm managers demonstrate knowledge offarm areas at risk to erosion

92%

93%

99%

CG-SR1.2

There is an explicit soil management plan that
includes measures to minimize surface erosion

NA

96%

99%

64%

91%

97%

74%

94%

96%

74%

94%

96%

73%

94%

96%

63%

92%

93%

63%

92%

93%

63%

91%

93%

55%

87%

87%

54%

85%

87%

54%

85%

87%

98%

94%

99%

NA

71%

80%

CG-SR1.3
CG-SR1.4

CG-SR1.5

CG-SR1.6

CG-SR1.7

CG-SR1.8

CG-SR1.9

CG-SR1.10

CG-SR1.11

CG-SR1.12

CG-SR1.13

CG-SR1.16

Areas at high risk of erosion are identified on amap
Productive areas on at least 25% of slopes
over 10% slope are covered by shade trees
and/or cover crops/vegetation
Productive areas on at least 50% of slopes
over 10% slope are covered by shade trees
and/or cover crops/vegetation
Productive areas on all of slopes over 10%
slope are covered by shade trees and/or covercrops/vegetation
Productive areas on at least 25% of slopes
over 20% slope are covered by shade trees
and/or cover crops/vegetation
Productive areas on at least 50% of slopes
over 20% slope are covered by shade trees
and/or cover crops/vegetation
Productive areas on all of slopes over 20%
slope are covered by shade trees and/or covercrops/vegetation
Productive areas on at least 25% of slopes
over 30% slope are covered by shade trees
and/or cover crops/vegetation
Productive areas on at least 50% of slopes
over 30% slope are covered by shade trees
and/or cover crops/vegetation
Productive areas on all of slopes over 30%
slope are covered by shade trees and/or covercrops/vegetation
Herbicides are not used to control groundvegetation/cover crops and are only used inspot applications for aggressive weeds
Areas in which the risk of landslides is extreme
are not cultivated, are left or taken out of
production and restored with native vegetation
where practicable133

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Indicator
Coffee Growing -- Environmental Leadership
Maintaining
At least 25% of the productive area is covered
Soilby an organic matter layer and/or nitrogenCG-SR2.1
Productivity
fixing cover crops

March 2011
Small

Medium

Large

100%

98%

100%

100%

98%

100%

100%

98%

100%

100%

98%

100%

CG-SR2.5

At least 50% of the productive area is coveredby an organic matter layer and/or nitrogenfixing cover crops
All of the productive area is covered by anorganic matter layer and/or nitrogen-fixing
cover crops
At least 25% of the productive area is plantedwith nitrogen-fixing, leguminous trees

CG-SR2.6

At least 50% of the productive area is plantedwith nitrogen-fixing, leguminous trees

99%

98%

99%

CG-SR2.7

All of the productive area is planted withnitrogen-fixing, leguminous trees

98%

98%

99%

CG-SR2.12
Conserving Biodiversity

Farm is certified organic

NA

97%

97%

Maintaining a
Coffee Shade
CanopyCG-CB1.1

Native trees are removed only when they
constitute a human hazard or when theysignificantly compete with coffee plants

79%

96%

97%

CG-CB1.2

The farm has a shade management plan

NA

95%

99%

CG-SR2.2

CG-SR2.3

NA

82%

87%

CG-CB1.4

The farm is implementing the shade
management plan and meeting the timeline foractions
Farm is covered by at least 10% canopy cover

99%

97%

100%

CG-CB1.5

Canopy cover is comprised of a diversity oftree species

92%

98%

99%

CG-CB1.6

Canopy cover is retained at biologicallysignificant levels

94%

97%

99%

CG-CB1.7

Invasive exotic species are not used forcanopy cover

92%

97%

99%

CG-CB1.8

An average of 40% canopy cover is maintained
across the productive area of the farm

NA

96%

99%

92%

98%

99%

CG-CB1.10

Where local ecological conditions allow, shadecanopy is comprised of at least 2
distinguishable canopy strata

NA

92%

92%

CG-CB1.11

Locally native epiphytes are retained in the
canopy cover

60%

94%

96%

CG-CB1.3

CG-CB1.9

At least 75% of the canopy cover is comprisedof locally native species and/or the canopy
consists of at least 10 species that are locallynative or can be shown to contribute to theconservation of native biodiversity

134FINAL REPORT - FY08 Results Assessment
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Indicator
Coffee Growing -- Environmental Leadership
Biological legacies, such as cavity trees andMaintaining astanding and/or fallen dead trees, are retained
Coffee Shade
CG-CB1.12
or recruited
CanopyProtecting
Wildlife

Small

Medium

Large

63%

95%

99%

CG-CB2.1

There are specific implemented measures torestrict unauthorized hunting and commercial
collection of flora and fauna

70%

98%

100%

CG-CB2.2

Hunting threatened or rare wildlife species is
not allowed on the property

66%

96%

97%

NA

97%

99%

NA

97%

99%

NA

92%

96%

NA

76%

80%

98%

92%

94%

NA

30%

27%

NA

88%

95%

NA

72%

84%

NA

34%

21%

NA

32%

19%

CG-CB2.3

CG-CB2.4

CG-CB2.5

CG-CB2.6
Conservation
Areas and
EcologicalReserves

March 2011

Farm management has created a list of wildlife
species native to the region and identified
which of those species are classified asvulnerable, endangered or critically endangeredaccording to the IUCN red list
Farm management has consulted with relevant
government agencies or universities todetermine which wildlife species are native tothe farm's region and classified as vulnerable,
endangered, or critically endangered
A Wildlife Management Plan is developed on
the basis of the results gathered from theconsultations with relevant government
agencies or universities
The Wildlife Management Plan is properly
implemented on the farm
After March 2004, there is no conversion ofnatural forest to agricultural production

CG-CB3.1

CG-CB3.2

CG-CB3.3

CG-CB3.5

CG-CB3.6

CG-CB3.7

If areas of natural vegetation are converted toagricultural production, these are not areas ofhigh ecological value and there are equivalent
set-asides
There is an assessment of the farm for areas ofhigh ecological value
Areas of identified high ecological value areclearly defined, protected and managed with aconservation emphasis that maintains the highecological values
If areas of high ecological value are completely
lacking on the farm, there is a plan to restore
natural habitat, forest and other naturalvegetative areas on a portion of the farm
If areas of high ecological value are completely
lacking on the farm, managers have
implemented the plan to restore natural habitator condition on a portion of the farm

135

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Indicator
Coffee Growing -- Environmental Leadership
Conservation
Areas of high ecological value are protected from
Areas andfuture development through the declaration of
CG-CB3.8
Ecologicalprivate reserves, conservation emphasis areas or
Reserves
legal conservation easements

March 2011
Small

Medium

Large

NA

64%

81%

CG-CB3.9

At least 3% of the farm is set aside as aconservation emphasis area

NA

95%

99%

CG-CB3.10

At least 5% of the farm is set aside as aconservation emphasis area

NA

94%

99%

CG-CB3.11

Biological corridors are established to connect
multiple conservation emphasis areas

NA

74%

87%

CG-CB3.12

Multiple plant species selected for contribution tobiodiversity are established where space allows
within the farm

NA

96%

99%

Environmental Management and Monitoring
Ecological
Pest and
DiseaseManagement
and Reducing
Agrochemical
Use

CG-EM1.1

Farms do not use chemicals that are listed by theWorld Health Organization as Type 1A or 1B,
except as specified by the nematode amendment

94%

92%

100%

CG-EM1.7

Farms calculate total toxic load of productive
area on farm

NA

64%

91%

NA

55%

73%

NA

95%

99%

CG-EM1.19

Toxic load is decreased over time throughreduction in agrochemical use or selecting lesstoxic alternatives
There is a written Integrated Pest Management
Plan that is properly implemented in the field

CG-EM2.3

Farm mangers develop a written management
plan and supporting documents

NA

96%

99%

CG-EM2.4

Farm managers implement the written
management plan

NA

81%

90%

CG-EM2.5

The management plan is updated on an annual
basis

NA

78%

85%

CG-EM1.8

FarmManagement
and Monitoring
Practices

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March 2011

Appendix E: Mill Reporting Rates for C.A.F.E. Practices Indicators
Indicator
Social Responsibility
Hiring Practices and Employment Policies
Wages andAll full-time workers are paid the nationallySR-HP1.1
Benefitsor regionally established minimum wage
All part-time workers are paid the
SR-HP1.2
nationally or regionally establishedminimum wage
All temporary/seasonal workers are paid
SR-HP1.3
the nationally or regionally establishedminimum wage
Management maintains complete written
SR-HP1.4
earning records
Workers are paid regularly in cash/cash
SR-HP1.5
equivalent

Small
Wet

Wet

Dry

Both

NA

48%

81%

99%

NA

11%

30%

25%

NA

83%

84%

81%

NA

89%

99%

100%

NA

92%

99%

100%

SR-HP1.6

If nationally legally mandated benefits are
required for full-time workers, then theseare paid by employer

NA

47%

75%

90%

SR-HP1.7

If nationally legally mandated benefits are
required for part-time workers, then theseare paid by employer

NA

10%

9%

20%

SR-HP1.8

If nationally legally mandated benefits are
required for seasonal/temporary workers,
then these are paid by employer

NA

61%

54%

64%

SR-HP1.9

Overtime pay meets national requirements

NA

51%

81%

97%

NA

49%

79%

97%

NA

47%

81%

99%

NA

11%

30%

25%

NA

83%

84%

81%

NA

53%

82%

88%

NA

10%

8%

10%

NA

89%

99%

100%

NA

90%

99%

100%

NA

65%

91%

99%

NA

90%

99%

100%

NA

52%

80%

97%

SR-HP1.10
SR-HP1.11

SR-HP1.12

SR-HP1.13
SR-HP1.14
SR-HP1.15
SR-HP1.16
SR-HP1.17
SR-HP1.18

SR-HP1.19
SR-HP1.20

Overtime pay exceeds national
requirements
All full-time workers are paid more than
the nationally or regionally establishedminimum wage
All part-time workers are paid more than
the nationally or regionally establishedminimum wage
All temporary/seasonal workers are paid
more than the nationally or regionallyestablished minimum wage
Full-time workers are paid at least a living
wage
In-kind payments are itemized in writing
Workers have access to their earnings
records
Wages are paid directly to all workers
Time spent by workers in any required
trainings and meetings is consideredworking time and workers arecompensated at their normal rate
Financial disciplinary penalties are notassessed against workers
Provident/pension plan that exceeds legal
requirements is in place for full-time
employees

137FINAL REPORT - FY08 Results Assessment
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Indicator
Freedom of
Association/
Collective
Bargaining

Small Wet
SR-HP2.1

SR-HP2.2

SR-HP2.3

SR-HP2.4

SR-HP2.5

SR-HP2.6

SR-HP2.7

SR-HP2.8

SR-HP2.9

Vacation/Sick Leave
Regulation

March 2011

SR-HP3.1

SR-HP3.2

SR-HP3.3

SR-HP3.4
SR-HP3.5
SR-HP3.6

Workers have either directcommunication or designatedrepresentative with management or
employer
Workers can air workplace grievances tomanagement or employer with no fear of
reprisalWorkers' right to organize and/orcollectively bargain in accordance withnational laws and international
obligations is acknowledged bymanagement
There is a workers' association formed
and governed by the employees
independent of management influence
There are regular meetings between
management and employees or
employees' association
Regular meetings between management
and employees (or employees'
association) continually improve working
conditions
There is a workers' association fund towhich management and workers
contribute matching funds
Workers have equal access to
association fund to finance projects that
improve conditions for workers
A collective bargaining agreement exists
between employees and employer inregions or countries where agricultural
workers' organizations are established in
the coffee sector
Workers do not work more regular hours
per day or week than allowable underlocal laws
Permanent workers must have theequivalent of one continuous 24 period
off in each 7 day period or whatever isrequired by law
Workers must not work more total hours
in one day or week than allowable underapplicable laws
Hours worked on potentially hazardous
activities are restricted in accordancewith the law
Employer has a paid sick leave programfor full-time employees
Employer has a paid sick leave programfor all workers

Wet

Dry

Both

NA

90%

99%

100%

NA

89%

99%

100%

NA

74%

80%

93%

NA

51%

55%

65%

NA

80%

96%

99%

NA

77%

94%

97%

NA

20%

39%

43%

NA

14%

28%

35%

NA

18%

23%

23%

NA

89%

99%

100%

NA

55%

87%

100%

NA

89%

99%

100%

NA

50%

59%

58%

NA

51%

80%

99%

NA

85%

97%

100%

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Indicator
Vacation /Sick
Leave
Regulation

SR-HP3.7

SR-HP3.8

SR-HP3.9Child Labor/
Discrimination/
Forced Labor

SR-HP4.1
SR-HP4.2

SR-HP4.3

SR-HP4.4

SR-HP4.5

SR-HP4.6

SR-HP4.7

If overtime work is required as partof the job, such requirements areclear at the time of hiring
Employer has an annual leave(vacation) program that meets
applicable laws
If employees do not take annual
leave, employer can either allow
vacation time to accrue or can pay
wages for the equivalent amount oftime accrued under the regular pay
scale
Employer does not directly contract
any persons under the age of 14
Employment of authorized minors
older than 14 does not conflict with
their access to education
Management has an enforced policy
prohibiting discrimination on thebasis of gender, race, ethnicity, age
or religion as per ILO Convention
111
Employer prohibits the use offorced, bonded, indentured or
involuntary convict labor
Employer observes all legal
requirement for the work ofauthorized minors
Workers do not surrender theiridentity papers or other original
personal documents or pay deposits
as a condition of employment
All workers receive equal pay for
equal work, except in case ofrecognizing seniority of service
through higher pay

March 2011
Small
Wet

Wet

Dry

Both

NA

56%

93%

99%

NA

56%

82%

97%

NA

43%

59%

41%

NA

92%

97%

100%

NA

18%

41%

26%

NA

93%

98%

100%

NA

92%

98%

93%

NA

16%

38%

19%

NA

90%

99%

100%

NA

87%

90%

100%

Worker Conditions
Access to
Housing, Waterand Sanitary
Facilities

SR-WC1.1

Part-time/seasonal and full-timeworkers living onsite have habitable
dwellings

NA

60%

34%

68%

SR-WC1.2

Workers have ready access topotable water

NA

89%

74%

96%

SR-WC1.3

Worker housing has buffer zones
from productive area andagrochemical storage facilities
sufficient to prevent agrochemical
exposure

NA

59%

14%

49%

SR-WC1.4

Workers have ready access tosanitary facilities that do not impact
the local environment

NA

87%

75%

96%

SR-WC1.5

Garbage from employer-provided
housing and facilities is removed toeither a municipal waste dump or toa waste site located at least 200
meters from any water body

NA

76%

43%

90%

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Small
Wet

Wet

Dry

Both

NA

40%

31%

38%

NA

33%

30%

33%

NA

77%

90%

94%

SR-WC2.4

Where there is convenient access topublic education programs, employer
supports those schools either through inkind donation or financial support

NA

87%

92%

97%

SR-WC2.5

Employer supports training or workshops
on additional skills or trades

NA

86%

98%

99%

NA

96%

98%

100%

NA

97%

99%

100%

NA

81%

92%

94%

NA

94%

98%

100%

NA

50%

80%

94%

NA

90%

98%

96%

NA

79%

165%

99%

NA

85%

99%

97%

NA

78%

98%

99%

NA

73%

91%

96%

NA

79%

95%

91%

Indicator
Access to
Education
SR-WC2.1

SR-WC2.2

SR-WC2.3

Access to
MedicalCare

SR-WC3.1
SR-WC3.2

SR-WC3.3

SR-WC3.4
SR-WC3.5
SR-WC3.6
Access toTraining,
Health &SafetyMarch 2011

SR-WC4.1

SR-WC4.2

SR-WC4.3

SR-WC4.4

SR-WC4.5

Where there is insufficient access topublic education, schoolchildren (ofprimary school age) of workers who live
on-site have access to primaryeducational instruction, facilities andmaterials that meet national
requirements
Where there is insufficient access topublic education, schoolchildren (ofsecondary school age) of workers wholive on-site have access to primaryeducational instruction, facilities andmaterials that meet national
requirements
Employer provides direct incentives foreducation in the form of scholarships,
educational materials, boos,
transportation, food, etc.

Employer has transportation/care plan inplace in case of medical emergency
Employer provides sufficient onsite firstaid kits
Where there is convenient access topublic medical care, employer supports
these facilities either through in-kind
donation or financial support
Employer or manager maintains
communication with workers in case of
medical emergencies
Employer offsets the cost of healthservices for permanent workers
Employer offsets the cost of healthservices for all workers
Employer provides personal protective
equipment to all applicable employees at
no costThere is a written record documenting
instructor, materials, and attendance forall health and safety training
Training occurs at regular intervals, at aminimum yearly, free of charge, andduring regular working hours
Training covers, at a minimum: use ofprotective equipment, safe handling of
hazardous materials, operation ofequipment and personal safety/hygiene
There are regular safety meetings for all
relevant workers

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Indicator
Access toTraining,
Health &SafetySR-WC4.6

SR-WC4.7

SR-WC4.8

SR-WC4.9

SR-WC4.10

There is a written protocol for workers
and management to review safety
procedures and training materials in theevent of accidents, exposures tohazardous materials or spills
Workers utilize appropriate protective
equipment when applying agrochemicals
and operating machinery
All workers who handle/mix/apply
agrochemicals have access to eyebaths, hand washing and showers posthandling of agrochemicals
Authorized minors and pregnant womenare prohibited from handling or applying
agrochemicals OR operating heavymachinery
Workers do not enter areas where
pesticides were applied in the prior 48
hours without protective gear

March 2011
Small
Wet

Wet

Dry

Both

NA

81%

91%

97%

NA

69%

84%

93%

NA

19%

4%

25%

NA

52%

59%

59%

NA

12%

4%

13%

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Indicator

March 2011
Small Wet

Wet

Dry

Both

CP-WC1.1

The total volume of water used for
pulping, washing, and sorting forcoffee processing operations istracked and recorded

20%

98%

NA

NA

CP-WC1.2

If water is used for separating coffee
cherry before pulping, then a siphon ofless than 3 cubic meters is used

NA

62%

NA

NA

NA

88%

NA

NA

23%

76%

NA

NA

23%

87%

NA

NA

75%

99%

NA

NA

NA

77%

NA

NA

14%

30%

NA

NA

There is no evidence of contaminationfrom processing operations ofneighboring or local water bodies

NA

97%

NA

NA

Processing wastes are managed insuch a way as to not contaminate thelocal environment

77%

99%

NA

NA

Skin, pulp, mucilage and unacceptablecherries are composted or processedby worms

NA

99%

NA

NA

CP-WM1.3

Organic processing byproducts areused as soil amendments by the farmor, in the case of a standalone
processor, distributed to local farmers

76%

98%

NA

NA

CP-WM1.4

Solids are recovered fromsedimentation pools, composted andused by coffee farms

NA

89%

NA

NA

CP-WM2.1

Parchment hulls from dry milling arerecovered for use in mechanicalcoffee dryers, generating energy orother beneficial uses

NA

NA

88%

NA

Coffee Processing -- Environmental Leadership
Water Conservation
Minimizing
Water
Consumption

CP-WC1.3
CP-WC1.4
CP-WC1.5
Reducing
Wastewater
Impacts

CP-WC2.1

CP-WC2.2

CP-WC2.3

CP-WC2.4
Waste Management
Waste
Management
CP-WM1.1
Operations/Beneficial
Reuse
CP-WM1.2

Waste
Management

Processing facility recycles water fortransporting coffee prior to pulping andfor pulping
The amount of water used on a perunit basis shows a decrease over time
The ratio between coffee cherry andwater is no more than 1:1
Wastewater from pulping and washingis managed in a way that does not
impact the local or surrounding
environment
If wastewater from pulping andwashing is released into a leach fieldor lagoon or sprayed onto fields, theboundary of the fields or lagoon is aminimum of 40m from allwatercourses
If wastewater is discharged to awatercourse or a sewer, the following
wastewater tests are conducted at allexit points (and meet establishedenvironmental regulatory norms) on amonthly basis during operations:biological oxygen demand, chemical
oxygen demand, pH

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Indicator
Energy Use
EnergyConservation/Impacts

March 2011
Small Wet

Wet

Dry

Both

CP-EC1.1

The quantity of energy used on-site forcoffee processing operations shall bereported

NA

93%

92%

NA

CP-EC1.2

Parchment coffee is patio dried or driedin other energy efficient ways

83%

82%

66%

NA

CP-EC1.3

The quantity of wood/other fuel used fordrying coffee is recorded

NA

31%

22%

NA

NA

25%

16%

NA

NA

68%

79%

NA

NA

89%

87%

NA

NA

61%

78%

NA

CP-EC1.4

CP-EC1.5

CP-EC1.6

CP-EC1.7

Wood used for drying coffee comesfrom pruning of shade trees,
responsibly managed forests, or otherminimal impact harvests
The total amount of energy used per
pound of green coffee show a decreaseover time
Operation shows commitment to the
production of renewable energy
through the production or purchase ofenergy produced using renewable andhigh-efficiency technologies
All on-site energy production operations
are demonstrably operated inaccordance with local permitting
requirements or they meet the
maximum practicable emissions
standards

143